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Outlier Detection over Sliding Windows for Probabilistic Data Streams

Journal of Computer Science & Technology 2010年第3期25卷 389-400页

作者：王斌杨晓春王国仁于戈 School of Information Science and Engineering Northeastern University(Shenyang) Key Laboratory of Medical Image Computing (Northeastern University) Ministry of Education

Outlier detection is a very useful technique in many applications, where data is generally uncertain and could be described using probability. While having been studied intensively in the field of deterministic data, outlier detection is still novel in the emerging uncertain data field. In this paper, we study the semantic of outlier detection on probabilistic data stream and present a new definition of distance-based outlier over sliding window. We then show the problem of detecting an outlier over a set of possible world instances is equivalent to the problem of finding the k-th element in its neighborhood. Based on this observation, a dynamic programming algorithm （DPA） is proposed to reduce the detection cost from 0（2IR（~＇d）l） to O（Ik.R（e, d）l）, where R（e, d） is the d-neighborhood of e. Furthermore, we propose a pruning-based approach （PBA） to effectively and efficiently filter non-outliers on single window, and dynamically detect recent m elements incrementally. Finally, detailed analysis and thorough experimental results demonstrate the efficiency and scalability of our approach.

关键词： outlier detection uncertain data probabilistic data stream sliding window

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Edge-Enhanced Cascaded MRF for SAR image Segmentation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Liu, Mengmeng Shang, Ronghua Liu, Kang Feng, Jie Wang, Chao Xu, Songhua Li, Yangyang Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Shaanxi Xi’an710071 China Xidian University School of Computer Science and Technology Shaanxi Xi’an710071 China Research Center for Data Hub and Security Zhejiang Lab Hangzhou311100 China The Second Affiliated Hospital of Xi’an Jiaotong University Department of Health Management Institute of Medical Artificial Intelligence Xi’an710004 China

Markov random fields (MRFs) effectively capture local contextual information by modeling the spatial dependencies between pixels, which helps highlight details and enhances segmentation smoothness. To fully exploit MRF for synthetic aperture radar (SAR) image segmentation, we propose a novel edge-enhanced cascaded MRF (ECMRF) approach. Specifically, we introduce multiple edge-constrained filters to emphasize SAR image boundaries and provide relatively clean features. Building on this, we present a cascaded MRF framework that sequentially integrates region-level and pixel-level segmentations with feature perturbation and fusion to generate the final segmentation output. The framework comprises four key components: 1) a region-level MRF (RMRF), regulated by edge features, to achieve precise region segmentation;2) a pixel-level MRF (PMRF) with selective label smoothing to refine edges and reduce noise clusters;3) equal-channel feature perturbation (EC-FP) to increase feature diversity;and 4) a random probability-based feature fusion (RP-FF) scheme to merge the input features. The experimental results demonstrate that our ECMRF outperforms six state-of-the-art comparable methods, underscoring its competitive performance. © 1980-2012 IEEE.

关键词： image segmentation

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Reliable and Fair Trustworthiness Evaluation Protocol for Platoon Service Recommendation System

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Chinese Journal of Electronics 2025年第2期34卷 563-575页

作者： Cheng, Hongyuan Liu, Yining Zhou, Fei Tan, Zhiyuan Zhang, Xianchao School of Computer Science & Engineering Linyi University Linyi273300 China School of Data Science and Artificial Intelligence Wenzhou University of Electronic Technology Wenzhou325027 China Guangxi Key Laboratory of Digital Infrastructure Nanning530023 China School of Computing Engineering & the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom College of Information Science and Engineering Jiaxing University Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China

Aiming at the problems of the communication inefficiency and high energy consumption in vehicular networks, the platoon service recommendation systems (PSRS) are presented. Many schemes for evaluating the reputation of platoon head vehicles have been proposed to obtain and recommend reliable platoon head vehicles. These trustworthiness evaluation protocols for PSRS fail to achieve both reliability and fairness. We first provide a reliable trustworthiness evaluation method to ensure that the reputation level of platoon head vehicle can be calculated by cloud service provider (CSP) with the help of key agreement mechanism and truth discovery technology. The semi-trusted entity CSP may maliciously tamper with the reputation level of the platoon head vehicle. We also provide a reputation level confirmation method to ensure the fairness of trustworthiness evaluation. Formal security proof and security analysis are provided to show that our trustworthiness evaluation protocol can achieve the goals of privacy protection, reliability, fairness and resistance to several security attacks. Experiments demonstrate that this protocol can save execution time and achieve reliable and fair trustworthiness evaluation for PSRS. © 2015 Chinese Institute of Electronics.

