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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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YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algorithm 12

YOLOv8-GR: Gesture Recognition by Improving of YOLOv8 Algori...

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12th International Conference on Advanced Cloud and Big Data, CBD 2024

作者： Ding, Yi Guo, Naixuan Meng, Haitao School of Information Engineering Yancheng Institute of Technology Yancheng China Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration China

ISBN: (纸本)9798331511074

Gesture recognition has been widely used in many situations, such as human-computer interaction, virtual reality and smart home. To meet the current demands for accuracy and speed in gesture recognition, this paper proposes an improved algorithm based on YOLOv8, referred to as YOLOv8-GR. By replacing the original YOLOv8 detection head with a Dynamic Head, we integrate scale-aware, spatial-aware, and task-aware attention mechanisms within a unified framework. To enhance context information capture, the SPPF (Spatial Pyramid Pooling - Fast) module is optimized to a modified SPPF-LSKA (Large Separable Kernel Attention) module. In addition, we employ the EIoU loss function for more accurate evaluation of object detection results. To evaluate our algorithm, we integrate a gesture dataset in complex and diverse scenarios through network and field shooting annotations. Compared to the original YOLOv8, the proposed YOLOv8-GR achieves a 22.5% improvement in mAP50 and a 10.0% improvement in mAP50-95 on our dataset. We also compare YOLOv8-GR with the MobileNetv2-based SSD, which is a kind of one-stage object detection algorithm. While maintaining similar recognition accuracy, the computational cost of YOLOv8-GR is 9.9 GFLOPs, which is 15.8% of SSD, and our algorithm can be deployed on Raspberry Pi 4B. © 2024 IEEE.

关键词： Gesture recognition

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Evaluation Method of Object Control Capability for Children Based on Nonlinear Dynamics 23

Evaluation Method of Object Control Capability for Children ...

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3rd International Conference on Bioinformatics and Intelligent Computing, BIC 2023

作者： Wang, Huaijun Li, Chao Zhang, Dongsheng Li, Junhuai Key Laboratory of Network Computing and Security Technology School of Computer Science and Engineering Xi an University of Technology Xi'an China

ISBN: (纸本)9798400700200

DCD (Developmental Coordination Disorder) has become a global problem, and several studies have shown that early detection and intervention are important for the prevention and treatment of DCD in children. To address the problems of low intelligence, simple assessment indexes and single dimension in assessing children's motor coordination, a method based on non-linear dynamics is proposed to assess children's object control ability. Using the child's in-situ ball tapping as the test item, the Kinect and inertial sensors are used to collect hand spatial position information and human motion biomechanical signals during the ball tapping process. The complexity of the signal is reflected by the sample entropy of the motion signal. The approach suggested in this thesis, which uses non-linear kinetic characteristics, enhances the accuracy of behavioral and developmental identification by 1% to 3% when compared to existing methods. The experiments prove that the proposed method has a better recognition rate and stability than the traditional assessment methods. © 2023 ACM.

关键词： Principal component analysis

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Deep Learning-Based network Channel Quality Prediction Algorithm in High-Speed Mobile Scenarios 12

Deep Learning-Based Network Channel Quality Prediction Algor...

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12th International Conference on Computing and Pattern Recognition, ICCPR 2023

作者： Song, Heying Guo, Jia Song, Linying Wang, Luyao Zhu, Yexuan Li, Shiqiang Tianjin Normal University China Wireless Network Laboratory Department of Computer and Information Engineering Tianjin University of Technology China

ISBN: (纸本)9798400707988

As a comfortable and fast new generation popular transportation, high-speed rail(HSR) has benefited from the development of mobile Internet technology, and passengers' demand for information during HSR journeys has been increasing. At the same time, the demand for streaming media services by users is also constantly increasing. However, in high-speed mobile scenarios, streaming media transmission faces frequent network channel quality fluctuations and unpredictable issues. To address these problems, this paper proposes a channel quality prediction algorithm based on Long Short Term Memory network (LSTM) and Convolutional Neural network (CNN), called the CNN-LSTM model. This model can predict the available channel quality in real-time and improve the efficiency of streaming media transmission, enhancing the user's viewing experience. Through extensive simulation experiments, the feasibility and effectiveness of the channel quality prediction algorithm are validated. © 2023 ACM.

关键词： Long short-term memory

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Resource Cooperative Scheduling Optimization Considering Security in Edge Mobile networks 19th

Resource Cooperative Scheduling Optimization Considering Sec...

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19th EAI International Conference on Collaborative Computing: networking, Applications and Worksharing, CollaborateCom 2023

作者： Fang, Cheng Yang, Peng Yi, Meng Du, Miao Li, Bing Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China School of Computer Science and Engineering Southeast University Nanjing China School of Cyber Science and Engineering Southeast University Nanjing China

