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multi-modal Military Event Extraction Based on Knowledge Fusion

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Computers, Materials & Continua 2023年第10期77卷 97-114页

作者： Yuyuan Xiang Yangli Jia Xiangliang Zhang Zhenling Zhang School of Computer Science Liaocheng UniversityLiaocheng252059China

Event extraction stands as a significant endeavor within the realm of information extraction,aspiring to automatically extract structured event information from vast volumes of unstructured *** event elements from multi-modal data remains a challenging task due to the presence of a large number of images and overlapping event elements in the *** researchers have proposed various methods to accomplish this task,most existing event extraction models cannot address these challenges because they are only applicable to text *** solve the above issues,this paper proposes a multi-modal event extraction method based on knowledge ***,for event-type recognition,we use a meticulous pipeline approach that integrates multiple pre-trained *** approach enables a more comprehensive capture of the multidimensional event semantic features present in military texts,thereby enhancing the interconnectedness of information between trigger words and *** event element extraction,we propose a method for constructing a priori templates that combine event types with corresponding trigger *** approach facilitates the acquisition of fine-grained input samples containing event trigger words,thus enabling the model to understand the semantic relationships between elements in greater ***,a fusion method for spatial mapping of textual event elements and image elements is proposed to reduce the category number overload and effectively achieve multi-modal knowledge *** experimental results based on the CCKS 2022 dataset show that our method has achieved competitive results,with a comprehensive evaluation value F1-score of 53.4%for the *** results validate the effectiveness of our method in extracting event elements from multi-modal data.

关键词： Event extraction multi-modal knowledge fusion pre-trained models

multi-modal Correlated Network for emotion recognition in speech

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Visual Informatics 2019年第3期3卷 150-155页

作者： Minjie Ren Weizhi Nie Anan Liu Yuting Su The School of Electrical and Information Engineering Tianjin UniversityChina

With the growing demand of automatic emotion recognition system,emotion recognition is becoming more and more crucial for human-computer interaction(HCI)***,there is a continuous improvement in the performance of automatic emotion recognition due to the development of both hardware and deep learning ***,because of the abstract concept and multiple expressions of emotion,automatic emotion recognition is still a challenging *** this paper,we propose a novel multi-modal Correlated Network for emotion recognition aiming at exploiting the information from both audio and visual channels to achieve more robust and accurate *** the proposed method,the audio signals and visual signals are first preprocessed for the feature *** preprocessing,we obtain the Mel-spectrograms,which can be treated as images,and the representative frames from visual *** the Mel-spectrograms are fed to the convolutional neural network(CNN)to get the audio features and the representative frames are fed to the CNN and LSTM to get ***,we employ the triplet loss to increase the differentiation of ***,we propose a novel correlated loss to reduce the differentiation of ***,we apply the feature fusion method to fuse the audio and visual feature for emotion recognition *** experimental result on AEFW dataset demonstrates the correlation information of multiple modals is crucial for automatic emotion recognition and the proposed method can achieve the state-of-the-art performance on the classification task.

关键词： multi-modal Emotion recognition Neural networks

Automated brain tumor segmentation on multi-modal MR image using SegNet

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Computational Visual Media 2019年第2期5卷 209-219页

作者： Salma Alqazzaz Xianfang Sun Xin Yang Len Nokes School of Engineering Cardiff UniversityCardiffCF24 3AAUK Department of Physics College of Science for WomenBaghdad UniversityBaghdadIraq School of Computer Science and Informatics Cardiff UniversityCardiffCF24 3AAUK

The potential of improving disease detection and treatment planning comes with accurate and fully automatic algorithms for brain tumor ***, a type of brain tumor, can appear at different locations with different shapes and sizes. Manual segmentation of brain tumor regions is not only timeconsuming but also prone to human error, and its performance depends on pathologists’ experience. In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3 D data sets for four MRI modalities(Flair, T1, T1 ce, and T2)for automated segmentation of brain tumor and subtumor parts, including necrosis, edema, and enhancing tumor. To further improve tumor segmentation, the four separately trained SegNet models are integrated by post-processing to produce four maximum feature maps by fusing the machine-learned feature maps from the fully convolutional layers of each trained model. The maximum feature maps and the pixel intensity values of the original MRI modalities are combined to encode interesting information into a feature *** the combined feature as input, a decision tree(DT) is used to classify the MRI voxels into different tumor parts and healthy brain tissue. Evaluating the proposed algorithm on the dataset provided by the Brain Tumor Segmentation 2017(BraTS 2017)challenge, we achieved F-measure scores of 0.85, 0.81,and 0.79 for whole tumor, tumor core, and enhancing tumor, *** results demonstrate that using SegNet models with 3 D MRI datasets and integrating the four maximum feature maps with pixel intensity values of the original MRI modalities has potential to perform well on brain tumor segmentation.

