检索结果-内蒙古大学图书馆

Learning with Partial Labels from Semi-supervised Perspective

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Li, Ximing Jiang, Yuanzhi Li, Changchun Wang, Yiyuan Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University China College of Information Science and Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University China

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate values, can achieve promising performance. Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). Specifically, we first form the pseudo-labeled dataset by selecting a small number of reliable pseudo-labeled instances with high-confidence prediction scores and treating the remaining instances as pseudo-unlabeled ones. Then we design a SS learning objective, consisting of a supervised loss for pseudo-labeled instances and a semantic consistency regularization for pseudo-unlabeled instances. We further introduce a complementary regularization for those non-candidate labels to constrain the model predictions on them to be as small as possible. Empirical results demonstrate that PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels. Code available: https://***/changchunli/PLSP. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

IoT Device Identification via A Bio-Inspired Feature Selection Approach

学校读者我要写书评

暂无评论

IoT Device Identification via A Bio-Inspired Feature Selecti...

IEEE International Conference on Communications (ICC)

作者： Boxiong Wang Hui Kang Geng Sun Jiahui Li College of Software Jilin University Changchun China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China

The rapid development of the Internet-of-Things (IoT) also brings security and other problems. Device identification is a crucial tool for IoT security issues, which can detect and prevent cyber-attacks. Feature selection is an effective data preprocessing technique in IoT device identification, which can improve the performance of classification and reduce computational complexity. In this paper, we propose a novel wrapper feature selection approach based on the improved binary honey badger algorithm (IBHBA) to select features in IoT traffic. Four improved factors are employed in IBHBA to expand the search scope, balance the exploration and exploitation phases, and enhance the search capability. Moreover, a binary mechanism is adopted to make the algorithm more suitable for feature selection in IoT device identification. The experimental results on several real IoT traffic datasets denote that IBHBA outperforms some classical and latest comparison algorithms in the feature selection of IoT device identification.

关键词：

Energy Efficient UAV-assisted Communications via Collaborative Beamforming

学校读者我要写书评

暂无评论

Energy Efficient UAV-assisted Communications via Collaborati...

IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Tingting Zheng Yanheng Liu Geng Sun Mushu Li Conghao Zhou Xuemin Sherman Shen College of Computer Science and Technology Jilin University Changchun China Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China

In this paper, we propose collaborative beamforming (CB) in unmanned aerial vehicle (UAV)-assisted communication networks to improve transmission data rate with minimum energy consumption. Specifically, CB allows a group of UAVs forming a virtual element antenna array (VEAA) and transmitting data collaboratively in a synchronous manner through a high-gain mainlobe (ML) beam. The goal is to optimize the deployment locations of UAVs in the VEAA and excitation current weights for performing CB transmissions considering the energy cost for UAV deployment. Accordingly, we formulate an Energy-Efficient Communication Multi-objective Optimization Problem (EECMOP) to jointly maximize the transmission rate and minimize the maximum sidelobe level (SLL) as well as UAV energy consumption. Then, we propose an Enhanced Multi Objective Ant Lion Optimizer (EMOALO) algorithm which incorporates a chaos theory to develop chaotic initialization and adjustable mode operators for solving the problem. Simulation results demonstrate the effectiveness of the EMOALO algorithm in improving energy efficiency for UAV-assisted communication networks.

关键词：

Semi-supervised multi-label learning with balanced binary angular margin loss 24

学校读者我要写书评

暂无评论

Semi-supervised multi-label learning with balanced binary an...

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Ximing Li Silong Liang Changchun Li Pengfei Wang Fangming Gu College of Computer Science and Technology Jilin University China and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Computer Network Information Center Chinese Academy of Sciences China and University of Chinese Academy of Sciences Chinese Academy of Sciences China

ISBN: (纸本)9798331314385

Semi-supervised multi-label learning (SSMLL) refers to inducing classifiers using a small number of samples with multiple labels and many unlabeled samples. The prevalent solution of SSMLL involves forming pseudo-labels for unlabeled samples and inducing classifiers using both labeled and pseudo-labeled samples in a self-training manner. Unfortunately, with the commonly used binary type of loss and negative sampling, we have empirically found that learning with labeled and pseudo-labeled samples can result in the variance bias problem between the feature distributions of positive and negative samples for each label. To alleviate this problem, we aim to balance the variance bias between positive and negative samples from the perspective of the feature angle distribution for each label. Specifically, we extend the traditional binary angular margin loss to a balanced extension with feature angle distribution transformations under the Gaussian assumption, where the distributions are iteratively updated during classifier training. We also suggest an efficient prototype-based negative sampling method to maintain high-quality negative samples for each label. With this insight, we propose a novel SSMLL method, namely Semi-Supervised Multi-Label Learning with Balanced Binary Angular Margin loss (S2ML2-BBAM). To evaluate the effectiveness of S2ML2-BBAM, we compare it with existing competitors on benchmark datasets. The experimental results validate that S2ML2-BBAM can achieve very competitive performance.

