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Event detection and evolution in multi-lingual social streams

Frontiers of Computer science 2020年第5期14卷 213-227页

作者： Yaopeng Liu Hao Peng Jianxin Li Yangqiu Song Xiong Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang UniversityBeijing100083China State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Department of Computer Science and Engineering HKUSTHong Kong99907China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029China

Real-life events are emerging and evolving in social and news *** methods have succeeded in capturing designed features of monolingual events,but lack of interpretability and multi-lingual *** this end,we propose a multi-lingual event mining model,namely MLEM,to automatically detect events and generate evolution graph in multilingual hybrid-length text streams including English,Chinese,French,German,Russian and ***,we merge the same entities and similar phrases and present multiple similarity measures by incremental word2vec *** propose an 8-tuple to describe event for correlation analysis and evolution graph *** evaluate the MLEM model using a massive human-generated dataset containing real world *** results show that our new model MLEM outperforms the baseline method both in efficiency and effectiveness.

关键词： event detection event evolution stream processing multi-lingual anomaly detection

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MG-DVD: A real-time framework for malware variant detection based on dynamic heterogeneous graph learning

arXiv

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arXiv 2021年

作者： Liu, Chen Li, Bo Zhao, Jun Su, Ming Liu, Xu-Dong School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

Detecting the newly emerging malware variants in real time is crucial for mitigating cyber risks and proactively blocking intrusions. In this paper, we propose MG-DVD, a novel detection framework based on dynamic heterogeneous graph learning, to detect malware variants in real time. Particularly, MG-DVD first models the fine-grained execution event streams of malware variants into dynamic heterogeneous graphs and investigates real-world meta-graphs between malware objects, which can effectively characterize more discriminative malicious evolutionary patterns between malware and their variants. Then, MG-DVD presents two dynamic walk-based heterogeneous graph learning methods to learn more comprehensive representations of malware variants, which significantly reduces the cost of the entire graph retraining. As a result, MG-DVD is equipped with the ability to detect malware variants in real time, and it presents better interpretability by introducing meaningful meta-graphs. Comprehensive experiments on large-scale samples prove that our proposed MG-DVD outperforms state-of-the-art methods in detecting malware variants in terms of effectiveness and efficiency. Copyright © 2021, The Authors. All rights reserved.

关键词： Malware

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Erratum to "Design of an Extreme Low Cutoff Frequency Highpass Frontend for CMOS ISFET via Direct Tunneling Principle"

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IEEE transactions on biomedical circuits and systems 2025年第1期19卷 238页

作者： Jing Liang Yuanqi Hu Hefei Innovation Research Institute Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing and School of Integrated Circuits Science and Engineering Beihang University Beijing China

In [1], in section III.E of the article, we calculate the equivalent tunnelling current according to equation (4) by using the value of Cg, eff as 1.679 fF, which is about 4.6 times smaller than the correct value. This leads to the wrong equivalent impedance value obtained in the final Fig. 10 is about 4.6 times larger than the correct value, and the equivalent impedance should be about 2.2 PΩ at this size, so according to the basis of the above, the article should be corrected as follows.

关键词： Circuits and systems Cutoff frequency Tunneling

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Pointedness of an Image: Measuring How Pointy an Image is Perceived 23rd

Pointedness of an Image: Measuring How Pointy an Image is Pe...

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23rd International Conference on Human-Computer Interaction, HCII 2021

作者： Matsuhira, Chihaya Kastner, Marc A. Ide, Ichiro Kawanishi, Yasutomo Hirayama, Takatsugu Doman, Keisuke Deguchi, Daisuke Murase, Hiroshi Graduate School of Informatics Nagoya University Nagoya Aichi Japan National Institute of Informatics Tokyo Japan Mathmatical and Data Science Center Nagoya University Nagoya Aichi Japan Institute of Innovation for Future Society Nagoya University Nagoya Aichi Japan School of Engineering Chukyo University Toyota Aichi Japan

ISBN: (纸本)9783030786342

For computers to understand human perception, metrics that can capture human perception well are important. However, there are few metrics that characterize the visual perception of humans towards images. Therefore, in this paper, we propose a novel concept and a metric of pointedness of an image, which describes how pointy an image is perceived. The algorithm is inspired by the Features from Accelerated Segment Test (FAST) algorithm for corner detection which looks on the number of continuous neighboring darker pixels surrounding each pixel. We assume that this number would be proportional to the perceived pointedness in the region around the pixel. We evaluated our method towards how well it could capture the human perception of images. To compare the method with similar metrics that describe shapes, we prepared silhouette images of both artificial shapes and natural objects. The results showed that the proposed method gave nearly equivalent perceptual performance to other metrics and also worked in a larger variety of images. © 2021, Springer Nature Switzerland AG.

