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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Xu, Wanni Guan, Minghua Fu, You-Lei Xie, Weifang Xiamen Academy of Arts and Design Fuzhou University Xiamen China School of Design and Fashion Zhejiang University of Science & Technology Zhejiang China College of Big Data and Internet Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798350350890

This research develops an optimized exoskeleton design for human-robot interaction, using surface electromyography (sEMG) to detect patient movement intentions and motor functions. The study involved rapid identification of human gait events, joint angles, and movements, using data from six individuals performing various movements on a 3D force platform. The exoskeleton and motion analysis system (MAS) processed this data, which was utilized for model training and validation. The evaluation was conducted using Root-mean-square error (RMSE) and Correlation Coefficient (CC). We introduced a novel sEMG analysis method leveraging deep and integrated learning to directly classify sEMG signals, achieving high classification accuracy (AUC of 0.9554). A fast-training classification technique utilizing motor-related cortical potentials and varied neural oscillations also exceeded the accuracy of existing classifiers, effectively supporting joint motion recognition and prediction. The optimized exoskeleton provides a viable and safe rehabilitation alternative for patients with neurological disabilities, offering a potential replacement for conventional therapies. © 2024 IEEE.

关键词： Exoskeleton (Robotics)

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Boundary-Enhanced Network for Semantic Segmentation of Remote Sensing Images 5

Boundary-Enhanced Network for Semantic Segmentation of Remot...

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

作者： You, Qian Lyu, Xin Li, Chunhua Li, Xin Chen, Shangjing Fang, Yiwei Jiang, Wenxuan College of Computer Science and Software Engineering Hohai University Nanjing211100 China Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing211100 China

ISBN: (数字)9781510685871

ISBN: (纸本)9781510685864

Improving the accuracy of semantic segmentation for remote sensing images (RSIs) is crucial for the geoscientific research and applications. However, existing models tend to focus too much on the subject information for semantic objects, but less on the global-local boundary information of images. To tackle this issue, we introduce a novel approach named the Boundary Enhanced Network (BENet), which applies synchronized attention to both global and local aspects of images. Specifically, the proposed method utilizes the Global-Local Boundary Fusion module (GLBF) to aggregate the boundary information of the objects from a high-dimensional feature perspective. To adapt to the multi-scale characteristics of RSIs, GLBF is embed hierarchically to maintain boundary details across different stages. Besides, a Feature Enhancement Module (FEM) is devised with sequentially DEConv and Squeeze-and-Excitation (SE) blocks to refine the features from decoder. The experiments on the ISPRS Vaihingen and Potsdam datasets showed that BENet outperforms mainstream methodology, OA, mIoU and mF1 metrics were all significantly elevated. © 2024 SPIE.

关键词： Semantic Segmentation

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Semantics Guided Diffusion for Deep Joint Source and Channel Coding Design 16

Semantics Guided Diffusion for Deep Joint Source and Channel...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Zhang, Maojun Zhu, Guangxu Han, Kaifeng Jin, Richeng Chen, Xiaoming College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Shenzhen Research Institute of Big Data Shenzhen China China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9798350390643

Semantic communication (SemCom) offers a promising avenue for enhancing transmission efficiency, especially as traditional bit-level communication approaches its theoretical limits. A key enabling technology in SemCom is deep learning-based joint source and channel coding (DeepJSCC). However, existing DeepJSCC methods lack interpretability because they transmit semantic information implicitly by mapping data to latent features. Additionally, the black-box nature of neural networks makes it challenging to ensure the reliable transmission of critical semantics. To address these challenges, we propose advancing DeepJSCC towards a more 'semantic' approach. Specifically, we suggest transmitting interpretable and lightweight semantics as side information alongside JSCC latent features. At the receiver end, we introduce a novel latent diffusion model designed for wireless communication, trained from scratch to integrate seamlessly with DeepJSCC. Using the semantic side information, the receiver employs the proposed semantics-guided latent diffusion for denoising. Furthermore, since accurate channel state information (CSI) is essential in practice, we propose estimating CSI directly from the channel output. The estimated CSI is then used for step matching in the denoising diffusion process, enabling CSI-free transmission. Finally, the denoised feature is fed into the JSCC decoder to reconstruct the image. Numerical results demonstrate that our proposed scheme can achieve comparable performance without accurate CSI. Additionally, guided by semantic information and leveraging the powerful diffusion model, our method surpasses current DeepJSCC schemes, delivering satisfactory reconstruction performance even at SNR = -5dB. This proposed scheme highlights the potential of incorporating diffusion models in future SemCom systems and suggests several promising applications. © 2024 IEEE.

