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7th International Conference on Attacks and Defenses for Internet-of-Things, ADIoT 2024

作者： Li, Jianying Zhou, Shuo Liu, Yining School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China School of Data Science and Artificial Intelligence Wenzhou University of Technology Zhejiang Wenzhou325035 China

ISBN: (纸本)9783031855924

data aggregation masks individual entries by summing values, thus preventing the disclosure of private information. Multi-subset data aggregation divides subsets according to the range of data and collects the aggregated values of each subset. This provides the data control center with granular data. If data aggregation loses its verifiability, it becomes impossible to confirm the authenticity of messages or detect any tampering. This vulnerability could lead to an increased incidence of data tampering attacks across the system. Furthermore, a centralized control center is prone to disruptions due to natural disasters or malicious attacks, creating a single point of failure that may cause service interruptions and data loss. This could result in service interruptions and data loss. Therefore, we propose a robust and verifiable multi-subset data aggregation scheme. In this scheme, ciphertext information is transmitted to multiple control centers in the form of shards, and the aggregation results are recovered through cooperation between control centers. The security analysis demonstrates the protocol could resist collusion attacks and single points of failure. Experimental analysis indicates that the scheme is feasible. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： data aggregation

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UAV-YOLOv5: A Swin-Transformer-Enabled Small Object Detection Model for Long-Range UAV Images

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Annals of data science 2024年第4期11卷 1109-1138页

作者： Li, Jun Xie, Chong Wu, Sizheng Ren, Yawei Artificial Intelligence Security Innovation Team Beijing Information Science and Technology University Beijing100192 China School of Information Management Beijing Information Science and Technology University Beijing100192 China

This paper tackle the challenges associated with low recognition accuracy and the detection of occlusions when identifying long-range and diminutive targets (such as UAVs). We introduce a sophisticated detection framework named UAV-YOLOv5, which amalgamates the strengths of Swin Transformer V2 and YOLOv5. Firstly, we introduce Focal-EIOU, a refinement of the K-means algorithm tailored to generate anchor boxes better suited for the current dataset, thereby improving detection performance. Second, the convolutional and pooling layers in the network with step size greater than 1 are replaced to prevent information loss during feature extraction. Then, the Swin Transformer V2 module is introduced in the Neck to improve the accuracy of the model, and the BiFormer module is introduced to improve the ability of the model to acquire global and local feature information at the same time. In addition, BiFPN is introduced to replace the original FPN structure so that the network can acquire richer semantic information and fuse features across scales more effectively. Lastly, a small target detection head is appended to the existing architecture, augmenting the model’s proficiency in detecting smaller targets with heightened precision. Furthermore, various experiments are conducted on the comprehensive dataset to verify the effectiveness of UAV-YOLOv5, achieving an average accuracy of 87%. Compared with YOLOv5, the mAP of UAV-YOLOv5 is improved by 8.5%, which verifies that it has high-precision long-range small-target UAV optoelectronic detection capability. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Unmanned aerial vehicles (UAV)

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Internet of Medical Things: A Revolution in Healthcare for Enhancing Cardiac Monitoring and Patient-Care Through Wearables 3

Internet of Medical Things: A Revolution in Healthcare for E...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Chhoton, Amit Chakraborty Sharma, Arvind K. Yogananda School of Artificial Intelligence Computers and Data Science Shoolini University Himachal Pradesh India Daffodil International University Department of Computer Science Dhaka Bangladesh

ISBN: (纸本)9798331518592

Wearables play an important role in parallel with IoT towards the era of healthcare and it has to be designed effectively providing workable solutions for all areas of other medical scopes. The fundamental objective of this technology kinship is to make smart healthcare services possible towards societal necessities. This paper conducts a systematic review on current medical applications with Wearable Internet of Things. The use of well-defined keywords for strict paper selection is a criterion in further comprehensive analysis with published scientific work. This paper synthesizes the current state of knowledge and Wearable Internet of Things applications to be used in early prognosis, diagnosis, treatments and patient monitoring more specifically in cardiovascular diseases. Of interest is the characteristics in terms of design and structure, as well as efficiency in reducing the current disparities in healthcare. Additionally, this paper presents potential lines of research and obstacles to be overcome before increasing implementation of Wearable Internet of Things devices in smart-healthcare. © 2024 IEEE.

关键词： Diagnosis

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An improved method for small object detection in aerial images based on YOLOv8

An improved method for small object detection in aerial imag...

