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IEEE Annual International Symposium Virtual Reality

作者： Yunfei Lai Minghui Sun Zhuofeng Li 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

Gaze input is a popular hands-free input method that allows for intuitive and rapid pointing but lacks a confirmation mechanism. This study introduces GazePuffer, an interaction method that combines puffing cheeks with gaze. We explored the design space of mouth gestures, proposed a set of candidate gestures, filtered them through user subjective evaluation, and selected five basic gestures and four variations. We determined the corresponding virtual reality (VR) actions for these gestures through brainstorming. We achieved an accuracy of 93.8% in recognizing the five basic mouth gestures using the built-in sensors of the head-mounted display devices. We compared GazePuffer with two baseline methods in target selection tasks, demonstrating that GazePuffer is on par with Gaze&Pinch in throughput and speed, slightly outperforming Gaze&Dwell. Finally, we showcased the applicability of GazePuffer in real VR interaction tasks, with users generally finding it usable and effortless.

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Medical Image Segmentation using PCNN based on Multi-feature Grey Wolf Optimizer Bionic Algorithm

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Journal of Bionic engineering 2021年第3期18卷 711-720页

作者： Xue Wang Zhanshan Li Heng Kang Yongping Huang Di Gai College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun 130012China ZICT Technology Co. Ltd.Shenzhen 518063China

Medical image segmentation is a challenging task especially in multimodality medical image *** this paper,an improved pulse coupled neural network based on multiple hybrid features grey wolf optimizer(MFGWO-PCNN)is proposed for multimodality medical image ***,a two-stage medical image segmentation method based on bionic algorithm is presented,including image fusion and image *** image fusion stage fuses rich information from different modalities by utilizing a multimodality medical image fusion model based on maximum energy *** the stage of image segmentation,an improved PCNN model based on MFGWO is proposed,which can adaptively set the parameters of PCNN according to the features of the *** modalities of FLAIR and TIC brain MRIs are applied to verify the effectiveness of the proposed MFGWO-PCNN *** experimental results demonstrate that the proposed method outperforms the other seven algorithms in subjective vision and objective evaluation indicators.

关键词： grey wolf optimizer pulse coupled neural network bionic algorithm medical image segmentation

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Label-aware Multi-level Contrastive Learning for Cross-lingual Spoken Language Understanding

Label-aware Multi-level Contrastive Learning for Cross-lingu...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Liang, Shining Shou, Linjun Pei, Jian Gong, Ming Zuo, Wanli Zuo, Xianglin Jiang, Daxin College of Computer Science and Technology Jilin University China Key laboratory of Symbolic Computation and Knowledge Engineering MOE STCA Microsoft Department of Electrical & Computer Engineering Duke University United States

Despite the great success of spoken language understanding (SLU) in high-resource languages, it remains challenging in low-resource languages mainly due to the lack of labeled training data. The recent multilingual code-switching approach achieves better alignments of model representations across languages by constructing a mixed-language context in zero-shot cross-lingual SLU. However, current code-switching methods are limited to implicit alignment and disregard the inherent semantic structure in SLU, i.e., the hierarchical inclusion of utterances, slots, and words. In this paper, we propose to model the utterance-slot-word structure by a multi-level contrastive learning framework at the utterance, slot, and word levels to facilitate explicit alignment. Novel code-switching schemes are introduced to generate hard negative examples for our contrastive learning framework. Furthermore, we develop a label-aware joint model leveraging label semantics to enhance the implicit alignment and feed to contrastive learning. Our experimental results show that our proposed methods significantly improve the performance compared with the strong baselines on two zero-shot cross-lingual SLU benchmark datasets. © 2022 Association for computational Linguistics.

关键词： Semantics

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Simple-Sampling and Hard-Mixup with Prototypes to Rebalance Contrastive Learning for Text Classification

arXiv

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arXiv 2024年

作者： Li, Mengyu Liu, Yonghao Giunchiglia, Fausto Feng, Xiaoyue Guan, Renchu Key Laboratory of Symbolic Computation and Knowledge Engineering The Ministry of Education College of Computer Science and Technology Jilin University China University of Trento Italy