关键词： brain tumor segmentation multi-modal MRI convolutional neural networks fully convolutional networks decision tree

Joint multi-modal Parcellation of the Human Striatum:Functions and Clinical Relevance

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Neuroscience Bulletin 2020年第10期36卷 1123-1136页

作者： Xiaojin Liu Simon B.Eickhoff Felix Hoffstaedter Sarah Genon Svenja Caspers Kathrin Reetz Imis Dogan Claudia R.Eickhoff Ji Chen Julian Caspers Niels Reuter Christian Mathys Andre Aleman Renaud Jardri Valentin Riedl Iris E.Sommer Kaustubh R.Patil Institute of Neuroscience and Medicine(INM-7 Brain and Behaviour)Research Centre JiilichJiilichGermany Institute of Systems Neuroscience Heinrich Heine University DusseldorfDusseldorfGermany Institute of Neuroscience and Medicine(INM-1) Research Centre Jiilich52428 JiilichGermany Institute for Anatomy I Medical FacultyHeinrich Heine University Dusseldorf40225 DusseldorfGermany Jiilich Aachen Research Alliance-BRAIN(JARA)Institute of Molecular Neuroscience and Neuroimaging Forschungszentrum JiilichRheinisch Westfallische Technische Hochschule(RWTH)Aachen University52074 AachenGermany Department of Neurology Rheinisch Westfallische Technische Hochschule(RWTH)Aachen University52074 AachenGermany Institute of Clinical Neuroscience and Medical Psychology Medical FacultyUniversity of Dusseldorf40225 DusseldorfGermany Department of Diagnostic and Interventional Radiology Medical FacultyUniversity of Dusseldorf40225 DusseldorfGermany Institute of Radiology and Neuroradiology Evangelisches KrankenhausUniversity of Oldenburg26129 OldenburgGermany Department of Neuroscience University Medical Center GroningenUniversity of Groningen9713 AV GroningenThe Netherlands SCALab(CNRS UMR9193)&CHU de LiUe Hopital FontanPole de Psychiatrie(CURE)Universite de Lille59037 LilleFrance Departments of Neuroradiology Nuclear Medicine and Neuroimaging CenterTechnische Universitat Miinchen80333 MunichGermany

The human striatum is essential for both lowand high-level functions and has been implicated in the pathophysiology of various prevalent disorders,including Parkinson's disease(PD)and schizophrenia(SCZ).It is known to consist of structurally and functionally divergent ***,previous parcellations are based on a single neuroimaging modality,leaving the extent of the multi-modal organization of the striatum ***,we investigated the organization of the striatum across three modalities—resting-state functional connectivity,probabilistic diffusion tractography,and structural covariance—to provide a holistic convergent view of its structure and *** found convergent clusters in the dorsal,dorsolateral,rostral,ventral,and caudal *** characterization revealed the anterior striatum to be mainly associated with cognitive and emotional functions,while the caudal striatum was related to action ***,significant structural atrophy in the rostral and ventral striatum was common to both PD and SCZ,but atrophy in the dorsolateral striatum was specifically attributable to *** study revealed a cross-modal convergent organization of the striatum,representing a fundamental topographical model that can be useful for investigating structural and functional variability in aging and in clinical conditions.

关键词： Striatum multi-modal Connectivity-based parcellation Convergent clusters Voxel-based morphometry Parkinson's disease Schizophrenia

A Hand Features Based Fusion Recognition Network with Enhancing multi-modal Correlation

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Computer Modeling in Engineering & Sciences 2024年第7期140卷 537-555页

作者： Wei Wu Yuan Zhang Yunpeng Li Chuanyang Li YanHao School of Information Engineering Shenyang UniversityShenyang110044China

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing ***,it leverages inter-modal correlation to enhance recognition ***,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal ***,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct ***,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition *** address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level *** information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel *** the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal *** separable convolution markedly reduces the training parameters and further enhances the feature *** evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of *** Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,*** comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical *** experiments in this article utilized amodest sample database comprising 200 *** subsequent phase involves preparin

关键词： Biometrics multi-modal correlation deep learning feature-level fusion

Cryptomining Malware Detection Based on Edge Computing-Oriented multi-modal Features Deep Learning

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China Communications 2022年第2期19卷 174-185页

作者： Wenjuan Lian Guoqing Nie Yanyan Kang Bin Jia Yang Zhang College of Computer Science&Engineering Shandong University of Science and TechnologyQingdaoShandong 266590China College of Foreign Languages Shandong University of Science and TechnologyQingdaoShandong 266590China

In recent years,with the increase in the price of cryptocurrencies,the number of malicious cryptomining software has increased *** their powerful spreading ability,cryptomining malware can unknowingly occupy our resources,harm our interests,and damage more legitimate ***,although current traditional rule-based malware detection methods have a low false alarm rate,they have a relatively low detection rate when faced with a large volume of emerging *** though common machine learning-based or deep learning-based methods have certain ability to learn and detect unknown malware,the characteristics they learn are single and independent,and cannot be learned *** at the above problems,we propose a deep learning model with multi-input of multi-modal features,which can simultaneously accept digital features and image features on different *** model in turn includes parallel learning of three sub-models and ensemble learning of another specific *** four sub-models can be processed in parallel on different devices and can be further applied to edge computing *** model can adaptively learn multi-modal features and output prediction *** detection rate of our model is as high as 97.01%and the false alarm rate is only 0.63%.The experimental results prove the advantage and effectiveness of the proposed method.