关键词： Channel coding

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Multifactor prediction of carbon price based on principal component analysis and long short-term memory 3

Multifactor prediction of carbon price based on principal co...

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3rd International Conference on Electronic Information Engineering, big data, and Computer technology, EIBDCT 2024

作者： Wang, Yuanling Wen, Hao Deng, Shuyun Xia, Xin Chongqing Key Laboratory of Public Big Data Security Technology Chongqing College of Mobile Communication Chongqing401527 China School of Management Science and Engineering Chongqing Technology and Business University Chongqing400067 China

ISBN: (纸本)9781510680449

In recent years, the development of the carbon trading market has become an important policy for China to achieve its "dual carbon"goals at an early date and to take on the role of a major international player in reducing emissions. Since the opening of the carbon market in 2013, there are now eight mature carbon trading pilot cities in China. Considering that carbon prices are susceptible to influences from factors such as the natural environment and financial policies, this paper introduces eight influencing factors from the energy, economic, financial, and natural environment levels for analysis. This paper selects the carbon trading market in Hubei, with the highest carbon trading volume, and the carbon trading market in Fujian, with the lowest carbon trading volume, as research objects. Firstly, the PCA model is applied to extract the main factors that influence carbon prices from the eight influential factors. Then, the LSTM model is used to establish a multifactor prediction model, and the GRU, RNN, and CNN models are used as comparative models. Finally, the predicted fitting graph is drawn, and the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are calculated. The research results show that the multi-factor prediction model based on LSTM performs the best in terms of price prediction accuracy. Through an in-depth exploration of various models, this study has made a significant contribution to the progress of carbon market research. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Carbon

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Virtual Image Generation: Bridging Reality and Virtuality for Long-Tail Traffic scenes 4

Virtual Image Generation: Bridging Reality and Virtuality fo...

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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Xu, Feibing Zhang, Hui Suo, Xiaohua Li, Yidong Beijing Jiaotong University Ministry of Education Key Laboratory of Big Data & Artificial Intelligence in Transportation China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China

ISBN: (纸本)9798350349252

Although visual perception algorithms have made significant progress in most normal scenes, it is still challenging for autonomous driving systems to accurately perceive long-tail scenes that occur less frequently, which can lead to serious traffic safety issues. However, existing open-source datasets do not systematically collect sufficient long-tail scenes. To fill this gap, we propose a pipeline for designing large-scale, diverse long-tail traffic scenes and generating virtual datasets based on the parallel vision approach. A virtual dataset named Vir-LTTS (virtual long-tail traffic scenes) is built, comprising various scenes such as extreme weather conditions, adverse lighting conditions, traffic accidents, unique forms of traffic objects, and blurry images caused by camera defects. We investigate the potential of training models using the Vir-LTTS dataset in long-tail traffic scenes. Experimental results show that pre-training with Vir-LTTS significantly improves the performance of visual models in long-tail traffic scenes. © 2024 IEEE.

关键词： Virtual environments

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Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised Semantic Hashing 24

Bit-mask Robust Contrastive Knowledge Distillation for Unsup...

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33rd ACM Web Conference, WWW 2024

作者： He, Liyang Huang, Zhenya Liu, Jiayu Chen, Enhong Wang, Fei Sha, Jing Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China School of Data Science University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Unsupervised semantic hashing has emerged as an indispensable technique for fast image search, which aims to convert images into binary hash codes without relying on labels. Recent advancements in the field demonstrate that employing large-scale backbones (e.g., ViT) in unsupervised semantic hashing models can yield substantial improvements. However, the inference delay has become increasingly difficult to overlook. Knowledge distillation provides a means for practical model compression to alleviate this delay. Nevertheless, the prevailing knowledge distillation approaches are not explicitly designed for semantic hashing. They ignore the unique search paradigm of semantic hashing, the inherent necessities of the distillation process, and the property of hash codes. In this paper, we propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method, specifically devised for the distillation of semantic hashing models. To ensure the effectiveness of two kinds of search paradigms in the context of semantic hashing, BRCD first aligns the semantic spaces between the teacher and student models through a contrastive knowledge distillation objective. Additionally, to eliminate noisy augmentations and ensure robust optimization, a cluster-based method within the knowledge distillation process is introduced. Furthermore, through a bit-level analysis, we uncover the presence of redundancy bits resulting from the bit independence property. To mitigate these effects, we introduce a bit mask mechanism in our knowledge distillation objective. Finally, extensive experiments not only showcase the noteworthy performance of our BRCD method in comparison to other knowledge distillation methods but also substantiate the generality of our methods across diverse semantic hashing models and backbones. The code for BRCD is available at https://***/hly1998/BRCD. © 2024 Owner/Author.

