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检索条件"机构=Key Laboratory of Intelligent Computer and Signal Processing"

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ResPocket: A Multi-Scale Feature Fusion Method for Improving Protein Binding Site Detection

ResPocket: A Multi-Scale Feature Fusion Method for Improving...

引用

2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Xiaoli Yang, Wenli Chen, Yaoyu Liao, Xiongwei Wu, Zimo Zhang, Xiaolong Wuhan University of Science and Technology Hubei Key Laboratory of Intelligent Information Processing and Real-Time Industrial System School of Computer Science and Technology China

ISBN: (纸本)9798350386226

Accurate detection of protein binding sites is critical for facilitating drug design. Most existing models rely on features at a single scale, leading to the loss of crucial global or local structural information. Therefore, this paper proposes a novel 3D model (ResPocket), to fully capture the multi-scale features of proteins. First, Residual Block (ResB) based on U-Net is used to capture features from different scales, including global and local structural information. Then, an Enhanced Attention Module (EAM) is designed to encode semantic dependencies by effectively capturing global context information. In addition, ResPocket focuses on learning local information about protein pockets with Self-Guided Mask Block (SGMB), to increase the robustness of the overall model and reduce its dependence on external mask guidance. Experimental results show that ResPocket improves by an average of 4.89% on DCA Top-n, by an average of 1.48% and 2.25% on DCC and DVO metrics, respectively. © 2024 IEEE.

关键词： Binding sites

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Registration Error-resistant Track-to-track Association for Multiple Sensors 2

Registration Error-resistant Track-to-track Association for ...

引用

2nd IEEE International Conference on signal, Information and Data processing, ICSIDP 2024

作者： Zhang, Shuyang Gao, Chang Zhang, Qingfu Yan, Junkun Jia, Tianyi Wang, Rongrong Liu, Hongwei Xidian University National Key Laboratory of Radar Signal Processing Xi'an China City University of Hong Kong Department of Computer Science Kowloon Hong Kong

ISBN: (纸本)9798331515669

The process of Multi-sensor Track-to-Track Association (MTTA) involves the association of tracks observed by multiple sensors, which are of the same target. This process serves as a foundation for enhancing track continuity and refining state estimation via the fusion of multi-sensor information. Predominantly, the existing methods employ statistical modeling of target motion to devise association gates between tracks. However, these methods encounter a decrease in association accuracy when faced with unknown registration errors. To address this challenge, a data-driven approach is proposed, which reconfigures track association into a classification problem. This approach harnesses target kinematic characteristics and track trajectory features extracted at the track level. Subsequently, these features are employed in constructing a track association algorithm based on decision trees. Experiments are conducted on a publicly available dataset, Multi-source Track Association Dataset (MTAD) derived from real data. The results have demonstrated superior association performance, particularly under conditions of inadequate system registration. © 2024 IEEE.

关键词： Information fusion

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Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation

Inference-Time Decontamination: Reusing Leaked Benchmarks fo...

引用

2024 Conference on Empirical Methods in Natural Language processing, EMNLP 2024

作者： Zhu, Qin Cheng, Qinyuan Peng, Runyu Li, Xiaonan Liu, Tengxiao Peng, Ru Qiu, Xipeng Huang, Xuanjing School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China College of Computer Science and Technology Zhejiang University China

ISBN: (纸本)9798891761681

The training process of large language models (LLMs) often involves varying degrees of test data contamination (Yang et al., 2023b).Although current LLMs are achieving increasingly better performance on various benchmarks, their performance in practical applications does not always match their benchmark *** of benchmarks can prevent the accurate assessment of LLMs' true ***, constructing new benchmarks is costly, labor-intensive and still carries the risk of ***, in this paper, we ask the question "Can we reuse these leaked benchmarks for LLM evaluation?" We propose Inference-Time Decontamination (ITD) to address this issue by detecting and rewriting leaked samples without altering their *** can mitigate performance inflation caused by memorizing leaked *** proof-of-concept experiments demonstrate that ITD reduces inflated accuracy by 22.9% on GSM8K and 19.0% on *** MMLU, using Inference-time Decontamination can lead to a decrease in the results of Phi3 and Mistral by 6.7% and 3.6% *** hope that ITD can provide more truthful evaluation results for large language models. © 2024 Association for Computational Linguistics.

关键词： Decontamination

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AI-Based Text Detection and Classification for Smart City Monitoring Using BERT and TextCNN Models

AI-Based Text Detection and Classification for Smart City Mo...

