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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wang, Wenxuan Huang, Yi Cui, Yunhe Guo, Chun Chen, Yi Shen, Guowei Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798331509712

Computation offloading technology plays an important role in Mobile Edge computing (MEC). Most mainstream Deep Reinforcement Learning (DRL)-based computation offloading methods employ random experience replay to train networks. This training method does not take into account the value differences between experiences, resulting in the decrease of training speeds and the increase of task completion delay, energy consumption, and task drop rate. Prioritized Experience Replay (PER) alleviates this issue to some extent. However, using Temporal Difference (TD) error as the criterion for the importance of experiences does not allow for the selection of experiences that are more "concerned"by the Actor network under the Actor-Critic framework. This limitation restricts the performance of the algorithm. To address these issues, this paper focuses on the computation offloading problem in scenarios with multiple mobile devices (MDs) and multiple MEC servers. A near-far area experience replay algorithm-based computation offloading method named NFAERCOM is proposed. NFAERCOM additionally considers the queuing delay at the MEC server and introduces a new near-far area experience replay algorithm. Evaluation results demonstrate that NFAERCOM effectively reduces the task completion delay, energy consumption, and task drop rate of tasks. © 2024 IEEE.

关键词： Deep reinforcement learning

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A Graph with Adaptive AdjacencyMatrix for Relation Extraction

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Computers, Materials & Continua 2024年第9期80卷 4129-4147页

作者： Run Yang YanpingChen Jiaxin Yan Yongbin Qin Engineering Research Center of Text Computing&Cognitive Intelligence Ministry of EducationGuizhou UniversityGuiyang550025China State Key Laboratory of Public Big Data Guizhou UniversityGuiyang550025China College of Computer Science and Technology Guizhou UniversityGuiyang550025China

The relation is a semantic expression relevant to two named entities in a *** a sentence usually contains several named entities,it is essential to learn a structured sentence representation that encodes dependency information specific to the two named *** related work,graph convolutional neural networks are widely adopted to learn semantic dependencies,where a dependency tree initializes the adjacency ***,this approach has two main ***,parsing a sentence heavily relies on external toolkits,which can be ***,the dependency tree only encodes the syntactical structure of a sentence,which may not align with the relational semantic *** this paper,we propose an automatic graph learningmethod to autonomously learn a sentence’s structural *** of using a fixed adjacency matrix initialized by a dependency tree,we introduce an Adaptive Adjacency Matrix to encode the semantic dependency between *** elements of thismatrix are dynamically learned during the training process and optimized by task-relevant learning objectives,enabling the construction of task-relevant semantic dependencies within a *** model demonstrates superior performance on the TACRED and SemEval 2010 datasets,surpassing previous works by 1.3%and 0.8%,*** experimental results show that our model excels in the relation extraction task,outperforming prior models.

关键词： Relation extraction graph convolutional neural network adaptive adjacency matrix

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VERY-RL: An Adaptive Image Steganography Algorithm Based on Reinforcement Learning 2

VERY-RL: An Adaptive Image Steganography Algorithm Based on ...

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2nd International Symposium on computing and Artificial intelligence, ISCAI 2023

作者： Long, Yong Qian, Qing Cui, Yunhe Information School Guizhou University of Finance and Economics Guiyang China Engineering Research Center of Text Computing and Cognitive Intelligence Guizhou University Guiyang China

ISBN: (纸本)9798400708954

Adaptive image steganalysis algorithm is one of the important research direction in the field of information security. Nowadays, digital image steganography based on deep learning has been widely used to protect sensitive data during transmission or storage. However, it is a great challenge to achieve the accurate location of the best steganography position in the cover image and improve the model training accuracy is a great challenge. To solve the above problems, a novel digital image steganography algorithm based on reinforcement learning - VERY-RL, is proposed to obtain the optimal embedding strategy. Meanwhile, according to the designed distortion function, the parameters of VERY-RL are updated by gradient backpropagation to minimize the embedding loss. Experimental results show that, compared with other steganographic methods based on reinforcement learning, the detection error rate of VERY-RL is improved by 21.94% when resisting steganalysis detection. In addition, the FLOPs of the model are reduced by 24.8%. It can be seen that VERY-RL improves the localization accuracy of pixel modification bits, and the steganography network has a stronger representation ability, indicating that VERY-RL has better steganographic security. © 2023 ACM.