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2024 International Conference on computer Graphics, artificial intelligence, and data Processing, ICCAID 2024

作者： Ma, Liting Zhao, Junzhe Hsiang, Han-Cheng School of Data and Computer Science Xiamen Institute of Technology Xiamen China

ISBN: (纸本)9781510689275

In recent years, detecting objects in aerial images has emerged as a crucial area of study within the domain of computer vision. However, due to obstacles like the limited size of objects, dense distributions, and class imbalance in these images, traditional object detection methods often face issues like missed detections and low performance. In response to these challenges, this study introduces an enhanced YOLOv8s model designed to optimize small object detection. First, to enhance feature extraction capabilities for objects of different scales, the C2f structure within the backbone of the baseline YOLOv8s model is modified by PKI(Poly Kernel Inception)Block from PKINet, thereby strengthening its recognition capabilities across multi-scale targets. Second, to further elevate the efficiency of the neck network for detecting small targets, the MCAttention module is incorporated as part of the C2f design in the neck, leading to the enhanced C2fMCAttn module, which augments the neck network's effectiveness in modeling features from small object areas. Moreover, a 160×160 detection head is added to the detection layer, tailored to enhance the detection of small objects. The experiments were conducted on the VisDrone2019 drone aerial imagery dataset. The results indicate that the improved model outperformed the baseline YOLOv8s model. It achieved a 5.3% increase in precision, a 5% increase in recall and a 4.4% increase in mAP@50. Furthermore, the proposed model outperforms other mainstream object detection methods, exhibiting superior detection effectiveness. © 2025 SPIE.

关键词： Aerial photography

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DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework 39

DCHM: Dynamic Collaboration of Heterogeneous Models Through ...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance Beijing Technology and Business University Beijing China Department of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)157735897X

Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data isolation. In response, distributed AI systems like federated learning have emerged to facilitate dynamic collaboration among models;however, they still depend on central coordinators, which pose risks to system security and efficiency. Moreover, traditional federated learning primarily supports homogeneous models and lacks effective strategies for the interaction of heterogeneous models. To address these limitations, we propose a novel method called Dynamic Collaboration of Heterogeneous Models (DCHM), based on Isomerism Learning, which leverages a consortium blockchain network to enhance model credibility and facilitate coordination among heterogeneous models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share them for standardized processing. After several iterative cycles, these nodes perform secondary integration of local results to produce global outcomes. Experimental results demonstrate that DCHM effectively analyzes the temporal variability of body shape changes with high efficiency. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Federated learning

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Lingual-Fusion Adapter-Based Transfer Learning for Low-Resource Code Vulnerability Detection

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1008-1023页

作者： Long, Xinyue Guo, Shikai Chai, Yu Li, Hui Jan, Sumaira Ameer Ma, Qian Ning, Qiao Dalian Maritime University School of Informafion Science and Technology Dalian China Key Laboratory of Artificial Intelligence of Dalian Dalian China Shanghai Jiao Tong University Shanghai China Dalian Maritime University School of Information and Science Technology Dalian China

Software vulnerabilities pose significant security threats to modern systems, particularly those involving complex execution sequences and intricate call relationships across multiple execution *** instance, in a scenario where a software system integrates legacy code in a low-resource programming language like PHP, detecting vulnerabilities becomes challenging due to data scarcity and the complexity of temporal relationships among code fragments. This scarcity hampers the ability to capture critical temporal features essential for identifying vulnerabilities spanning multiple execution ***, existing approaches face major limitations, including neglecting temporal information in code fragments and lacking sufficient data to enable effective generalization for models in low-resource *** address these challenges, we introduce, a novel approach that enhances vulnerability detection in low-resource languages by extracting complex temporal features from code fragments and employing parameter-efficient transfer learning to leverage shared knowledge from resource-rich languages. comprises two key components: 1) Code Vulnerability Detection Component: This component models temporal dependencies by leveraging execution paths extracted from Abstract Syntax Trees (ASTs), capturing both short-term variations and long-term dependencies among code fragments. This enables comprehensive extraction of complex temporal features, significantly enhancing the accuracy of vulnerability detection. 2) Cross-Lingual Transfer Component: This component learns generalizable features from resource-rich languages and efficiently transfers them to low-resource languages. By updating a small number of downstream parameters, it enhances model generalization and achieves precise vulnerability detection. We evaluated using a diverse set of programming languages from publicly available GitHub repositories, employing C as the resource-rich source language and Java, Pytho

关键词： C (programming language)

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KSGTN-DDI: Key Substructure-aware Graph Transformer Network for Drug-drug Interaction Prediction

KSGTN-DDI: Key Substructure-aware Graph Transformer Network ...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zhang, Peiliang Liu, Yuanjie Shen, Zhishu Wuhan University of Technology School of Computer Science and Artificial Intelligence Wuhan430070 China

