Journal of Information and Intelligence最新文献

{"title":"Channel computation based on multi-scale attention residual network","authors":"Wengang Li,&nbsp;Deli Zhou,&nbsp;Qiong Ye","doi":"10.1016/j.jiixd.2025.03.001","DOIUrl":"10.1016/j.jiixd.2025.03.001","url":null,"abstract":"<div><div>Orthogonal time-frequency space (OTFS) modulation can effectively counter ICI in high-speed mobile scenarios, fully enhance the spectral efficiency of communication systems in high Doppler expansion scenarios, and improve the quality of communication systems. Channel estimation performance serves as a critical evaluation parameter within the OTFS modulation system. In this paper, we propose a multi-scale attention residual neural structure for improved channel estimation of OTFS waveforms in different satellite-ground scenario. Firstly, a multi-scale channel feature extraction module is designed, which applies multi-dimensional feature extraction to the channel matrix, thereby bolstering the capability to capture features at diverse scales. Subsequently, a self-attention mechanism is incorporated to concentrate on subtle yet significant features. The extracted features are then integrated and exploited through a residual convolutional architecture to derive an estimation of the channel matrix. Simulations are conducted using the satellite-ground mobile channel model outlined in 3GPP TR 38.811, with the NTN-TDL-C and NTN-TDL-B channel models representing line of sight (LoS) and non-line of sight (NLoS) conditions, respectively. Results demonstrate that the attention-based approach presented surpasses alternative neural network methodologies in terms of mean squared error (MSE), bit error rate (BER), and complexity, and meets the demands of OTFS channel estimation in satellite-ground scenario.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 275-287"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Positionally restricted masked knowledge graph completion via multi-head mutual attention","authors":"Qiang Yu ,&nbsp;Liang Bao ,&nbsp;Peng Nie ,&nbsp;Lei Zuo","doi":"10.1016/j.jiixd.2025.02.006","DOIUrl":"10.1016/j.jiixd.2025.02.006","url":null,"abstract":"<div><div>Knowledge graph completion aims to enhance the completeness of knowledge graphs by predicting missing links. Link prediction is a common approach for this task, but existing methods, particularly those based on similarity computation, are often computationally expensive, especially for large models. To address this, we propose a novel method, positionally restricted masked knowledge graph completion (PR-MKGC), which reduces inference time by leveraging masked prediction and relying solely on structural information from the knowledge graph, without using textual data. We introduce a multi-head mutual attention mechanism that aggregates neighbor information more effectively, improving the model's ability to predict missing links. Experimental results demonstrate that PR-MKGC outperforms existing models in terms of both predictive performance and inference time on the FB15K-237 dataset.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 210-222"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation","authors":"Yunfei Zhao ,&nbsp;Jie Guo ,&nbsp;Longyu Wen ,&nbsp;Letian Wang","doi":"10.1016/j.jiixd.2025.04.001","DOIUrl":"10.1016/j.jiixd.2025.04.001","url":null,"abstract":"<div><div>In recent years, there has been a burgeoning interest in multimodal recommender systems, which integrate various data types to achieve more personalized recommendations. Despite this, the effective incorporation of user preferences for multimodal data and the exploration of inherent semantic relationships between modalities still need to be explored. Prior research typically utilizes multimodal data to construct item graphs, often overlooking the nuanced details within the data. As a result, these studies fail to thoroughly examine the semantic relationships between items and user behavioral patterns. Our proposed approach, METRIC, addresses this gap by delving deeper into multimodal information. METRIC consists of two primary modules: the multiple preference modelling (MPM) module and the item semantic enhancement (ISE) module. The ISE module performs relational mining across multiple attributes, leveraging the semantic structural relationships inherent in items. In contrast, the MPM module enables users to articulate their preferences across different modalities and facilitates adaptive fusion through an attention mechanism. This approach not only improves precision in capturing user preferences and interests but also minimizes interference from varying modalities. Our extensive experiments on three benchmark datasets substantiate METRIC's superiority and the efficacy of its core components.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 242-256"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethink delay Doppler channels and time-frequency coding","authors":"Xiang-Gen Xia","doi":"10.1016/j.jiixd.2025.02.002","DOIUrl":"10.1016/j.jiixd.2025.02.002","url":null,"abstract":"<div><div>In this paper, we rethink delay Doppler channels (also called doubly selective channels). We prove that no modulation schemes (including the current active VOFDM/OTFS) can compensate a non-trivial Doppler spread well. We then discuss some of the existing methods to deal with time-varying channels, in particular time-frequency (TF) coding in an OFDM system. TF coding is equivalent to space-time coding in the math part. We also summarize state of the art on space-time coding that was an active research topic over a decade ago.