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Issue 8, 2026

Editors-in-Chief

Yunhe Pan, Aiguo Fei

Advisor  Xicheng Lu

ISSN  3069-8928

CN  33-1439/TP

IF (2025 JCR) 3.2

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Volume 27  Issue 8，2026 2026年第27卷第8 Issue

  • Regular Papers

    Biying WANG, Baosheng WANG, Shuang ZHAO, Jinshu SU, Shuhui CHEN, Minxin WANG, Zhengpeng LI, Ziling WEI

    Vol. 27, Issue 8, Pages: 1-18(2026) DOI: 10.1631/ENG.ITEE.2026.0095
    Abstract：Network traffic research relies on large-scale, high-quality traffic data. However, obtaining such data remains difficult because of privacy constraints, collection costs, class imbalance, and continuous updates. These challenges have increased researchers’ interest in traffic generation. Although many generation methods have been proposed, existing studies and surveys often overlook two key questions: what form of traffic is generated and what practical objectives it can support. Based on 113 candidate records published from 2019 to 2026, this survey provides a detailed analysis of 39 representative network traffic generation studies through the lenses of representation levels and objective consistency. We organize existing methods into four representation levels and analyze how generated data relate to usage scenarios. We find that many methods preserve information that is useful for downstream tasks such as classification and intrusion detection, but task usefulness does not guarantee replayability or usability in real network environments. High-level representations are easier to model, yet they often discard protocol semantics, packet dependencies, and communication logic. We therefore distinguish task consistency from protocol consistency and show that the latter remains underexplored. We further summarize evaluation practices, discuss level-specific metrics, and highlight future directions including controllable generation, protocol-aware state-consistent synthesis, and engineering-oriented evaluation.  
    Keywords：Network traffic generation;Traffic representation levels;Task consistency;Protocol consistency   
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  • Regular Papers

    Yanzheng WANG, Yujun WANG, Fengshuo DAI, Xin YANG, Xiaoheng JIANG, Pei LV, Shizhe HU, Mingliang XU

    Vol. 27, Issue 8, Pages: 1-13(2026) DOI: 10.1631/ENG.ITEE.2025.0185
    Abstract：Contrastive learning has received extensive attention because it can effectively strengthen inter-modal alignment and information complementarity in multi-modal clustering. However, there are two main problems with existing contrastive multi-modal clustering methods: (1) Current methods usually perform contrastive learning at only one or two stages and have imprecise selection strategies for negative sample pairs. (2) Most methods focus only on contrastive learning and ignore the retention of consistency information. To address the above challenges, we propose a framework of multi-stage contrastive multi-modal clustering with consistency retained. First, we design a systematic contrastive learning framework, which includes early–middle–late contrastive learning between single features, fused features, and clusters, respectively, which improves the semantic integrity and information fidelity of the fused representation. Second, we propose a new negative sample pair selection strategy to discriminately select the correct negative sample pair for each sample. Finally, we add a new loss term to late-stage contrastive learning, which considers the consistency of clustering results of the same sample under different modalities for the first time. Therefore, compared to the baseline model, this module has improved the accuracy by an average of 7.4 percentage points (PPs). In addition, we design a consistency retained module to ensure that the model can learn the information of each modality without being disturbed by high/low quality modalities, which improves the accuracy by an average of 2.2 PPs. Experiments conducted on multiple multi-modal datasets demonstrate that the accuracy of our method is 4.1 PPs higher on average compared to the second-best multi-modal clustering method, proving the excellent potential of our method.  
    Keywords：Multi-modal clustering;Contrastive learning;Consistency retention;Negative sample selection   
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  • Regular Papers