关键词： Pixels

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Neuromodulated Meta-Learning

arXiv

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arXiv 2024年

作者： Wang, Jingyao Guo, Huijie Qiang, Wenwen Li, Jiangmeng Zheng, Changwen Xiong, Hui Hua, Gang University of Chinese Academy of Sciences Beijing China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Thrust of Artificial Intelligence Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Wormpex AI Research LLC BellevueWA98004 United States

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network structure, not as flexible as BNS. To investigate the role of flexible network structure (FNS) in meta-learning, we conduct extensive empirical and theoretical analyses, finding that model performance is tied to structure, with no universally optimal pattern across tasks. This reveals the crucial role of FNS in meta-learning, ensuring meta-learning to generate the optimal structure for each task, thereby maximizing the performance and learning efficiency of meta-learning. Motivated by this insight, we propose to define, measure, and model FNS in meta-learning. First, we define that an effective FNS should possess frugality, plasticity, and sensitivity. Then, to quantify FNS in practice, we present three measurements for these properties, collectively forming the structure constraint with theoretical supports. Building on this, we finally propose Neuromodulated Meta-Learning (NeuronML) to model FNS in meta-learning. It utilizes bi-level optimization to update both weights and structure with the structure constraint. Extensive theoretical and empirical evaluations demonstrate the effectiveness of NeuronML on various tasks. Code is publicly available at https://***/WangJingyao07/NeuronML. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Meta-Heuristic Search Based Model for Task Offloading and Time Allocation in Mobile Edge Computing 20

Meta-Heuristic Search Based Model for Task Offloading and Ti...

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6th International Conference on Computing and Artificial Intelligence, ICCAI 2020

作者： Xu, Yufan Wang, Yan Yang, Junyao School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big-Data Based Precision Medicine Beihang University Beijing China

ISBN: (纸本)9781450377089

Limited battery lifetime and computing ability of size-constrained wireless devices (WDs) have restricted the performance of many low-power wireless networks, e.g. wireless sensor networks and Internet of Things. To that end, our goal is to make a binary offloading policy, so that each computation task of WDs is either executed locally or fully offloaded to a mobile edge computing (MEC) server and further compute the time allocation among multi-users. Specifically, we propose the order-preserving policy generation method, which computationally feasible and efficient in large-size networks and generate various action policies. Then we introduce the bi-section search, using a one-dimensional bisection search over the dual variable associated with the time allocation constraint in O(N) complexity. Finally, extensive simulations show that the proposed methods can effectively achieve a near-optimal performance under various supposed network setups. © 2020 ACM.

关键词： Mobile edge computing

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Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction

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Precision Clinical Medicine 2024年第2期7卷 132-145页

作者： Zehua Wang Ruichong Lin Yanchun Li Jin Zeng Yongjian Chen Wenhao Ouyang Han Li Xueyan Jia Zijia Lai Yunfang Yu Herui Yao Weifeng Su Guangdong Key Laboratory of Cross-Application of Data Science and Technology Beijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087China Faculty of Innovation Engineering Macao University of Science and TechnologyTaipaMacao 999078China Department of Computer and Information Engineering Guangzhou Huali CollegeGuangzhou 511325China Department of Pathology Sun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China Guangzhou National Laboratory Guangzhou 510005China Dermatology and Venereology Division Department of Medicine SolnaCenter for Molecular MedicineKarolinska InstitutetStockholm 17177Sweden Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation Department of Medical OncologyBreast Tumor CentrePhase I Clinical Trial CentreSun Yat-sen Memorial HospitalSun Yat-sen UniversityGuangzhou 510120China The Second Clinical Medical College Southern Medical UniversityGuangzhou 510515China Faculty of Medicine Macao University of Science and TechnologyTaipaMacao 999078China

Background:The prognosis of breast cancer is often unfavorable,emphasizing the need for early metastasis risk detection and accurate treatment *** study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival(DFS).Methods:We retrospectively collected pathology imaging,molecular and clinical data from The Cancer Genome Atlas and one independent institution in *** developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal(DeepClinMed-PGM)model for DFS prediction,integrating clinicopathological data with molecular *** patients included the training cohort(n=741),internal validation cohort(n=184),and external testing cohort(n=95).Result:Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve(AUC)*** the training cohort,AUC values for 1-,3-,and 5-year DFS predictions increased to 0.979,0.957,and 0.871,while in the external testing cohort,the values reached 0.851,0.878,and 0.938 for 1-,2-,and 3-year DFS predictions,*** DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts,including the training cohort[hazard ratio(HR)0.027,95%confidence interval(CI)0.0016-0.046,P<0.0001],the internal validation cohort(HR 0.117,95%CI 0.041-0.334,P<0.0001),and the external cohort(HR 0.061,95%CI 0.017-0.218,P<0.0001).Additionally,the DeepClinMed-PGM model demonstrated C-index values of 0.925,0.823,and 0.864 within the three cohorts,***:This study introduces an approach to breast cancer prognosis,integrating imaging and molecular and clinical data for enhanced predictive accuracy,offering promise for personalized treatment strategies.