关键词：

Sfmfold: A Multi-Modal Contrast Learning Model for Rna Secondary Structure Prediction

学校读者我要写书评

暂无评论

SSRN

SSRN 2025年

作者： Li, Ang Liu, Yuanning Zhang, Zhaoyang Li, Shuwang Dong, Hao Lv, Meng College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Software Jilin University Changchun130012 China College of Information Engineering Chang’an University Xi’an710064 China

The secondary structure of RNA can regulate the interaction between RNA and protein and perform various biological tasks through specific folding shapes, which plays an important role in revealing biomolecular mechanisms and developing new vaccines and therapeutic drugs. However, existing RNA secondary structure prediction methods based on thermodynamics and traditional neural network models usually lack the ability to model long-distance dependencies of RNA molecules and cannot handle complex non-nested structures in RNA molecules. In this paper, we propose a multi-modal contrastive learning method SFMFold based on state space model, which captures long-distance interactions in RNA sequences through RNA sequence representation learning part, and models the structure of RNA molecules through RNA structure representation learning part. Finally, the features of the two modalities are aligned and deeply fused to obtain the final RNA molecule prediction map. We test SFMFold on several datasets, and the experiments show that our proposed model shows excellent performance in cross-family prediction and long sequence prediction tasks. In addition, SFMFold can also effectively predict non-nested structures such as pseudoknot with better accuracy than the SOTA methods. © 2025, The Authors. All rights reserved.

关键词： Prediction models

Why Misinformation is Created? Detecting them by Integrating Intent Features

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Bing Fu, Bo Li, Ximing Pei, Songwen Li, Changchun Wang, Shengsheng College of Computer Science and Technology Jilin University Changchun Jilin China Liaoning Normal University Liaoning Dalian China University of Shanghai for Science and Technology Shanghai China Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education Jilin University China

Various social media platforms, e.g., Twitter and Reddit, allow people to disseminate a plethora of information more efficiently and conveniently. However, they are inevitably full of misinformation, causing damage to diverse aspects of our daily lives. To reduce the negative impact, timely identification of misinformation, namely Misinformation Detection (MD), has become an active research topic receiving widespread attention. As a complex phenomenon, the veracity of an article is influenced by various aspects. In this paper, we are inspired by the opposition of intents between misinformation and real information. Accordingly, we propose to reason the intent of articles and form the corresponding intent features to promote the veracity discrimination of article features. To achieve this, we build a hierarchy of a set of intents for both misinformation and real information by referring to the existing psychological theories, and we apply it to reason the intent of articles by progressively generating binary answers with an encoder-decoder structure. We form the corresponding intent features and integrate it with the token features to achieve more discriminative article features for MD. Upon these ideas, we suggest a novel MD method, namely Detecting Misinformation by Integrating Intent featuRes (Dm-inter). To evaluate the performance of Dm-inter, we conduct extensive experiments on benchmark MD datasets. The experimental results validate that Dm-inter can outperform the existing baseline MD methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Data mining

Discrepancy-Guided Reconstruction Learning for Image Forgery Detection

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Shi, Zenan Chen, Haipeng Chen, Long Zhang, Dong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of CSE The Hong Kong University of Science and Technology Hong Kong

In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus on the discrepant-specific patterns (e.g., noises, textures, and frequencies), our method has a greater generalization. Specifically, we first propose a Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual patterns. DisGE consists of two branches, where the mainstream backbone branch is used to extract general semantic features, and the accessorial discrepant external attention branch is used to extract explicit forgery cues. Besides, a Double-Head Reconstruction (DouHR) module is proposed to enhance genuine compact visual patterns in different granular spaces. Under DouHR, we further introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine compact visual patterns, such that the forgery detection capability on unknown patterns can be improved. Extensive experimental results on four challenging datasets validate the effectiveness of our proposed method against state-of-the-art competitors. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

END-TO-END ENTITY DETECTION WITH PROPOSER AND REGRESSOR

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wen, Xueru Zhou, Changjiang Tang, Haotian Liang, Luguang Jiang, Yu Qi, Hong College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University China

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of query vectors, which fail to adapt to the rich semantic information in the context, limits these approaches. An end-to-end entity detection approach with proposer and regressor is presented in this paper to tackle the issues. First, the proposer utilizes the feature pyramid network to generate high-quality entity proposals. Then, the regressor refines the proposals for generating the final prediction. The model adopts encoder-only architecture and thus obtains the advantages of the richness of query semantics, high precision of entity localization, and easiness of model training. Moreover, we introduce the novel spatially modulated attention and progressive refinement for further improvement. Extensive experiments demonstrate that our model achieves advanced performance in flat and nested NER, achieving a new state-of-the-art F1 score of 80.74 on the GENIA dataset and 72.38 on the WeiboNER dataset. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

Multi-Task Self-Supervised Learning for Medical Image Segmentation

学校读者我要写书评

暂无评论

Multi-Task Self-Supervised Learning for Medical Image Segmen...

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bo Wang Hang Zhao Xiongfei Li Mingjie Tian Bo Huang Feiyang Yang Intelligent Information Processing Laboratory Yanbian University Yanji China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Although medical image segmentation has achieved remarkable results with supervised learning, obtaining labeled data remains challenging and costly. To counteract this, we present the MTSPSeg, a multi-task self-supervised learning framework. We establish the dynamic gradient learning rate (DGLR) strategy and the dynamic multi-task loss weight adjustment method (DWL) to relieve potential early convergence and diverse learning difficulties of self-supervised learning. Our method deeply integrates the prior knowledge inherent in medical images, facilitates collaborative learning among tasks, and delves further into the latent information within the images. Experimental results demonstrate that regardless of the proportion of labeled data, our method outperforms baseline models on the KiTS19 and LiTS datasets, achieving state-of-the-art performance on the KiTS19 dataset.

关键词：