Text classification is a crucial and fundamental task in natural language processing. Compared with the previous learning paradigm of pre-training and fine-tuning by cross entropy loss, the recently proposed supervised contrastive learning approach has received tremendous attention due to its powerful feature learning capability and robustness. Although several studies have incorporated this technique for text classification, some limitations remain. First, many text datasets are imbalanced, and the learning mechanism of supervised contrastive learning is sensitive to data imbalance, which may harm the model performance. Moreover, these models leverage separate classification branch with cross entropy and supervised contrastive learning branch without explicit mutual guidance. To this end, we propose a novel model named SharpReCL for imbalanced text classification tasks. First, we obtain the prototype vector of each class in the balanced classification branch to act as a representation of each class. Then, by further explicitly leveraging the prototype vectors, we construct a proper and sufficient target sample set with the same size for each class to perform the supervised contrastive learning procedure. The empirical results show the effectiveness of our model, which even outperforms popular large language models across several datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Enabling Generalized Zero-Shot Vulnerability Classification

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Hu, Jinghao Guo, Jinsong Luo, Chen Hu, Yang Lanzinger, Matthias Li, Zhanshan Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Software Changchun130012 China University College London Department of Computer Science United Kingdom Amazon United States United Kingdom University of Oxford Department of Computer Science United Kingdom Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education College of Computer Science and Technology Changchun130012 China

Regarding computer security, the growth of code vulnerability types presents a persistent challenge. These vulnerabilities, which may cause severe consequences, necessitate precise classification for effective mitigation. However, the rapid emergence of new vulnerability types complicates the classification process. Traditional methodologies, which often involve human expertise and the manual labeling or generation of example instances, are not only resource-intensive but also struggle to adapt to the dynamic nature of these vulnerabilities. This paper introduces VulnSense, an innovative method that harnesses the capabilities of Generalized Zero-Shot Learning (GZSL) to address the vulnerability classification problem. VulnSense learns about unseen vulnerability classes from the descriptions of these unseen classes, while not requiring to see any instances of these unseen classes. Specifically, VulnSense learns from three main resources: (1) seen classes with labeled code instances;(2) descriptions of these seen classes;and (3) descriptions of "unseen"classes which have no labeled instances. Our experiments underscore VulnSense's superiority over existing GZSL methods in classifying instances of unseen classes. Concurrently, it maintains a performance parity with traditional labeled-example based learning methods in classifying instances of seen vulnerabilities. VulnSense demonstrates the potential of using GZSL for vulnerability classification, while also highlighting challenges that inspire future work. © 2004-2012 IEEE.

关键词： Zero-shot learning

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A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features

A Method for Extracting Electronic Medical Record Entities b...

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作者： Hongyan Xu Hong Wang Yong Feng Rongbing Wang Yonggang Zhang College of Information Liaoning University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

With the implementation of the "Internet+" strategy, electronic medical records are generally applied in the medical field. Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients' states, but the existing methods for extracting entities from electronic medical records have problems of redundant information, overlapping entities, and low accuracy rates. Therefore, this paper proposes an entity extraction method for electronic medical records based on the network framework of BERT-Bi LSTM, which incorporates a multichannel self-attention mechanism and location relationship features. First, the text input sequence was encoded using the BERT-BiLSTM network framework, and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mechanism. Then, the position relation characteristic was used to extract the local semantic message of the text, and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained. Next, the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix. Finally,the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus, and the experimental results indicate that the proposed method surpasses existing methods in terms of precision, recall, F1 value and training time.

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KSSANet: KAN-Driven Spatial-Spectral Attention Networks for Hyperspectral Image Super-Resolution

KSSANet: KAN-Driven Spatial-Spectral Attention Networks for ...

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

作者： Baisong Li Xingwang Wang Haixiao Xu College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Krowledge Engineering of Ministry of Education Jilin University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the limitations of physical imaging, acquiring high-resolution hyperspectral images (HR-HSIs) has always been a significant challenge. Single hyperspectral image super-resolution (SHSR) technology aims to generate corresponding HR-HSIs by processing low-resolution hyperspectral images (LR-HSIs). Compared to multi-source data fusion methods, SHSR relies solely on a single low-resolution image and does not require additional auxiliary information or multimodal data, making it more flexible and efficient in data acquisition. Recently, Kolmogorov–Arnold Networks (KAN), which derive from the Kolmogorov–Arnold representation theorem, show great potential in modeling long-range dependencies. In this paper, we further investigate the potential of KAN for hyperspectral image restoration. Specifically, we propose a spatial-spectral attention block (SSAB) module, which includes a KAN-based spatial attention module (KAN-SpaAB) and a KAN-based spectral attention module (KAN-SpeAB), designed for the restoration of spatial and spectral information, respectively. Experimental results demonstrate that KSSANet outperforms existing methods in both quantitative evaluation and image generation quality, achieving state-of-the-art (SOTA) performance. Our code is available at: https://***/Baisonm-Li/KSSANet.