    Yunxiang GE, Bing XIA, Yong DING, Wenbo LIU

    Vol. 27, Issue 8, Pages: 1-11(2026) DOI: 10.1631/ENG.ITEE.2026.0017
    Abstract：In practical reverse engineering, stripped and optimized binaries lack high-level semantics, hindering automated function understanding. This paper adopts binary function name prediction as a benchmark to evaluate open-source large language models (LLMs) for function-level semantic inference under realistic conditions. We systematically examine key factors affecting performance, including the pretraining domain, model scale, architecture and optimization settings, prompting, and contextual information. Experiments on a large-scale multi-project binary dataset reveal clear limitations in recovering function semantics from stripped binaries. Code-oriented LLMs consistently outperform general-purpose models, while increasing the model size alone does not yield monotonic gains. We further show that realistically recoverable contextual signals, especially structurally recoverable cross-function contexts, substantially mitigate semantic sparsity and improve prediction quality. These results delineate current capability boundaries of LLM-based binary semantic understanding and suggest future directions in contextual modeling and domain-adaptive techniques for practical reverse engineering.  
    Keywords：Binary function name prediction;Large language models (LLMs);Reverse engineering (RE);Binary code analysis   
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  • Regular Papers

    Kuen Wai MA, Jiajun ZHU, Hai Ming WONG

    Vol. 27, Issue 8, Pages: 1-14(2026) DOI: 10.1631/ENG.ITEE.2026.0013
    Abstract：Automated dental age estimation is essential for clinical dentistry. However, traditional methods like Demirjian’s tooth development stage (TDS) require time-consuming manual annotation by trained experts. Moreover, population differences in dental development patterns can significantly affect the accuracy of age estimation, highlighting the need for an ethnicity-specific reference data set (RDS). This study aims to use deep neural networks (DNNs) for automated estimation of Demirjian’s TDS of molars on digital panoramic radiographs, integrating the most complete southern Chinese RDS for ethnicity-appropriate dental age assessment. Panoramic radiographs from individuals aged 2 to 25 years are annotated for molar TDS and used to train eight DNN architectures, including AlexNet, DenseNet-201, and ResNet-50. DenseNet-201 achieves the highest accuracy of 93% in classifying molar TDS. Most misclassifications involve adjacent stages. The integrated mean dental age (IMDA) is obtained by mapping the predicted molar TDS using the southern Chinese RDS. The mean difference and correlation coefficient between the estimated IMDA from the best-performing DNN (AlexNet) and chronological age are -0.063 years (-3.3 weeks) and r=0.898 (p<0.001), respectively. These findings demonstrate that combining DNN for TDS estimation with ethnic-specific RDS enables accurate and reliable dental age assessment.  
    Keywords：Deep neural networks (DNNs);Dental age estimation;Panoramic radiographs;Southern Chinese;Tooth development   
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  • Regular Papers

    Liya HU, Bo BAI, Juntao YANG, Mingxuan SONG, Youwei LI, Dandan LIU, Zhenhai LI, Yirou LIU, Guowei LI

    Vol. 27, Issue 8, Pages: 1-12(2026) DOI: 10.1631/ENG.ITEE.2026.0108
    Abstract：Accurate and efficient estimation of aboveground biomass (AGB) is important for peanut phenotyping and field management. This study evaluates spectral and textural features derived from unmanned aerial vehicle (UAV) multispectral imagery for nondestructive and high-throughput estimation of peanut AGB. Vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (TFs) are derived from the green, red, red-edge, and near-infrared bands and assessed via six regression models. The results reveal that near-infrared TFs exhibit the highest sensitivity to AGB. Among the GLCM configurations, a setting of a 7×7 window size, a 45° orientation, and 16 gray levels produces the most stable texture representation. Although combining all VIs and TFs slightly improves prediction accuracy, the relatively large differences between the coefficient of determination ( R2) and adjusted R2 in some models suggest that the full feature set contains redundant predictors and has limited model parsimony. An extreme gradient boosting (XGBoost)–Shapley additive explanation (SHAP) feature selection strategy is therefore used to identify five key variables: difference vegetation index (DVI), variance, mean, energy, and modified soil-adjusted vegetation index (MSAVI). This compact feature set substantially reduces predictor dimensionality, limits the differences between R2 and adjusted R2 to below 0.020, and achieves consistent predictive accuracy, with R2 above 0.840 and the root mean square error ( RMSE) below 0.055 kg/m2. The proposed approach provides a practical tool for high-throughput peanut biomass monitoring, phenotyping, and production management.  
    Keywords：High-throughput monitoring;Unmanned aerial vehicle (UAV) remote sensing;Peanut;Aboveground biomass;Texture features (TFs)   
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  • Regular Papers

