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17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013

作者： Cao, Peng Zhao, Dazhe Zaïane, Osmar R. Key Laboratory of Medical Image Computing of Ministery of Education Northeastern University China Department of Computing Science University of Alberta Canada

ISBN: (纸本)9783642403187

Learning from imbalanced data is an important and common problem. Many methods have been proposed to address and attempt to solve the problem, including sampling and cost-sensitive learning. This paper presents an effective wrapper approach incorporating the evaluation measure directly into the objective function of cost-sensitive neural network to improve the performance of classification, by simultaneously optimizing the best pair of feature subset, intrinsic structure parameters and misclassification costs. The optimization is based on Particle Swarm Optimization. Our designed method can be applied on the binary class and multi-class classification. Experimental results on various standard benchmark datasets show that the proposed method is effective in comparison with commonly used sampling techniques. © Springer-Verlag 2013.

关键词： Swarm intelligence

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Efficient secure data publishing algorithms for supporting information sharing

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Science in China(Series F) 2009年第4期52卷 627-644页

作者： YANG XiaoChun Key Laboratory of Medical Image Computing (Northeastern University) Ministry of Education Shenyang 110004 China School of Information Science and Engineering Northeastern University Shenyang 110004 China

Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals, such as diseases of patients, the credit rating of a customer, and the salary of an employee. Meanwhile, certain information is required to be published. In this paper, we consider data-publishing applications where the publisher specifies both sensitive information and shared information. An adversary can infer the real value of a sensitive entry with a high confidence by using publishing data. The goal is to protect sensitive information in the presence of data inference using derived association rules on publishing data. We formulate the inference attack framework, and develop complexity results. We show that computing a safe partial table is an NP-hard problem. We classify the general problem into subcases based on the requirements of publishing information, and propose algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data. The test results show that the proposed algorithms can produce approximate maximal published data and improve the performance of existing algorithms.

关键词： Information sharing data publishing data privacy association rule inference attack

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NiuTrans: An open source toolkit for phrase-based and syntax-based machine translation 50

NiuTrans: An open source toolkit for phrase-based and syntax...

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Xiao, Tong Zhu, Jingbo Zhang, Hao Li, Qiang Natural Language Processing Lab Northeastern University Key Laboratory of Medical Image Computing Ministry of Education

ISBN: (纸本)9781937284275

We present a new open source toolkit for phrase-based and syntax-based machine translation. The toolkit supports several state-of-the-art models developed in statistical machine translation, including the phrase-based model, the hierachical phrase-based model, and various syntax-based models. The key innovation provided by the toolkit is that the decoder can work with various grammars and offers different choices of decoding algrithms, such as phrase-based decoding, decoding as parsing/tree-parsing and forest-based decoding. Moreover, several useful utilities were distributed with the toolkit, including a discriminative reordering model, a simple and fast language model, and an implementation of minimum error rate training for weight tuning. © 2012 Association for Computational Linguistics

关键词： Machine translation

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An optimized cost-sensitive SVM for imbalanced data learning

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17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013

作者： Cao, Peng Zhao, Dazhe Zaiane, Osmar Key Laboratory of Medical Image Computing Ministry of Education Northeastern University China University of Alberta Canada

ISBN: (纸本)9783642374555

Class imbalance is one of the challenging problems for machine learning in many real-world applications. Cost-sensitive learning has attracted significant attention in recent years to solve the problem, but it is difficult to determine the precise misclassification costs in practice. There are also other factors that influence the performance of the classification including the input feature subset and the intrinsic parameters of the classifier. This paper presents an effective wrapper framework incorporating the evaluation measure (AUC and G-mean) into the objective function of cost sensitive SVM directly to improve the performance of classification by simultaneously optimizing the best pair of feature subset, intrinsic parameters and misclassification cost parameters. Experimental results on various standard benchmark datasets and real-world data with different ratios of imbalance show that the proposed method is effective in comparison with commonly used sampling techniques. © Springer-Verlag 2013.