关键词： breast cancer multi-modality deep learning pathological disease-free survival

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CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma:A multicenter study

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Hepatobiliary & Pancreatic Diseases International 2022年第4期21卷 325-333页

作者： Jing-Wei Wei Si-Rui Fu Jie Zhang Dong-Sheng Gu Xiao-Qun Li Xu-Dong Cheng Shuai-Tong Zhang Xiao-Fei He Jian-Feng Yan Li-Gong Lu Jie Tian Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Zhuhai Interventional Medical Center Zhuhai Precision Medical CenterZhuhai People’s HospitalZhuhai Hospital of Jinan UniversityZhuhai 519000China Department of Radiology Zhuhai Precision Medical CenterZhuhai People’s HospitalZhuhai Hospital of Jinan UniversityZhuhai 519000China Department of Interventional Treatment Zhongshan City People’s HospitalZhongshan 528400China Department of Radiology Shenzhen People’s HospitalShenzhen 518000China Interventional Diagnosis and Treatment Department Nanfang HospitalSouthern Medical UniversityGuangzhou510000China Department of Radiology Yangjiang People’s HospitalYangjiang 529500China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Medicine and EngineeringBeihang UniversityBeijing 100191China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’an 710126China

Background:Macrovascular invasion(MaVI)occurs in nearly half of hepatocellular carcinoma(HCC)patients at diagnosis or during follow-up,which causes severe disease deterioration,and limits the possibility of surgical *** study aimed to investigate whether computed tomography(CT)-based radiomics analysis could help predict development of MaVI in ***:A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis ***-based radiomics signature was built via multi-strategy machine learning ***,MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model(CRIM,clinical-radiomics integrated model)via random forest ***-regression analysis was used to select independent risk factors to predict the time of MaVI ***-Meier analysis was conducted to stratify patients according to the time of MaVI development,progression-free survival(PFS),and overall survival(OS)based on the selected risk ***:The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors(P<0.001).CRIM could predict MaVI with satisfactory areas under the curve(AUC)of 0.986 and 0.979 in the training(n=154)and external validation(n=72)datasets,*** presented with excellent generalization with AUC of 0.956,1.000,and 1.000 in each external cohort that accepted disparate CT scanning protocol/***9_fos_InterquartileRange[hazard ratio(HR)=1.98;P<0.001]was selected as the independent risk *** cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development(P<0.001),PFS(P<0.001)and OS(P=0.002).Conclusions:The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

关键词： Hepatocellular carcinoma Macrovascular invasion Radiomics Computed tomography Prognosis

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ColonCLIP: An Adaptable Prompt-Driven Multi-Modal Strategy for Colonoscopy Image Diagnosis

ColonCLIP: An Adaptable Prompt-Driven Multi-Modal Strategy f...

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IEEE International Symposium on Biomedical Imaging

作者： Shuangyi Tan Yinqi Cai Xutao Lin Wei Qi Zhen Li Xiang Wan Guanbin Li The Chinese University of Hong Kong (Shenzhen) Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Department of Gastrointestinal Endoscopy Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases Biomedical Innovation Center The Sixth Affiliated Hospital Sun Yat-Sen University Department of Gastroenterology The Second Hospital of Hebei Medical University

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Colonoscopies serve as a pivotal instrument for colorectal cancer detection and diagnosis. Images in colonoscopy reports typically contain valuable information from the procedure, but manual annotation of these images is laborious and time-consuming due to the high volume of daily procedures. The situation is further complicated when a few images with new image categories reflecting the complex nature of colorectal conditions and the flexibility of annotations occur. Conventional uni-modal lesion classification models fall short in accommodating these new categories, while multi-modal finetuning methods often compromise the integrity of original category knowledge when integrating new ones, leading to subpar mixed-category test performance. In response to these challenges, we present the first comprehensive, open-access colonoscopy image diagnosis dataset OpenColonDB and introduce ColonCLIP. Through the integration of prompts during both training and testing, ColonCLIP effectively learns the features of basic categories and retains knowledge of these features even when a small amount of data with new categories is introduced. This ensures the accuracy of mixed category predictions after adaptation. Our experiments validate the efficacy of our approach, and the GitHub link is https://***/Zoe-TAN/ColonCLIP-OpenColonDB.

关键词： Training Annotations Instruments Colonoscopy Manuals Colorectal cancer Lesions

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Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

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