关键词： Steganography

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SEED-VII: A Multimodal Dataset of Six Basic Emotions with Continuous Labels for Emotion Recognition

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IEEE Transactions on Affective computing 2024年第02期16卷 969-985页

作者： Jiang, Wei-Bang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Brain Science and Technology Research Center Shanghai200240 China

Recognizing emotions from physiological signals is a topic that has garnered widespread interest, and research continues to develop novel techniques for perceiving emotions. However, the emergence of deep learning has highlighted the need for comprehensive and high-quality emotional datasets that enable the accurate decoding of human emotions. To systematically explore human emotions, we develop a multimodal dataset consisting of six basic (happiness, sadness, fear, disgust, surprise, and anger) emotions and the neutral emotion, named SEED-VII. This multimodal dataset includes electroencephalography (EEG) and eye movement signals. The seven emotions in SEED-VII are elicited by 80 different videos and fully investigated with continuous labels that indicate the intensity levels of the corresponding emotions. Additionally, we propose a novel Multimodal Adaptive Emotion Transformer (MAET), that can flexibly process both unimodal and multimodal inputs. Adversarial training is utilized in the MAET to mitigate subject discrepancies, which enhances domain generalization. Our extensive experiments, encompassing both subject-dependent and cross-subject conditions, demonstrate the superior performance of the MAET in terms of handling various inputs. Continuous labels are used to filter the data with high emotional intensity, and this strategy is proven to be effective for attaining improved emotion recognition performance. Furthermore, complementary properties between the EEG signals and eye movements and stable neural patterns of the seven emotions are observed. © 2010-2012 IEEE.

关键词： Eye movements

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Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model

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Plant Phenomics 2023年第3期5卷 501-519页

作者： Qianding Huang Xingcai Wu Qi Wang Xinyu Dong Yongbin Qin Xue Wu Yangyang Gao Gefei Hao State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang 550025China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang 550025China National Key Laboratory of Green Pesticide Guizhou UniversityGuiyang 550025China

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.

关键词： Plant diagnosis image

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A Two Dimensional Feature engineering Method for Relation Extraction 5

A Two Dimensional Feature Engineering Method for Relation Ex...

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5th International Conference on Control, Robotics, and Intelligent System, CCRIS 2024

作者： Wang, Hao Chen, Yanping Yang, Weizhe Lin, Chuan Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry Huaxi District Guizhou China State Key Laboratory of Public Big Data Huaxi District Guizhou China Guizhou University Huaxi District Guizhou China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Transforming a sentence into a 2D representation, similar to table structures, reveals a semantic plane where elements can represent potential relations between paired entities. The 2D representation effectively addresses the common issue of overlapping relations. However, previous works directly transformed the representation from raw input, neglecting the utilization of prior knowledge, which is essential to support the entity relation extraction task. In this paper, we propose a 2D feature engineering method. The method innovatively integrates prior knowledge within 2D sentence representations, marking an advancement over previous strategies, and employs an attention mechanism to build the hitherto overlooked association between entities and prior knowledge. The fusion of prior knowledge and targeted attention enhances the model’s relational discernment capabilities. Evaluations on three benchmark datasets—ACE05 Chinese, ACE05 English, and CLTC—demonstrate the efficacy of the proposed method. It surpasses existing techniques, achieving state-of-the-art performance with F1 score improvements of 0.88%, 3.27%, and 2.83% respectively. The results indicate that 2D feature engineering can take advantage of a 2D sentence representation and make full use of prior knowledge. © 2024 SPIE.

关键词： Semantics

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DFGET: Displacement-Field Assisted Graph Energy Transmitter for Gland Instance Segmentation 17

DFGET: Displacement-Field Assisted Graph Energy Transmitter ...

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17th IEEE International Conference on Signal Processing, ICSP 2024

作者： Jian, Caiqing Qin, Yongbin Wang, Lihui Ye, Chen Cheng, Xinyu Guizhou University Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350387384

Gland instance segmentation is an essential but challenging task in the diagnosis of adenocarcinoma. The existing models usually achieve gland instance segmentation through multi-task learning and boundary loss constraint. However, how to deal with the problems of gland adhesion and inaccurate boundary in segmenting the complex samples remains a challenge. In this work, we propose a novel geometric feature named displacement field (DF), which can represent the geometric information such as shape, size, and orientation of the glands. Based on the explicit constraint of DF, a displacement-field assisted graph energy transmitter (DFGET) framework is proposed for solving the above problems. Using such graph framework, the gland semantic segmentation map and the displacement field of the graph nodes are estimated with two graph network branches. With the constraint of DF, a graph cluster module is presented to improve the intra-class feature consistency and inter-class feature discrepancy, as well as to separate the adherent glands. Extensive comparison and ablation experiments on the GlaS dataset demonstrate the effectiveness of the displacement field and the superiority of DFGET. Compared to the best comparative model, DFGET increases the object-Dice and object-F1 score by 2.27% and 3.11% respectively, while decreases the object-HD by 15.58, achieving state-of-the-art performance, which indicates that DFGET can serve as an auxiliary tool for the diagnosis of adenocarcinoma. © 2024 IEEE.