关键词： Costs

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TLSTMF-YOLO: Transfer Learning and Feature Fusion Network for Earthquake-Induced Landslide Detection in Remote Sensing images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Meng, Shaoqiang Shi, Zhenming Pirasteh, Saied Liberata Ullo, Silvia Peng, Ming Zhou, Changshi Nunes Goncalves, Wesley Zhang, Limin Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Key Laboratory of Geotechnical and Underground Engineering Ministry of Education Shanghai200092 China Institute of Artificial Intelligence Shaoxing University Zhejiang Shaoxing312000 China Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Geotechnics and Geomatics Tamil Nadu Chennai600 077 India University of Sannio Department of Engineering Benevento82100 Italy Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200092 China Federal University of Mato Grosso do Sul Faculty of Computing Campo Grande79070-900 Brazil The Hong Kong University of Science and Technology Department of Civil and Environmental Engineering Hong Kong Hong Kong

Detecting earthquake-induced landslides in remote sensing images is challenging due to the varying sizes of landslides, uneven distribution, and the prevalence of small targets. This study proposes a novel approach, the TLSTMF-YOLO model, which combines a C3-Swin-Transformer and multiscale feature fusion techniques to enhance detection accuracy and efficiency. key innovations include the use of a convolutional block attention module (CBAM) to improve feature representation, and a bidirectional feature pyramid network (BiFPN) for optimized cross-scale feature fusion. To address data scarcity, a transfer learning strategy is applied, supported by an AdamW optimizer and cosine learning rate strategy for faster convergence. Evaluations on the Jiuzhaigou and Luding landslide datasets demonstrate the model's effectiveness, achieving precision, recall, and mean average precision (mAP)@0.5 of 95.7%, 89.9%, and 90.5% on the Jiuzhaigou dataset, and 96.0%, 90.9%, and 94.5% on the Luding dataset, respectively. In addition, the model processes frames efficiently, with times of 6.61 and 12.2 ms on the two datasets. These results confirm the model's capability for accurate and efficient landslide detection, highlighting its potential for real-world applications. © 1980-2012 IEEE.

关键词： Earthquakes

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Altered spatiotemporal consistency of local neural activities in Parkinson’s disease 3

Altered spatiotemporal consistency of local neural activitie...

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3rd International Conference on Optics and image Processing, ICOIP 2023

作者： Jin, Chaoyang Yang, Lei Qi, Shouliang College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China

ISBN: (纸本)9781510667426

The present study aims to explore the local spatiotemporal consistency of brain connectivity in PD patients. In total, 30 PD patients and 20 health controls (HC) were collected from the Parkinson’s progression markers initiative (PPMI) and their resting state functional MRI images are acquired. FOur-dimensional Consistency of local neural Activities (FOCA) was employed for the first time to explore local brain activity differences between the two groups. To explore the relationship between FOCA values and neuropsychological indices, the Pearson correlation analysis was performed. And receiver operating characteristic (ROC) analysis was carried out to distinguish PD from HC. Compared with HC, PD patients demonstrated decreased FOCA values in the right inferior temporal gyrus (ITG.R), right middle temporal gyrus (MTG.R), right middle occipital gyrus (MOG.R), right superior occipital gyrus, right inferior occipital gyrus, left precentral gyrus (PreCG.L), left postcentral gyrus (PoCG.L) and left rolandic operculum. The FOCA values in MTG.R and ITG.R were positively related to Montreal Cognitive Assessment (MoCA) values. Negative relationships had been found in FOCA values of PreCG.L and MOG.R and Unified Parkinson’s Disease Rating Scale (UPDRS) III scores. What’s more, the ROC analysis showed that the FOCA values of altered brain regions can be used to distinguish PD patients from HC with high performance. PD patients demonstrated disrupted local brain activity in several brain regions. These brain regions are mainly involved in motor, cognitive and emotional functions. The current study provides important insights into understanding the pathophysiological mechanism of PD symptoms. © 2023 SPIE. All rights reserved.

关键词： Magnetic resonance imaging

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An Efficient Quad-Tree Based Index Structure for Cloud Data Management

An Efficient Quad-Tree Based Index Structure for Cloud Data ...