关键词： Resistance Privacy Protocols Uncertainty Reliability engineering Delays Reliability Security Protection Recommender systems

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Eco-friendly, mechanically robust, weather-resistant and rapidly cured polyurea elastomer synthesized by vat photopolymerization 3D printing

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Composites Part A: Applied Science and Manufacturing 2025年 196卷

作者： Yu, Yin Xu, Lisheng Liu, Siqi Gu, Gege Chen, Yufeng Yuan, Haochen Kuan, Hsu-Chiang Liu, Jianbang Meng, Qingshi Ma, Jun College of Medicine and Bioinformatics Engineering Northeastern University Shenyang110819 China College of Information Science and Engineering Northeastern University Shenyang110004 China Key Laboratory of Medical Image Computing Ministry of Education Shenyang110169 China College of Civil Aviation Shenyang Aerospace University Shenyang110136 China College of Aerospace Engineering Shenyang Aerospace University Shenyang110136 China UniSA STEM and Future Industries Institute University of South Australia Mawson Lakes5095 Australia

Vat photopolymerization (VP) 3D printing is increasingly employed for efficient elastomer production. However, the printed elastomers often exhibit unsatisfactory mechanical properties, and the process involves harmful volatile substances. Polyurea elastomers, which have a fast reaction rate and high viscosity during synthesis, pose challenges for solvent-free or diluent-free VP 3D printing. This study developed an eco-friendly, mechanically robust and rapidly cured polyurea elastomer by VP 3D printing. The synthesis rate and viscosity of polyurea prepolymer were reduced by controlling the reaction time and stirring speed for reactions without solvent and diluent. Novel urea-acryl resins were designed by employing two acrylic monomers. The mechanical properties of the UV-cured polyurea elastomer were further enhanced through graphene incorporation. The polyurea/graphene nanocomposite at an optimal graphene loading of 0.10 wt% exhibited tensile strength of 12.9 MPa, and elongation at break of 399.2 %. These values correspond to increments of 76.7 % and 66.8 %, respectively, over the neat DLP polyurea elastomer. Moreover, these photocured elastomers exhibited excellent recyclability and weather resistance. The innovative synthesis strategy offers a solution to the mechanical limitations of photocured resins while minimizing environmental impact. © 2025 The Authors

关键词： Tensile strength

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Segmentation of Lung Parenchyma Based on FPN++Mask R-CNN Model 4

Segmentation of Lung Parenchyma Based on FPN++Mask R-CNN Mod...

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4th International Symposium on image computing and Digital Medicine, ISICDM 2020

作者： Xu, Xingfei Li, Gen Li, Xiaoshuo Liu, Pan Tan, Wenjun Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110189 China

ISBN: (纸本)9781450389686

In recent years, the number of lung cancer patients has continued to increase. In the process of detecting lung cancer, accurate segmentation of lung parenchyma plays a key role. In this paper, we proposed a method of lung parenchyma segmentation based on FPN++Mask R-CNN neural network model. The model improved original Mask R-CNN networks and optimized the structure of FPN (Feature Pyramid Networks), which is the feature extraction model of Mask R-CNN, by expanding the scale and level of FPN to fuse and extract more picture feature information from different levels. The experimental results show that compared with original Mask R-CNN models, FPN++Mask R-CNN demonstrates better segmentation results. © 2020 ACM.

关键词： Biological organs

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Predicting Transcription Factor Binding Signals Based on a Novel Encoder-Decoder Framework 2nd

Predicting Transcription Factor Binding Signals Based on a N...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wang, Siguo Li, Zhipeng 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

ISBN: (纸本)9789819619061

The transcription process is the initial step in gene expression and the core of the regulatory mechanism, in which transcription factors control gene expression through interactions with particular DNA sequences. Therefore, identification of transcription factor binding sites (TFBSs) provides theoretical basis for further dissection of transcriptional regulatory mechanisms and disease diagnosis. To address the limitations of current research on TFBSs, a novel symmetric Encoder-Decoder framework is proposed for predicting transcription factor binding signals values at base sites. Our approach utilizes word vectors to encode DNA sequences, followed by encoders that progressively extract feature representations from these sequences. We then employ Gated Recurrent Unit (GRU) to capture the contextual dependencies within the sequences, ultimately using decoders to reconstruct the binding signal values for the DNA sequence sites. In order to access the effectiveness of the proposed symmetric Encoder-Decoder framework, some experiments were conducted on 20 ChIP-seq datasets, and the results show that the proposed novel method outperformers several existing advanced methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： DNA sequences

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Automatic treebank conversion via informed decoding-A case study on Chinese treebanks

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ACM Transactions on Asian Language Information Processing 2011年第3期10卷 1–24页

作者： Zhu, Muhua Zhu, Jingbo Xiao, Tong Key Laboratory of Medical Image Computing Natural Language Processing Lab Northeastern University Shenyang 110819 China

Treebanks are valuable resources for syntactic parsing. For some languages such as Chinese, we can obtain multiple constituency treebanks which are developed by different organizations. However, due to discrepancies of underlying annotation standards, such treebanks in general cannot be used together through direct data combination. To enlarge training data for syntactic parsing, we focus in this article on the challenge of unifying standards of disparate treebanks by automatically converting one treebank (source treebank) to fit a different standard which is exhibited by another treebank (target treebank). We propose to convert a treebank in two sequential steps which correspond to the part-of-speech level and syntactic structure level (including tree structures and grammar labels), respectively. Approaches used in both levels can be unified as an informed decoding procedure, where information derived from original annotation in a source treebank is used to guide the conversion conducted by a POS tagger (or a parser in the syntactic structure level) trained on a target treebank. We take two Chinese treebanks as a case study, and experiments on these two treebanks show significant improvements in conversion accuracy over baseline systems, especially in situations where a target treebank is small in size. © 2011 ACM.