ISBN: (纸本)9798350337488

Drug substructure plays a crucial role in predicting drug-drug interaction (DDI) with combination drugs for disease therapies. In order to exploit the effect of drug substructure on DDI prediction, we propose a Key Substructure-aware Graph Transformer Network for Drug-drug Interaction Prediction (KSGTN-DDI). First, the substructure-adaptive graph Transformer module adaptively explicit encoding of drug structures information. Then, the key substructure-aware module calculates the importance of different substructures in DDI prediction. Finally, the calculated important substructure aggregation features are used to reconstruct the drug-drug interactions. Relevant experiments indicate that the performance of KSGTN-DDI outperforms other DDI prediction models. © 2023 IEEE.

关键词： DDI Drug Structures Graph Neural Networks Self-attention Transformer

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Badminton Action Recognition based on Improved I3D Convolutional Neural Network 3

Badminton Action Recognition based on Improved I3D Convoluti...

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3rd International Conference on artificial intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Zheng, Huangnan Shen, Huichang Zhang, Yisong Wang, Haotian School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to solve the issue of inaccurate recognition of time series of badminton action by 2D networks and the high cost of using skeleton data for badminton action recognition, this article aims to explore the use of RVM (Robust Video Matting) to extract the silhouette of individuals in videos, combined with an improved I3D network (referred to as the SI3D network) for action classification in badminton matches. We collected a dataset of badminton match videos featuring multiple players and labeled them according to different action categories. By using the SI3D network for training, this article achieved good performance on the test set, with an accuracy of 91.5%. This study demonstrates the effectiveness of the I3D network in badminton action classification and provides a new research direction for action recognition in sports. © 2023 SPIE.

关键词： Convolutional neural networks

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Visible and near-infrared image fusion based on information complementarity

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CAAI Transactions on intelligence technology 2025年第1期10卷 193-206页

作者： Zhuo Li Shiliang Pu Mengqi Ji Feng Zeng Bo Li The School of Computer Science and Engineering Beihang UniversityBeijingChina Hangzhou Hikvision Digital Technology Co. Ltd.HangzhouChina The School of Artificial Intelligence Beihang UniversityBeijingChina Hangzhou International Innovation Institute Beihang UniversityHangzhouChina

Images with complementary spectral information can be recorded using image sensors that can identify visible and near-infrared *** fusion of visible and nearinfrared(NIR)aims to enhance the quality of images acquired by video monitoring systems for the ease of user observation and data ***,current fusion algorithms produce artefacts and colour distortion since they cannot make use of spectrum properties and are lacking in information ***,an information complementarity fusion(ICF)model is designed based on physical *** order to separate high-frequency noise from important information in distinct frequency layers,the authors first extracted texture-scale and edge-scale layers using a two-scale ***,the difference map between visible and near-infrared was filtered using the extended-DoG filter to produce the initial visible-NIR complementary weight ***,to generate a guide map,the near-infrared image with night adjustment was processed as *** final complementarity weight map was subsequently derived via an arctanI function mapping using the guide map and the initial weight ***,fusion images were generated with the complementarity weight *** experimental results demonstrate that the proposed approach outperforms the state-of-the-art in both avoiding artificial colours as well as effectively utilising information complementarity.

关键词： color distortion image fusion information complementarity low light near-infrared

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Inconsistency Detecting and Resolving for Security Policy and IPv6 Firewall Policy 7

Inconsistency Detecting and Resolving for Security Policy an...

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7th International Conference on computer science and artificial intelligence, CSAI 2023

作者： Yin, Yi Tateiwa, Yuichiro Qian, Xiaojun Gao, Qian Shen, Lingling Zhang, Guoqiang School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University Nanjing China Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan School of Information and Technology School of Computer Science and Technology School of Artificial Intelligence Hainan Normal University Hainan China

ISBN: (纸本)9798400708688

Firewall is the first defense line for network security. Packet filtering is a basic function in firewall, it filter network packets according to a series of rules, which is called firewall policy. The design of firewall policy should be observe the regulations of security policy, which is a generic guideline that lists the basic requirements for network access permissions. However, even for IPv4 firewall policy, it is extremely hard to keep the consistency between firewall policy and security policy. Some inconsistency decision methods of security policy and IPv4 firewall policy were proposed. However, the address space of IPv6 address is a very large, the existing inconsistency decision methods could not be directly used to handle IPv6 firewall policy. To resolve the above problem, in this work, we use a formal technique to find and eliminate the inconsistencies between security policy and IPv6 firewall policy. We also build a prototype system and test the effectiveness of the proposed method through experiments. © 2023 ACM.

关键词： Network security

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