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 189-193"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient machine learning-enhanced DTCO framework for low-power and high-performance circuit design","authors":"Mingyang Liu ,&nbsp;Zhengguang Tang ,&nbsp;Hailong You ,&nbsp;Cong Li ,&nbsp;Guangxin Guo ,&nbsp;Zeyuan Wang ,&nbsp;Linying Zhang ,&nbsp;Xingming Liu ,&nbsp;Yu Wang ,&nbsp;Yong Dai ,&nbsp;Geng Bai ,&nbsp;Xiaoling Lin","doi":"10.1016/j.jiixd.2025.02.001","DOIUrl":"10.1016/j.jiixd.2025.02.001","url":null,"abstract":"<div><div>The standard design technology co-optimization (DTCO) involves frequent interactions between circuit design and process manufacturing, which requires several months. To assist designers in establishing a bridge between device parameters and circuit metrics efficiently, and provide guidance for parameter optimization in the early stages of circuit design. In this paper, we propose an efficient machine learning (ML)-enhanced DTCO framework. This framework achieves the co-optimization of device parameters and circuit metrics. We select the gate metal work function (WF) as the parameter to validate the effectiveness of our framework. And the ridge regression approach is used to bypass TCAD simulation, compact model extraction and cell library characterization. We reduces time consumption by at least 92% compared to traditional DTCO framework, while ensuring that errors of delay, internal power consumption and leakage power below 4 ps, 0.035 ​mJ, and 0.4 μW, respectively. By adjusting the WF, we achieved a better balance between circuit delay and power consumption. This work contributes to designers exploring a broader design space and achieving a efficient DTCO flow.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 194-209"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal emotion recognition method in complex dynamic scenes","authors":"Long Liu ,&nbsp;Qingquan Luo ,&nbsp;Wenbo Zhang ,&nbsp;Mengxuan Zhang ,&nbsp;Bowen Zhai","doi":"10.1016/j.jiixd.2025.02.004","DOIUrl":"10.1016/j.jiixd.2025.02.004","url":null,"abstract":"<div><div>Multimodal emotion recognition technology leverages the power of deep learning to address advanced visual and emotional tasks. While generic deep networks can handle simple emotion recognition tasks, their generalization capability in complex and noisy environments, such as multi-scene outdoor settings, remains limited. To overcome these challenges, this paper proposes a novel multimodal emotion recognition framework. First, we develop a robust network architecture based on the T5-small model, designed for dynamic-static fusion in complex scenarios, effectively mitigating the impact of noise. Second, we introduce a dynamic-static cross fusion network (D-SCFN) to enhance the integration and extraction of dynamic and static information, embedding it seamlessly within the T5 framework. Finally, we design and evaluate three distinct multi-task analysis frameworks to explore dependencies among tasks. The experimental results demonstrate that our model significantly outperforms other existing models, showcasing exceptional stability and remarkable adaptability to complex and dynamic scenarios.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 257-274"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Resource allocation for coexistence of eMBB and bursty URLLC based on queueing with preemption","authors":"Wei Guo ,&nbsp;Kai Liang ,&nbsp;Yuewen Song ,&nbsp;Xiaoli Chu ,&nbsp;Gan Zheng ,&nbsp;Kai-Kit Wong","doi":"10.1016/j.jiixd.2025.03.003","DOIUrl":"10.1016/j.jiixd.2025.03.003","url":null,"abstract":"<div><div>Enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) are two critical services in 5G mobile networks. While there has been extensive research on their coexistence, few studies have considered the impact of bursty URLLC on their coexistence performance. In this paper, we propose a method to allocate computing and radio resources for coexisting eMBB and bursty URLLC services by preempting both computing queues in the base station (BS) and time-frequency resources at the air interface. Specifically, we first divide the computing resources at the BS into a shared part for both URLLC and eMBB users and an exclusive part only for eMBB users, and propose a queuing mechanism with preemptive-resume priority for accessing the shared computing resources. Furthermore, we propose a preemptive puncturing method and a threshold-based queuing mechanism in the air interface to enable the multiplexing of eMBB and URLLC on shared time-frequency resources. We analytically derive the average queuing delay, average computation delay, and average transmission delay of eMBB and URLLC packets. Based on this analysis, we formulate a mixed-integer nonlinear programming problem to minimize the average delay of URLLC packets while satisfying the average delay and throughput requirements of eMBB by jointly optimizing the eMBB subcarrier allocation, the URLLC subcarrier scheduling and the computing resource allocation. We decompose this problem into three sub-problems and solve them alternately using a block coordinate descent algorithm. Numerical results show that our proposed method reduces the outage probability and average delay of URLLC compared to the existing works.</div></div>","PeriodicalId":100790,"journal":{"name":"Journal of Information and Intelligence","volume":"3 3","pages":"Pages 223-241"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144490377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual defense: Combining preemptive exclusion of members and knowledge distillation to mitigate membership inference attacks","authors":"Jun Niu ,&nbsp;Peng Liu ,&nbsp;Chunhui Huang ,&nbsp;Yangming Zhang ,&nbsp;Moxuan Zeng ,&nbsp;Kuo Shen ,&nbsp;Yangzhong Wang ,&nbsp;Suyu An ,&nbsp;Yulong Shen ,&nbsp;Xiaohong Jiang ,&nbsp;Jianfeng Ma ,&nbsp;He Wang ,&nbsp;Gaofei Wu ,&nbsp;Anmin Fu ,&nbsp;Chunjie Cao ,&nbsp;Xiaoyan Zhu ,&nbsp;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}

{"title":"Boosting brain-computer interface performance through cognitive training: A brain-centric approach","authors":"Ziyuan Zhang ,&nbsp;Ziyu Wang ,&nbsp;Kaitai Guo ,&nbsp;Yang Zheng ,&nbsp;Minghao Dong ,&nbsp;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}

{"title":"Hand-aware graph convolution network for skeleton-based sign language recognition","authors":"Juan Song ,&nbsp;Huixuechun Wang ,&nbsp;Jianan Li ,&nbsp;Jian Zheng ,&nbsp;Zhifu Zhao ,&nbsp;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}