    Qi WANG, Xiaoming CHEN, Qiao QI, Zhaolin WANG, Yuanwei LIU

    Vol. 27, Issue 8, Pages: 1-16(2026) DOI: 10.1631/ENG.ITEE.2026.0090
    Abstract：This paper proposes a novel continuous aperture array (CAPA)-assisted integrated communication and navigation (ICAN) framework for low Earth orbit (LEO) satellite constellations. Within this framework, an electromagnetic-based collaborative transmission model is developed, in which multiple satellites equipped with CAPAs simultaneously radiate downlink data streams and navigation reference signals over a shared spectrum. Building upon this, the achievable communication rate and the navigation Cramér–Rao bound (CRB) are derived, which explicitly characterize the intrinsic coupling between the dual-function beamformers and system performance. To improve the positioning accuracy with a communication quality of service guarantee, a joint beamforming optimization problem is formulated to minimize the average CRB subject to transmit power budgets and minimum rate constraints. To tackle the inherent infinite dimensionality of the CAPA beamformer design, an ICAN channel subspace is introduced to equivalently transform the formulation into a tractable finite-dimensional problem, which is then efficiently solved via an iterative convex optimization algorithm. Finally, numerical results demonstrate that the proposed CAPA-assisted beamforming design algorithm significantly outperforms conventional discrete phased array architectures and other benchmark schemes, yielding notable improvements in ICAN performance.  
    Keywords：Sixth-generation (6G);Electromagnetic information theory;Low Earth orbit (LEO) satellite constellation;Continuous aperture array;Integrated communication and navigation (ICAN)   
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  1. 0 1 Hybrid-augmented intelligence: collaboration and cognition

    Nan-ning ZHENG, Zi-yi LIU, Peng-ju REN, Yong-qiang MA, Shi-tao CHEN, Si-yu YU, Jian-ru XUE, Ba-dong CHEN, Fei-yue WANG 959 | 1864 | 52

  2. 0 2 Visual interpretability for deep learning: a survey

    Quan-shi ZHANG, Song-chun ZHU 268 | 799 | 50

  3. 0 3 Crowd intelligence in AI 2.0 era

    LI Wei, Wen-jun WU, Huai-min WANG, Xue-qi CHENG, Hua-jun CHEN, Zhi-hua ZHOU, Rong DING 487 | 517 | 41

  4. 0 4 Applications of artificial intelligence in intelligent manufacturing: a review*

    Bo-hu LI, Bao-cun HOU, Wen-tao YU, Xiao-bing LU, Chun-wei YANG 772 | 803 | 40

  5. 0 5 Challenges and opportunities: from big data to knowledge in AI 2.0

    Yue-ting ZHUANG, Fei WU, Chun CHEN, Yun-he PAN 380 | 1280 | 28

  6. 0 6 Current trends in the development of intelligent unmanned autonomous systems

    Tao ZHANG, Qing LI, Chang-shui ZHANG, Hua-wei LIANG, LI Ping, Tian-miao WANG, Shuo LI, Yun-long ZHU, Cheng WU 480 | 592 | 27

  7. 0 7 Recent advances in efficient computation of deep convolutional neural networks

    Jian CHENG, Pei-song WANG, Gang LI, Qing-hao HU, Han-qing LU 522 | 1196 | 25

  8. 0 8 Terahertz time-domain spectroscopy and micro-cavity components for probing samples: a review

    Lin CHEN, Deng-gao LIAO, Xu-guang GUO, Jia-yu ZHAO, Yi-ming ZHU, Song-lin ZHUANG 356 | 914 | 24

  9. 0 9 Cross-media analysis and reasoning: advances and directions

    Yu-xin PENG, Wen-wu ZHU, Yao ZHAO, Chang-sheng XU, Qing-ming HUANG, Han-qing LU, Qing-hua ZHENG, Tie-jun HUANG, Wen GAO 582 | 1506 | 24

  10. 10 Deep reinforcement learning: a survey

    Hao-nan WANG, Ning LIU, Yi-yun ZHANG, Da-wei FENG, Feng HUANG, Dong-sheng LI, Yi-ming ZHANG 937 | 1167 | 24

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