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2024 International Symposium on Internet of Things and Smart Cities, ISITSC 2024

作者： Liu, Yunxia Li, Mingyuan Ye, Zhonglin Shi, Xiaoan Wang, Weijie Zhao, Haixing College of Computer Qinghai Normal University Xining810000 China The State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810000 China

ISBN: (纸本)9798331505516

This paper proposes an Artificial Intelligence (AI) text detection and classification model that combines Bidirectional Encoder Representations from Transformers (BERT) and Text Convolutional Neural Network (TextCNN), applicable for smart city social media monitoring. By leveraging BERT's powerful contextual semantic understanding capabilities and TextCNN's local feature extraction abilities, the model achieves efficient text detection and classification. The model first utilizes BERT to perform semantic representation of the input text, capturing rich contextual features. These features are then fed into the TextCNN model, where multiple convolutional and pooling operations extract and compress the features. Finally, a fully connected layer converts the extracted features into fixed-length vectors for precise classification prediction. Experimental results show that this hybrid model significantly outperforms traditional baseline models on two public datasets, Human ChatGPT Comparison Corpus (HC3) and sharegpt_gpt4. The hybrid model demonstrates notable improvements in key metrics such as Accuracy (ACC), Precision (PREC), Recall (REC), and F1-score (F1). For example, on the sharegpt_gpt4 dataset, the accuracy reaches 0.8490, making a significant improvement over the baseline model. This validates the effectiveness and superiority of combining BERT and TextCNN in text classification tasks. The innovation of this paper lies in the novel integration of BERT and TextCNN, harnessing the contextual semantic understanding of BERT with the local feature extraction of TextCNN, resulting in enhanced text classification performance. The use of diverse datasets such as HC3 and sharegpt_gpt4 showcases the model's robustness across various text types. Additionally, the model's application in smart city social media monitoring demonstrates its practical relevance, providing accurate and efficient text detection crucial for monitoring public sentiment and emerging issues in real-t

关键词： Smart city

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引用

4th International Conference on computer Science and Blockchain, CCSB 2024

作者： Huang, Guimin Shi, Canjun School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350356502

Short textual similarity has been more and more widely used in the field of natural language processing. However, people tend to pay more attention to the semantic similarity of sentences and ignore the word order similarity in sentences. For this reason, we propose a short text similarity computation model that fuses semantics and word order. In terms of semantics, we propose a adjacency-aware similarity model based on deep convolutional neural networks, which effectively exploits the adjacency relationship in sentences and extracts text features from multiple perspectives for similarity prediction. In terms of word order, a word order model incorporating an external knowledge base is proposed, which uses an external knowledge base with pointwise mutual information to construct word order dense vectors to assess word order similarity. Ultimately, we propose a comprehensive assessment model for short text similarity that incorporates semantic and word order information. We conducted experiments on the public dataset MRPC, and the experimental results show that our model outperforms existing models in terms of performance. © 2024 IEEE.

关键词： Convolutional neural networks

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Network link weight optimization based on antisymmetric deep graph networks and reinforcement learning 6

Network link weight optimization based on antisymmetric deep...

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6th International Conference on Next Generation Data-Driven Networks, NGDN 2024

作者： Chen, Yuhan Guo, Yingya Department of Computer and Data Science Fuzhou University Fuzhou China Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

Route optimization is a key core technology to optimize network traffic distribution, achieve network load balancing, and improve network performance. Traditional distributed networks widely run shortest-path based routing protocols, and the path of traffic is determined by the link weights of the distributed network, so routing optimization methods are usually optimized around the link weights of the network. Heuristics-based link weight optimization methods are widely used. However, heuristic methods rely on manually set rules, which are poorly generalized and cannot adapt well to dynamically changing traffic demands. Compared to heuristics, deep reinforcement learning (DRL) has the advantage of extracting more accurate feature representations in addition to its ability to handle high-dimensional state and action spaces. We propose a network link weight optimization method based on anti-symmetric deep graph networks (A-DGN) and reinforcement learning using a novel GNN framework anti-symmetric deep graph networks, where link weights are adjusted to reduce the network link utilization with the optimization objective of minimizing the maximum link utilization in the network. Experimental results show that the proposed method achieves significant performance improvements in the link weight optimization problem in four real-world network topology scenarios. © 2024 IEEE.

关键词： Deep reinforcement learning

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Parameter-free Zero-shot 3D Model Classification Based on Multi-view Representation 24

Parameter-free Zero-shot 3D Model Classification Based on Mu...

