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Advances and challenges in artificial intelligence text generation

Frontiers of Information Technology & Electronic engineering 2024年第1期25卷 64-83页

作者： Bing LI Peng YANG Yuankang SUN Zhongjian HU Meng YI School of Computer Science and Engineering Southeast UniversityNanjing 210000China Key Laboratory of Computer Network and Information Integration Ministry of EducationSoutheast UniversityNanjing 210000China

Text generation is an essential research area in artificial intelligence(AI)technology and natural language processing and provides key technical support for the rapid development of AI-generated content(AIGC).It is based on technologies such as natural language processing,machine learning,and deep learning,which enable learning language rules through training models to automatically generate text that meets grammatical and semantic *** this paper,we sort and systematically summarize the main research progress in text generation and review recent text generation papers,focusing on presenting a detailed understanding of the technical *** addition,several typical text generation application systems are ***,we address some challenges and future directions in AI text *** conclude that improving the quality,quantity,interactivity,and adaptability of generated text can help fundamentally advance AI text generation development.

关键词： AI text generation Natural language processing Machine learning Deep learning

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JNMR: Joint Non-Linear Motion Regression for Video Frame Interpolation

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IEEE Transactions on Image Processing 2023年 32卷 5283-5295页

作者： Liu, Meiqin Xu, Chenming Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods. The code is available at https://***/ruhig6/JNMR. © 1992-2012 IEEE.

关键词： Interpolation

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BiRSwinT: Bilinear full-scale residual swin-transformer for fine-grained driver behavior recognition

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Journal of the Franklin Institute 2023年第2期360卷 1166-1183页

作者： Yang, Wenxuan Tan, Chenghao Chen, Yuxin Xia, Huang Tang, Xuexi Cao, Yifan Zhou, Wenhui Lin, Lili Dai, Guojun School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Hangzhou Ocean Engine Network Technology Co. Ltd. China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China

Driver behavior recognition has attracted extensive attention recently. Numerous methods have been developed on the basis of various deep neural networks. However, the existing models still suffer from various challenges in solving the downstream fine-grained recognition task, because many driver behaviors share similar global shape / appearances, and can only be discriminated by subtle but meaningful actions of local body parts. To remedy this emerging issue of fine-grained driver behavior recognition, we present a bilinear full-scale residual Swin-Transformer network (BiRSwinT) to learn and fuse the global shape/appearance and the local discriminative cues of driver actions. Specifically, a transformer-based dual-stream structure is designed, which contains two parallel branches: a global representation branch and a local residual branch. To learn the multi-scale local cues implied in driver actions, a set of full-scale skip connections are introduced in the local residual branch. Then a bilinear fusion with multi-step training strategy is employed to leverage and combine the global representation and local detail features of driver actions. The proposed method has been validated on the AUC V1 and V2 datasets, and achieved the average Top-1 accuracy of 93.235% and 92.253%, respectively. Comparison experiments show that the proposed method has superior performance when compared to the state-of-the-art methods. The generalization experiments on the State-Farm dataset also demonstrate the superiority of the proposed framework and strategy. The source code is available at https://***/shadow2469/***. © 2022 The Franklin Institute

关键词： Deep neural networks

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Improving Multimodal Rumor Detection via Dynamic Graph Modeling 27th

Improving Multimodal Rumor Detection via Dynamic Graph Model...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Wu, Xinyu Hu, Xiaoxu Qin, Xugong Zhang, Peng Zeng, Gangyan Guo, Yu Zhao, Runbo Huang, Xinjian School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China National Computer Network Response Technical Team/Coordination Center Technology Beijing China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China

ISBN: (纸本)9783031784552

Due to the proliferation of rumors in social networks, automatic rumor detection has evoked increasing attention in recent years. Despite great progress achieved by exploiting multimodal features, existing works suffer from false discrimination issues due to insufficient multimodal modeling, mainly from two aspects: 1) neglect of the dynamicity of social networks. 2) misaligned multimodal features. To alleviate the issues, we propose DGM, Dynamic Graph Modeling for rumor detection. Firstly, dynamic graph attention is devised to exploit message propagation’s structural and temporal features. Secondly, we propose a modality-shared adapter to learn better multimodal representation. Thirdly, well-aligned visual-textual features are introduced to achieve better multimodality alignment and fusion, together with cross-modal attention and alignment supervision. We conduct extensive experiments on two public datasets, demonstrating the effectiveness and superiority of DGM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Social networking (online)

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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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LT-DBFT: A Hierarchical Blockchain Consensus Using Location and Trust in IoT

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IEEE Internet of Things Journal 2025年

作者： Wang, Yang Xing, Xiaofei Li, Peiqiang Wang, Guojun Guangzhou University School of Computer Science and Cyber Engineering Guangzhou510006 China Guangzhou University School of Computer Science and Network Engineering Guangzhou510006 China

The exponential growth of IoT devices has led to the proposal of edge computing for data processing. The decentralized nature of edge computing servers and IoT devices makes blockchain ideal for connecting IoT users and servers. The consensus protocol, a core technology in blockchain, ensures node agreement and operational efficiency. However, as node numbers increase and spread geographically, traditional consensus protocols face deployment challenges, significantly reducing efficiency. To address this, we propose a hierarchical blockchain consensus protocol based on geographic location and a trust model called Location and Trust Delegated-BFT (LT-DBFT). This protocol reduces global communication delays caused by wide geographic distribution by assigning consensus nodes to different clusters based on their locations. A trust model is also designed to elect active nodes for consensus voting, thereby reducing the consensus overhead within clusters. Subsequently, the primary nodes of each cluster form a global shared layer to achieve the final ordering and execution of transactions. Through theoretical and experimental analysis, our scheme demonstrates lower latency and higher throughput performance than traditional PBFT and GeoBFT, making it more suitable for efficient deployment in large-scale and geographically widespread IoT environments. © 2014 IEEE.