关键词： Measurement Visualization Image synthesis Superresolution Imaging Image restoration Speech processing Spatial resolution Signal resolution Hyperspectral imaging

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A Novel Drug-Drug Interaction Prediction Model Based on Line Subgraph Generation Strategy

A Novel Drug-Drug Interaction Prediction Model Based on Line...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Bai, Tian Li, Chu Peng, Xinyue Guan, Haotian Zhang, Zefan Wang, Guishen Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China Jilin University College of Software Changchun China Jilin University School of Nursing Changchun China Jilin University School of Life Sciences Changchun China Changchun University of Technology College of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

Drug-Drug Interaction (DDI) prediction task is helpful for better-understanding drugs. In this paper, we propose a novel drug-drug interaction prediction model based on line subgraph generation strategy, named DDI-LSG model. Our DDI-LSG model consists of three main parts which include drug relation graph construction, line subgraph generation strategy, and graph-level classification. To consider more relationships among drugs, we propose a node feature-enhancing method to encode drug features in drug relation graph construction process. To consider drugs and DDI as equivalent factors of our DDI-LSG model, we introduce line graph transformation to integrate DDI with drug feature enhancing vector. Combining Jaccard similarity with cosine similarity, we propose a line subgraph generation strategy to evaluate node relation and extract key structures around target DDI in the line graph. Then, we reformulate the DDI prediction task into a graph-level classification task for the line subgraph of the target DDI. Therefore, in the final part of our DDI-LSG model, we use a graph-level classifier to classify the line subgraphs. Our DDI-LSG model outperforms better experiment results than baselines. Ablation results have validated the node feature enhancing method and line subgraph generation strategy. © 2023 IEEE.

关键词： drug-drug interaction prediction graph-level classification line graph line subgraph strategy node feature enhancing method

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SSRMamba: Efficient Visual State Space Model for Spectral Super-Resolution

SSRMamba: Efficient Visual State Space Model for Spectral Su...

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

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computational cost of Transformer structures, existing deep learning (DL)-based methods struggle to balance spectral reconstruction quality and computational efficiency. Recently, Mamba methods base on state-space models (SSM) show great potential in modeling long-range dependencies with linear complexity. Therefore, we introduce the Mamba model into spectral super-resolution (SSR) task. Specifically, we propose a three-stage SSR network base on Mamba, called SSRMamba. We design SpaMamba, SSMamba, and SpeMamba modules for shallow spatial information extraction, mixed information encoding, and spectral information reconstruction, respectively. Extensive experimental results demonstrate that SSRMamba not only surpasses existing methods in terms of quantification and quality, achieving state-of-the-art (SOTA) performance, but also significantly reduces model size and computational cost. The source code of SSRMamba is available at: https://***/Baisonm-Li/SSRMamba.

关键词： Visualization computational modeling Superresolution Information retrieval Transformers computational efficiency State-space methods Speech processing Image reconstruction Hyperspectral imaging

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Multi-Target Track Initiation in Heavy Clutter

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Computers, Materials & Continua 2022年第9期72卷 4489-4507页

作者： Li Xu Ruzhen Lou Chuanbin Zhang Bo Lang Weiyue Ding College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin UniversityChangchun130012China Norinco Group Air Ammunition Research Institute Co.Ltd. Harbin150036China Harvard Medical School Boston02115USA

In the heavy clutter environment, the information capacity is large,the relationships among information are complicated, and track initiationoften has a high false alarm rate or missing alarm rate. Obviously, it is adifficult task to get a high-quality track initiation in the limited measurementcycles. This paper studies the multi-target track initiation in heavy *** first, a relaxed logic-based clutter filter algorithm is presented. In thealgorithm, the raw measurement is filtered by using the relaxed logic *** not only design a kind of incremental and adaptive filtering gate, but alsoadd the angle extrapolation based on polynomial extrapolation. The algorithm eliminates most of the clutter and obtains the environment with highdetection rate and less clutter. Then, we propose a fuzzy sequential Houghtransform-based track initiation algorithm. The algorithm establishes a newmeshing rule according to system noise to balance the relationship between thegrid granularity and the track initiation quality. And a flexible superpositionmatrix based on fuzzy clustering is constructed, which avoids the transformation error caused by 0–1 voting method in traditional Hough *** addition, the algorithm allows the superposition matrixes of nonadjacentcycles to be associated to overcome the shortcoming that the track can’t beinitiated in time when the measurements appear in an intermittent way. Anda slope verification method is introduced to detect formation-intensive serialtracks. Last, the sliding window method is employed to feedback the trackinitiation results timely and confirm the track. Simulation results verify thatthe proposed algorithms can initiate the tracks accurately in heavy clutter.

关键词： Track initiation heavy clutter multi-target Hough transform

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