引用

2024 International Conference on intelligent Perception and Pattern Recognition, IPPR 2024

作者： Xi, Shuting Bai, Jing Yan, Hao School of Computer Science and Engineering North Minzu University China The Key Laboratory of Images Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798400707315

The large-scale pre-trained models have demonstrated excellent performance in processing visual and language tasks in open-world scenarios. However, recent studies have shown that there are limitations in using comparative language-vision pretrained models for zero-shot 3D model classification, mainly in ignoring the multi-view independent information of 3D models, and using only a single template for text prompts, which fails to exploit the rich contextual information provided by the language, and restricts the matching of visual and semantic information. In order to solve these problems, this paper proposes a new framework of Parameter-free zero-shot 3D model classification based on "interaction first, fusion later", which utilizes a pre-trained visual encoder to extract multi-view visual features, and then explores contextual information from multiple perspectives with the help of semantic comprehension of a large language model, establishes fine-grained associations between independent views and prompts through view-by-view interaction with text features, and integrates the independent decision results among views through decision-level fusion strategy to realize zero-shot classification. Without any 3D training, the proposed method achieves 78.8% accuracy on the ZS3D dataset, and 53.2%, 40.3%, and 26.3% accuracy on the three sub-datasets of Ali, which verifies the generalizability and effectiveness of the proposed algorithm. © 2024 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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Fine-grained point cloud classification based on adaptive KNN algorithm 24

Fine-grained point cloud classification based on adaptive KN...

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2024 International Conference on intelligent Perception and Pattern Recognition, IPPR 2024

作者： Liu, Lu Bai, Jing Yan, Hao School of Computer Science and Engineering North Minzu University China The Key Laboratory of images Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798400707315

In point cloud deep learning, local feature aggregation is crucial to improve the performance of the model. The traditional KNN (K-Nearest Neighbors) method performs local feature aggregation by fixing the K value, but often performs poorly in the face of different local structural variations. In order to overcome these limitations, this study proposes an improved KNN method that adapts to the diversity of local regions of the point cloud by adaptively adjusting the neighborhood size in order to maintain feature diversity and accuracy in regions of varying importance. Specifically, in this paper, we dynamically adjust different neighborhood ranges according to the attention weights of individual points, thus ensuring that local features are effectively captured in both important as well as non-important regions. In addition, we conduct experiments on the FG3D and ModelNet40, point cloud datasets. The overall accuracy of AK-Net reaches 96.31%, 79.85%, and 82.38% in the experimental results on the three sub-datasets Airplane, Car, and Chair of FG3D, and 94.04% on ModelNet40. Therefore, the classification performance of AK-Net is superior. © 2024 Copyright held by the owner/author(s).

关键词： Adaptive algorithms

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A Convolutional Knowledge Tracking Model with Forgetting Behavior and Graph Embedding 3

A Convolutional Knowledge Tracking Model with Forgetting Beh...

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3rd International Conference on Electronics and Information Technology, EIT 2024

作者： Huang, Guimin Huang, Zhiqing Guilin University of Electronic Technology School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350369151

With the prevalence of various intelligent educational systems, it is imperative to uncover learners' proficiency in mastering knowledge points through their prior practice circumstances. Knowledge tracking is an extremely beneficial instrument in this regard. Among them, Convolutional Knowledge Tracking (CKT) has demonstrated excellent performance in numerous KT tasks, while it does not take into account the phenomenon of forgetting and the interconnection between knowledge points. In order to tackle these problems, we propose a model called FGE-CKT, which integrates forgetting behavior and graph embedding with CKT. Our model addresses the drawbacks of forgetting characteristics by modelling three key factors affecting students' forgetting behaviors through a fully connected neural net: repeated knowledge learning times, adjacent learning interval and interval between visits to repeat knowledge. At the same time, convolutional neural networks are used to simultaneously extract and incorporate connections between knowledge points into the model's input portion. It was confirmed by experiments on two well-established datasets that FGE-CKT achieved significant improvements in AUC performance. © 2024 IEEE.

关键词： Graph embeddings

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intelligent Test Paper Generation Algorithm Integrating Normal Distribution and Parallel Genetic Algorithm 5

Intelligent Test Paper Generation Algorithm Integrating Norm...

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5th International Conference on intelligent Computing and Human-computer Interaction, ICHCI 2024

作者： Liu, Chuangye Huang, Guimin School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin541004 China

ISBN: (纸本)9798350368284

Genetic algorithms have been widely used in intelligent test paper generation systems. However, traditional genetic algorithms cannot ensure that the difficulty of test questions is normally distributed, and are prone to falling into local optimal solutions. To address the above problems, we proposed an intelligent test paper generation algorithm that combines normal distribution and parallel genetic algorithms. In the population initialization stage, we use the cumulative distribution function (CDF) of the normal distribution to initialize the population. In the subsequent genetic operations, we designed a directed mutation mechanism and added KL divergence as a penalty number in the design of the fitness function to ensure that the difficulty of the test questions is normally distributed. At the same time, in order to speed up the efficiency of the algorithm and escape from the local optimal solution, an adaptive migration strategy was introduced. Experimental results show that the difficulty of the test questions generated by this algorithm is normally distributed, and the algorithm performance is better than other algorithms. © 2024 IEEE.

关键词： Consensus algorithm

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