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12th International Conference on Web-Age Information Management

作者： Ding, Linlin Qiao, Baiyou Wang, Guoren Chen, Chen Key Laboratory of Medical Image Computing (NEU) Ministry of Education Canada College of Information Science and Engineering Northeastern University Canada

ISBN: (纸本)9783642235344;9783642235351

Recently, as a new computing infrastructure, cloud computing is getting more and more attention. How to improve the data management of cloud computing is becoming a research hot. Current cloud computing systems only support key-value insert and lookup operations. However, they can not effectively support complex queries and the management of multi-dimensional data due to lack of efficient index structures. Therefore, a scalable and reliable index structure is generally needed. In this paper, a novel quad-tree based multi-dimensional index structure is proposed for efficient data management and query processing in cloud computing systems. A local quad-tree index is built on each compute node to manage the data residing on the node. Then, the compute nodes are organized in a Chord-based overlay network. A portion of local indexes is selected from each compute node as a global index and published based on the overlay routing protocol. The global index with low maintenance cost can dramatically enhance the performance of query processing in cloud computing systems. Experiments show that the proposed index structure is scalable, efficient and reliable.

关键词： Cloud computing

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Automatic Multi-schema Integration Based on User Preference

Automatic Multi-schema Integration Based on User Preference

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11th International Conference on Web-Age Information Management (WAIM 2010)

作者： Ding, Guohui Wang, Guoren Xin, Junchang Geng, Huichao Key Laboratory of Medical Image Computing (NEU) Ministry of Education China College of Information Science and Engineering Northeastern University China

ISBN: (纸本)9783642142451

Schema, integration plays a central role in numerous database applications, such as Deep Web, Data Spaces and Ontology Merging. Although there have been many researches on schema integration, they all neglect user preference which is a very important factor for improving the quality of mediated schemas. In this paper, we propose the automatic multi-schema integration based on user preference. A new concept named reference schema is introduced to represent user preference. This concept can guide the process of integration to generate mediated schemas according to user preference. Different from previous solutions, our approach employs F-measure and "attribute density" to measure the similarity between schemas. Based on this similarity, we design a, top-k ranking algorithm that retrieves k mediate schemas which users really expect. The key component of the algorithm is a pruning strategy which makes use of Divide and Conquer to narrow down the search space of the candidate schemas. Finally, the experimental study demonstrates the effectiveness and good performance of our approach.

关键词： Integration

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering 2nd

scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell ...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wu, Zheyu Wang, Yueyue Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9789819619061

Single-cell RNA sequencing provides high-throughput gene expression information and offers higher resolution of cell differences compared to bulk RNA sequencing, which helps identify cell types and explore cellular heterogeneity at the single-cell level. Although many single-cell clustering methods based on masked autoencoders have been developed recently, they rarely effectively utilize the correlations between cells, resulting in suboptimal clustering results. In this work, we propose a novel clustering method for scRNA-seq data based on a masked graph convolutional autoencoder, which incorporates a graph convolutional network onto the basis of a masked autoencoder to enhance the capture of the correlation between cells and genes by fusing the cell-cell interaction graph and the gene expression matrix. By increasing the relationships between cells, dimensionality reduction is guided, and a clustering-friendly representation is obtained. We conducted extensive comparative experiments using various clustering evaluation metrics on six scRNA-seq datasets from different sequencing platforms. The experimental results demonstrate that scMGCAE outperforms other methods on these datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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Tissue P systems with cell separation: attacking the partition problem

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Science China(Information Sciences) 2011年第2期54卷 293-304页

作者： ZHANG XingYi1, WANG Shuo2, NIU YunYun2 & PAN LinQiang2 1key Lab of intelligent computing and Signal Processing of Ministry of Education, School of Computer Science and Technology, Anhui University, Hefei 230039, China 2image Processing and intelligent Control key laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China 1. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China2. Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cell separation is introduced into tissue P systems, which enables systems to generate an exponential workspace in a polynomial time. In this work, the computational power of tissue P systems with cell separation is investigated. Specifically, a uniform family of tissue P systems with cell separation is constructed for effciently solving a well-known NP-complete problem, the partition problem.

关键词： membrane computing tissue P system cell separation partition problem

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