关键词： Blockchain Consensus Protocol Internet of Things (IoT) Practical Byzantine Fault Tolerance (PBFT) Throughput

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Uncovering network Vulnerabilities and Conducting Digital Forensics Analysis for IoT Device Security in 6G 17

Uncovering Network Vulnerabilities and Conducting Digital Fo...

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17th IEEE International Conference on Advanced networks and Telecommunications Systems, ANTS 2023

作者： Siddiqui, Shams Tabrez Shahid Kamal, Mohammad Qidwai, Khalid Ali Imran Alam, Md Khan, Haneef Alam, Malik Zaib Budhiraja, Ishan Chintham, Sandeep Jazan University Department of Computer Science 45142 Saudi Arabia Jazan University Department of Computer and Network Engineering 45142 Saudi Arabia Bennett University School of Computer Science Engineering and Technology Greater Noida India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350307672

The proliferation of IoT gadgets has introduced game-changing technologies across a range of sectors. This widespread use has, however, revealed serious security holes, underscoring the importance of rigorous security measures and digital forensics investigation. Everyday objects are getting online connections, including eyewear, smart-watches, stoves, microwaves, door locks, etc., with most of their data being stored in the cloud. Information is the lifeblood of digital forensics. There was a time when digital forensics only dealt with devices like computers and mobile phones that were physically present at a crime scene. Forensic investigators face new challenges and opportunities as a result of the proliferation of digital data outside traditional storage mediums. This paper explains, what are security breaches, and different IoT security challenges, due to the increase in IoT devices day by day. And investigate the IoT devices by using a digital forensics investigation process that consists of different phases. And finally, discussed recent IoT forensics approaches and frameworks overview © 2023 IEEE.

关键词： Internet of things

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HY-PC:Enabling Consistent Positioning and Communication Using Visible Light

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China Communications 2023年第4期20卷 180-194页

作者： Xiangyu Liu Yaoheng Chen Lei Guo Song Song School of Information Science and Engineering Shenyang Ligong UniversityShenyang 110158China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Hangzhou Institute of Advanced Technology Hangzhou 310056China Institute of Intelligent Communications and Network Security Chongqing University of Posts and TelecommunicationsChongqing 400065China

Visible light(VL)plays an important role in achieving high-precision positioning and low bit error radio(BER)data ***,most VL-based systems can not achieve positioning and communication,*** are two problems:1)the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance,2)in the hybrid systems,the lost data bits in the inter-frame gap(IFG)are hard to recover,which affects positioning and communication ***,in this article,we propose a novel VL-based hybrid positioning and communication system,named HY-PC system,to solve the above ***,we propose the robust T-W mapping for recognizing specific Light Emitting Diodes(LEDs),which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication ***,we also propose the novel linear block coding and bit interleaving mechanism,which can recover the lost data bits in the IFG and improve data communication ***,we use commercial off-the-shelf devices to implement our HY-PC system,extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication,simultaneously.

关键词： visible light positioning visible light communication T-W Mapping linear block coding bit interleaving

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SenseFuzz: Black-box Fuzzing of ICS Protocols via Runtime Statistics Sensing 9

SenseFuzz: Black-box Fuzzing of ICS Protocols via Runtime St...

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9th IEEE Smart World Congress, SWC 2023

作者： Li, Yin Fu, Anmin Zhang, Yuqing University of Chinese Academy of Sciences National Computer Network Intrusion Protection Center Beijing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350319804

The ICS protocols undertake critical functions in industrial control systems, by enabling real-time data exchange, data acquisition, parameter configuration, status monitoring, fault diagnosis, command issuance and execution. It serves as the foundation for the collaborative operations of all components in industrial control systems. With the development of internet technology, industrial control systems, which were originally physically isolated, have become new targets for hackers. However, due to the high level of privatization of ICS protocols and their complex workflow, existing methods are insufficient in detecting vulnerabilities in these protocols. This paper proposes a black-box fuzzing test method based on sensing the program runtime feedback. By collecting run-time statistics, including CPU utilization, memory utilization, disk I/O, and message interaction, our method is able to determine whether the program has triggered a new execution path, thus improving the efficiency of the fuzzing procedure. We evaluated our prototype on five open-source ICS protocol programs and discovered six reported vulnerabilities, among which five could cause the industrial control programs to become unresponsive or crash. © 2023 IEEE.

关键词： Fuzzing ICS Protocols

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Never Lost Keys:A Novel Key Generation Scheme Based on Motor Imagery EEG in End-Edge-Cloud System

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China Communications 2022年第7期19卷 172-184页

作者： Yichuan Wang Dan Wu Xiaoxue Liu Xinhong Hei School of computer science and Engineering Xi’an University of TechnologyXi’an 710048China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an 710048China

Biometric key is generated from the user’s unique biometric features,and can effectively solve the security problems in ***,the current prevailing biometric key generation techniques such as fingerprint recognition and facial recognition are poor in randomness and can be forged *** to the characteristics of Electroencephalographic(EEG)signals such as the randomness,nonlinear and non-stationary etc.,it can significantly avoid these *** paper proposes a novel method to generate keys based on EEG signals with end-edgecloud collaboration *** sensors to measure motor imagery EEG data,the key is generated via pre-processing,feature extraction and *** show the total time consumption of the key generation process is about *** scheme is practical and feasible,which provides a research route to generate biometric keys using EEG data.

关键词： EEG biometric key generation end-edgecloud system information security

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