关键词： Semantic Segmentation

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VERY-RL: An Adaptive Image Steganography Algorithm Based on Reinforcement Learning 23

VERY-RL: An Adaptive Image Steganography Algorithm Based on ...

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Proceedings of the 2023 2nd International Symposium on computing and Artificial intelligence

作者： Yong Long Qing Qian Yunhe Cui Information School Guizhou University of Finance and Economics China Engineering Research Center of Text Computing & Cognitive Intelligence Guizhou University China

ISBN: (纸本)9798400708954

Adaptive image steganalysis algorithm is one of the important research direction in the field of information security. Nowadays, digital image steganography based on deep learning has been widely used to protect sensitive data during transmission or storage. However, it is a great challenge to achieve the accurate location of the best steganography position in the cover image and improve the model training accuracy is a great challenge. To solve the above problems, a novel digital image steganography algorithm based on reinforcement learning—VERY-RL, is proposed to obtain the optimal embedding strategy. Meanwhile, according to the designed distortion function, the parameters of VERY-RL are updated by gradient backpropagation to minimize the embedding loss. Experimental results show that, compared with other steganographic methods based on reinforcement learning, the detection error rate of VERY-RL is improved by 21.94% when resisting steganalysis detection. In addition, the FLOPs of the model are reduced by 24.8%. It can be seen that VERY-RL improves the localization accuracy of pixel modification bits, and the steganography network has a stronger representation ability, indicating that VERY-RL has better steganographic security.

关键词： Embedding strategy Feature extraction Image steganography Reinforcement learning Steganalysis

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NFAERCOM: A Near-Far Area Experience Replay-based Computation Offloading Method

NFAERCOM: A Near-Far Area Experience Replay-based Computatio...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Wenxuan Wang Yi Huang Yunhe Cui Chun Guo Yi Chen Guowei Shen Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education College of Computer Science and Technology Guizhou University Guiyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Computation offloading technology plays an important role in Mobile Edge computing (MEC). Most mainstream Deep Reinforcement Learning (DRL)-based computation offloading methods employ random experience replay to train networks. This training method does not take into account the value differences between experiences, resulting in the decrease of training speeds and the increase of task completion delay, energy consumption, and task drop rate. Prioritized Experience Replay (PER) alleviates this issue to some extent. However, using Temporal Difference (TD) error as the criterion for the importance of experiences does not allow for the selection of experiences that are more "concerned" by the Actor network under the Actor-Critic framework. This limitation restricts the performance of the algorithm. To address these issues, this paper focuses on the computation offloading problem in scenarios with multiple mobile devices (MDs) and multiple MEC servers. A near-far area experience replay algorithm-based computation offloading method named NFAERCOM is proposed. NFAERCOM additionally considers the queuing delay at the MEC server and introduces a new near-far area experience replay algorithm. Evaluation results demonstrate that NFAERCOM effectively reduces the task completion delay, energy consumption, and task drop rate of tasks.

关键词： Training Performance evaluation Energy consumption Runtime Multi-access edge computing Processor scheduling Mobile handsets Delays Servers Computational complexity

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An Improved YOLOv5s-Based Smoke Detection System for Outdoor Parking Lots

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Computers, Materials & Continua 2024年第8期80卷 3333-3349页

作者： Ruobing Zuo Xiaohan Huang Xuguo Jiao Zhenyong Zhang State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhou310027China Key Laboratory of Computing Power Network and Information Security Ministry of EducationQilu University of Technology(Shandong Academy of Sciences)Jinan250353China Text Computing&Cognitive Intelligence Engineering Research Center of National Education Ministry Guizhou UniversityGuiyang550025China

In the rapidly evolving urban landscape,outdoor parking lots have become an indispensable part of the city’s transportation *** growth of parking lots has raised the likelihood of spontaneous vehicle combus-tion,a significant safety hazard,making smoke detection an essential preventative ***,the complex environment of outdoor parking lots presents additional challenges for smoke detection,which necessitates the development of more advanced and reliable smoke detection *** paper addresses this concern and presents a novel smoke detection technique designed for the demanding environment of outdoor parking ***,we develop a novel dataset to fill the gap,as there is a lack of publicly available *** dataset encompasses a wide range of smoke and fire scenarios,enhanced with data augmentation to ensure robustness against diverse outdoor ***,we utilize an optimized YOLOv5s model,integrated with the Squeeze-and-Excitation Network(SENet)attention mechanism,to significantly improve detection accuracy while maintaining real-time processing ***,this paper implements an outdoor smoke detection system that is capable of accurately localizing and alerting in real time,enhancing the effectiveness and reliability of emergency *** show that the system has a high accuracy in terms of detecting smoke incidents in outdoor scenarios.

关键词： YOLOv5s smoke detection deep learning SENet

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