关键词： Decoding

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Stfi: Structure-Aware and Time-Frequency Domain Interaction Network for Deformable medical image Registration

SSRN

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SSRN 2025年

作者： Hu, Rui Wang, Nian Zhang, Yuxin Li, Teng Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei230601 China Advanced Imaging Research Center University of Texas Southwestern Medical Center 5323 Harry Hines Blvd Dallas75390 United States School of Cyber Science and Engineering Southeast University No.2 Southeast University Road Nanjing211189 China

Deformable image registration is crucial in medical image analysis. Recently, unsupervised methods using intensity matching have shown promising results without labeled data. However, these methods still face challenges: (1) They often encounter local optima when handling anatomical structures with significant variations in intensity distribution;(2) Time domain convolution in decoders leads to loss of high-frequency fine features, limiting the effectiveness of generating fine deformation vector fields. To address these issues, we propose a novel structure-aware and time-frequency domain interaction (STFI) network. STFI integrates a structure-aware frequency domain loss and a time-frequency domain interaction module. The former leverages principal structural information from the weighted phase spectrum to guide the registration process, thereby reducing the risk of local optima. The latter processes and amalgamates information from both the time and frequency domains, effectively preserving high-frequency details. The efficacy and adaptability of our approach have been demonstrated on two types of data, offering a new perspective on exploiting time-frequency domain information for medical image registration. © 2025, The Authors. All rights reserved.

关键词： image registration

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Language modeling for syntax-based machine translation using tree substitution grammars: A case study on Chinese-english translation

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ACM Transactions on Asian Language Information Processing 2011年第4期10卷 18.1-18.29页

作者： Xiao, Tong Zhu, Jingbo Zhu, Muhua Northeastern University Key Laboratory of Medical Image Computing Natural Language Processing Lab Shenyang 110004 China

The poor grammatical output of Machine Translation (MT) systems appeals syntax-based approaches within language modeling. However, previous studies showed that syntax-based language modeling using (Context- Free) Treebank Grammars was not very helpful in improving BLEU scores for Chinese-English machine translation. In this article we further study this issue in the context of Chinese-English syntax-based Statistical Machine Translation (SMT) where Synchronous Tree Substitution Grammars (STSGs) are utilized to model the translation process. In particular, we develop a Tree Substitution Grammar-based language model for syntax-based MT, and present three methods to efficiently integrate the proposed language model into MT decoding. In addition, we design a simple and effective method to adapt syntax-based language models for MT tasks. We demonstrate that the proposed methods are able to benefit a state-of-the-art syntax-based MT system. On the NIST Chinese-English MT evaluation corpora, we finally achieve an improvement of 0.6 BLEU points over the baseline. © 2011 ACM.

关键词： Machine translation

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A Self-Supervised Transfer Learning Framework for Gland Segmentation in Colorectal Cancer Histopathology with Limited Labeled Data 2nd

A Self-Supervised Transfer Learning Framework for Gland Segm...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wen, Huer Wu, Yan Huang, De-Shuang Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology Shanghai East Hospital School of Medicine Tongji University Shanghai200123 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Ningbo Institute of Digital TwinEastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China

ISBN: (纸本)9789819619061

Accurate gland segmentation in colorectal cancer histopathology images is crucial for diagnosis and grading but is hampered by the limited availability of annotated data. To develop a robust and accurate gland segmentation method that effectively leverages unlabeled data to overcome the limitations of small annotated datasets. We propose a novel framework that combines self-supervised learning and transfer learning. A vision transformer is pre-trained using DINOv2 on a large unlabeled dataset (IMP-CRS-2024) and then fine-tuned on the GlaS dataset using a lightweight decoder and a boundary-aware loss function that combines Dice loss and a distance transform-weighted boundary loss. Our method achieves state-of-the-art performance on the GlaS dataset, surpassing existing methods in terms of F1-score, Object Dice, and Object Hausdorff distance. An ablation study confirms the benefits of both the self-supervised pre-training and the boundary-aware loss. Our approach effectively addresses the challenge of limited annotated data in gland segmentation, offering a promising solution for improving the accuracy and efficiency of colorectal cancer diagnosis. The framework can be generalized to other histopathology image analysis tasks and cancer types. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Self-supervised learning

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