关键词： cryptomining malware multi-modal ensemble learning deep learning edge computing

MMInstruct: a high-quality multi-modal instruction tuning dataset with extensive diversity

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Science China(Information Sciences) 2024年第12期67卷 36-51页

作者： Yangzhou LIU Yue CAO Zhangwei GAO Weiyun WANG Zhe CHEN Wenhai WANG Hao TIAN Lewei LU Xizhou ZHU Tong LU Yu QIAO Jifeng DAI School of Computer Science Nanjing University School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai AI Laboratory School of Computer Science Fudan University Department of Information Engineering The Chinese University of Hong Kong SenseTime Research Department of Electronic Engineering Tsinghua University

Despite the effectiveness of vision-language supervised fine-tuning in enhancing the performance of vision large language models(VLLMs), existing visual instruction tuning datasets include the following limitations.(1) Instruction annotation quality: despite existing VLLMs exhibiting strong performance,instructions generated by those advanced VLLMs may still suffer from inaccuracies, such as hallucinations.(2) Instructions and image diversity: the limited range of instruction types and the lack of diversity in image data may impact the model's ability to generate diversified and closer to real-world scenarios outputs. To address these challenges, we construct a high-quality, diverse visual instruction tuning dataset MMInstruct,which consists of 973k instructions from 24 domains. There are four instruction types: judgment, multiplechoice, long visual question answering, and short visual question answering. To construct MMInstruct, we propose an instruction generation data engine that leverages GPT-4V, GPT-3.5, and manual correction. Our instruction generation engine enables semi-automatic, low-cost, and multi-domain instruction generation at 1/6 the cost of manual construction. Through extensive experiment validation and ablation experiments,we demonstrate that MMInstruct could significantly improve the performance of VLLMs, e.g., the model fine-tuning on MMInstruct achieves new state-of-the-art performance on 10 out of 12 benchmarks. The code and data shall be available at https://***/yuecao0119/MMInstruct.

关键词： instruction tuning multi-modal multi-domain dataset vision large language model

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Research on unified recognition model and algorithm for multi-modal gestures

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The Journal of China Universities of Posts and Telecommunications 2019年第2期26卷 30-42页

作者： Guo Xiaopei Feng Zhiquan Sun Kaiyun Liu Hong Xie Wei Bi Jianping School of Information Science and Engineering University of JinanJinan 250022China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan 250022China School of Information Science and Engineering Shandong Normal UniversityJinan 250014China School of Information and Electrical Engineering Harbin Institute of Technology at WeihaiWeihai 264209China Affiliated Hospital of Shandong University of Traditional Chinese Medicine Jinan 250022China

In gesture recognition,static gestures,dynamic gestures and trajectory gestures are collectively known as multi-modal *** solve the existing problem in different recognition methods for different modal gestures,a unified recognition algorithm is *** angle change data of the finger joints and the movement of the centroid of the hand were acquired respectively by data glove and *** the preprocessing of the multi-source heterogeneous data,all hand gestures were considered as curves while solving hand shaking,and a uniform hand gesture recognition algorithm was established to calculate the Pearson correlation coefficient between hand gestures for gesture *** this way,complex gesture recognition was transformed into the problem of a simple comparison of curves *** main innovations:1) Aiming at solving the problem of multi-modal gesture recognition,an unified recognition model and a new algorithm is proposed;2) The Pearson correlation coefficient for the first time to construct the gesture similarity operator is *** testing 50 kinds of gestures,the experimental results showed that the method presented could cope with intricate gesture interaction with the 97.7% recognition rate.

关键词： Kinect data glove multi-modal gesture gesture interaction

^(236)U multi-modal Fission Paths on a Five-dimensional Deformation Surface

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Communications in Theoretical Physics 2019年第4期71卷 417-420页

作者： Zhi-Ming Wang Wen-Jie Zhu Xin Zhu Chun-Lai Zhong Tie-Shuan Fan State Key Laboratory of Nuclear Physics and Technology Institute for Heavy Ion Physics School of PhysicsPeking University

In this paper, we develop a new path search algorithm which considers all the degrees of freedom and apply it on our calculated five-dimensional potential energy surface(PES) of^(236) U. Asymmetric and symmetric fissi... 详细信息

关键词： 236U multi-modal fission macroscopic-microscopic model