ISBN: (纸本)9783031545207

With the rapid development of technologies such as the Internet of Things and artificial intelligence, the contradiction between limited user computing resources and real-time, fast, and safe processing of large amounts of data has become an urgent issue. The emergence of edge computing provides IoT applications with a low-latency, high-bandwidth, and high-performance computing service. Due to the complexity and dynamics of the edge computing environment itself, and the limited resources of the terminal, the security issue of resource collaborative scheduling in the edge mobile network has become an important research topic. Different from existing work, this paper proposes an efficient and secure multi-user resource cooperative scheduling model, which comprehensively considers resource allocation, task offloading, QoE requirements, and data security. In the model, ChaCha20 encryption technology is introduced as a security mechanism to prevent data from being maliciously stolen by attackers during the offloading process, and computing speed is used as an indicator to quantify QoE requirements. A resource collaborative scheduling algorithm that integrates security mechanisms and computing acceleration is also proposed to minimize the total cost of optimizing the edge computing system. Finally, the effectiveness and superiority of the model and algorithm are verified by simulation experiments. © ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Edge computing

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Received value flipping based sphere decoding algorithm for polar codes

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Intelligent and Converged networks 2024年第4期5卷 370-379页

作者： Rui Wang Haiqiang Chen Yan Chen Yuanbo Liu Xiangcheng Li Youming Sun Qingnian Li School of Computer and Electronic Information Guangxi UniversityNanning 530004China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi UniversityNanning 530004China College of Information Engineering Nanning UniversityNanning 530004China

Polar codes are considered as one of the most competitive channel coding schemes for the future wireless communication *** improve the performance of polar codes with short code-length for control channels,a sphere decoding algorithm based on received value flipping is proposed in this *** a codeword fails the cyclic redundancy check,the algorithm flips the received value with low reliability and forms a new received ***,this new sequence is sent to the decoder for another decoding *** addition,we also compare the performance of different flipping sets and evaluate the influence of the associated flipping set *** results show that,the proposed algorithm can achieve performance improvement over additive white Gaussian noise channel with acceptable *** the(64,16)polar code,the proposed algorithm can achieve about 0.23 dB 10-3performance gain at frame error rate=,compared to the conventional sphere decoding ***,we also verify the applicability of the proposed algorithm over Rayleigh fading channel and observe similar results.

关键词： polar codes sphere decoding bit flipping row weight polarization weight

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A Provable Decision Rule for Out-of-Distribution Detection 41

A Provable Decision Rule for Out-of-Distribution Detection

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41st International Conference on Machine Learning, ICML 2024

作者： Ma, Xinsong Zou, Xin Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigating the decision rule based on the proposed score function. Different from previous work, this paper aims to design a decision rule with rigorous theoretical guarantee and well empirical performance. Specifically, we provide a new insight for the OOD detection task from a hypothesis testing perspective and propose a novel generalized Benjamini Hochberg (g-BH) procedure with empirical p-values to solve the testing problem. Theoretically, the g-BH procedure controls false discovery rate (FDR) at pre-specified level. Furthermore, we derive an upper bound of the expectation of false positive rate (FPR) for the g-BH procedure based on the tailed generalized Gaussian distribution family, indicating that the FPR of g-BH procedure converges to zero in probability. Finally, the extensive experimental results verify the superiority of g-BH procedure over the traditional threshold-based decision rule on several OOD detection benchmarks. Copyright 2024 by the author(s)

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Prediction of miRNA family based on class-incremental learning

Prediction of miRNA family based on class-incremental learni...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Liu, Yajun Qiu, Lulu Li, Aimin Fei, Rong Li, Junhuai Wu, Fang-Xiang Xi'an University of Technology Shaanxi Key Laboratory for Network Computing and Security Technology Xi'an China Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

With the development of deep sequencing, recent studies indicate that a miRNA precursor can generate multiple miRNA isoforms (isomiRs). The family prediction of canonical miRNAs and isomiRs could provide a basis for miRNA functional research. In this study, we propose a novel method for family identification of canonical miRNA and isomiRs based on incremental learning. First, a benchmark dataset is constructed by processing data based on miRNA sequences and their family annotation. Moreover, sequence embedding and RNN are used for capturing essential features and inherent dependencies within a sequence. Finally, incremental learning is applied to accommodate the continuous influx of miRNA sequencing data, enabling RNN to stay relevant and effective over time. Comparative experiments and ablation studies illustrate the effectiveness of our model, which can help to comprehensively understand miRNA's function. © 2024 IEEE.

关键词： Contrastive Learning

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Sequential Kernel Goodness-of-fit Testing 41

Sequential Kernel Goodness-of-fit Testing

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhengyu Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)

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Cloth-aware Augmentation for Cloth-generalized Person Re-identification 24

Cloth-aware Augmentation for Cloth-generalized Person Re-ide...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attire. Many existing CC-ReID methods overlook generalization, crucial for universality across cloth-consistent and cloth-changing scenarios. This paper pioneers exploring the cloth-generalized person re-identification (CG-ReID) task and introduces the Cloth-aware Augmentation (CaAug) strategy. Comprising domain augmentation and feature augmentation, CaAug aims to learn identity-relevant features adaptable to both scenarios. Domain augmentation involves creating diverse fictitious domains and simulating various clothing scenarios. Supervising features from different cloth domains enhances robustness and generalization against clothing changes. Additionally, for feature augmentation, element exchange introduces diversity concerning clothing changes. Regularizing the model with these augmented features strengthens resilience against clothing change uncertainty. Extensive experiments on cloth-changing datasets demonstrate the efficacy of our approach, consistently outperforming state-of-the-art methods. © 2024 ACM.

关键词： Feature extraction

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