关键词： Distillation

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Psychological Counseling with Integration of Knowledge Graph and Multi-Agent Collaboration

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Journal of Shanghai Jiaotong University (science) 2025年 1-13页

作者： Liu, Xi Zheng, Xiaohu Cui, Weiwei College of Information Science and Technology Donghua University Shanghai201620 China Institute of artificial Intelligence Donghua University Shanghai201620 China Shanghai Engineering Center of Industrial Big Data and Intelligent System Shanghai201620 China Management College Wuzhou University Guangxi Wuzhou543003 China

Applying large language models (LLMs) to psychological counseling can reduce costs and lower barriers to access. However, issues inherent to LLMs, such as output hallucinations, knowledge lags, and response biases, limit the quality of counseling services. To address these challenges, we propose a psychological counseling method that integrates a knowledge graph (KG) and multi-agent collaboration (MAC). First, we design an ontology model and use an improved convolution residual network (CRN) to extract relational triples for constructing a psychological medicine KG. Second, we perform low-rank adaptation (LoRA) on Qwen1.5-7B-chat using the Text2Cypher training set. Finally, we design MAC prompts based on the characteristics of the retrieval-based question-answering (QA) process to facilitate the collaborative implementation of psychological medicine knowledge QA and updates. The experiments showed that the improved CRN achieved an extraction accuracy of 80%. The mean squared error of the general-domain capability of the LLM before and after LoRA was only 0.738, with a Text2Cypher score of 99.8 after LoRA. The results generated after prompting received a 70.44% approval rate compared to other methods. Thus, the proposed method can effectively enhance the intelligence, accuracy, and controllability of psychological counseling. © Shanghai Jiao Tong University 2024.

关键词： Knowledge graph

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Joint user profiling with hierarchical attention networks

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Frontiers of Computer science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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Blockchain for Knowledge-driven Wireless Network Security 4

Blockchain for Knowledge-driven Wireless Network Security

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4th International Conference on Sensors and Information technology, ICSI 2024

作者： Zhang, Dehua Kuang, Chunxu Yang, Xiaopeng Li, Hao Big Data Research Center of Hebei Province Hebei Province Shijiazhuang City050000 China Science and Technology on Communication Networks Laboratory Hebei Province Shijiazhuang City050000 China

ISBN: (数字)9781510678699

ISBN: (纸本)9781510678682

Fueled by the capabilities of artificial intelligence and cloud computing, the development trend of future networks towards intelligence and marginalization is becoming increasingly evident. The knowledge-driven learning model as the basis of future network intelligence, fully utilizes various direct and indirect knowledge and automatic learning of knowledge variable values to achieve minimum resource cost and optimal communication capability. Correspondingly, security of the future wireless network faces new challenges. Traditional security schemes are generally composed of encryption, authentication, access control, and other modules. These schemes often require complex and centralized management systems, which are vulnerable to impersonation attacks, distributed denial of service attacks, and other network attacks. In recent years, blockchain technology has become a powerful tool for solving the above-mentioned problems due to its decentralized and tamper-resistant characteristics, that will effectively enhance wireless network defense capabilities. Many recent studies have explored using blockchain-based solutions in wireless networks to enhance the security capabilities of wireless networks by implementing distributed identity authentication, hierarchical access control, data security sharing, privacy protection, and other techniques. But the performance of blockchain has always been a fatal issue. This paper investigates the existing blockchain-based wireless network security solutions and sorts and categorizes them. Targeting the Characteristics of knowledge-driven wireless networks, this paper designs a partitioned and layered blockchain security architecture, and conducts experiments and security analysis on the architecture, the architecture can improve the transaction processing speed to a certain extent. © 2024 SPIE.

关键词： Blockchain

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Future Healthcare Recommender Systems: Applications, Open Issues, and Challenges

Future Healthcare Recommender Systems: Applications, Open Is...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Ju, Hongzheng Jin, Kebing Tang, Jianhang Zhang, Yang Wang, Bo Xiong, Zehui State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (纸本)9798350350548

With the enhancement of health awareness and the development of artificial intelligent technology, healthcare recommender systems (HRS) play an increasingly important role in individual health management. Meanwhile, the widespread usage of large models has significantly improved the efficiency and accuracy in the analysis and utilization of medical data. In this paper, we comprehensively summarize the basic types of recommender systems as well as the new trends in utilizing large models. Then we introduce the recommendation applications in healthcare areas from six aspects, i.e., disease risk prediction, medication recommendation, medical resource recommendation, mental health support, health life management, and health education. At last, we explore some current issues and challenges within HRS, as well as the development of potential solutions and directions in the future. © 2024 IEEE.

关键词： Electronic health record

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