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Three-dimensional diabetic macular edema thickness maps based on fluid segmentation and fovea detection using deep learning

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International Journal of Ophthalmology(English edition) 2022年第3期15卷 495-501页

作者： Jing-Jing Xu Yang Zhou Qi-Jie Wei Kang Li Zhen-Ping Li Tian Yu Jian-Chun Zhao Da-Yong Ding Xi-Rong Li Guang-Zhi Wang Hong Dai School of Medicine Tsinghua UniversityBeijing 100084China Visionary Intelligence Company Limited Beijing 100872China Department of Ophthalmology Beijing HospitalNational Center of GerontologyInstitute of Geriatric MedicineChinese Academy of Medical ScienciesBeijing 100730China Key Lab of Data Engineering and Knowledge Engineering Renmin University of ChinaBeijing 100872China

AIM: To explore a more accurate quantifying diagnosis method of diabetic macular edema(DME) by displaying detailed 3D morphometry beyond the gold-standard quantification indicator-central retinal thickness(CRT) and apply it in follow-up of DME ***: Optical coherence tomography(OCT) scans of 229 eyes from 160 patients were *** manually annotated cystoid macular edema(CME), subretinal fluid(SRF) and fovea as ground *** convolution neural networks(DCNNs) were constructed including U-Net, sASPP, HRNetV2-W48, and HRNetV2-W48+Object-Contextual Representation(OCR) for fluid(CME+SRF) segmentation and fovea detection respectively, based on which the thickness maps of CME, SRF and retina were generated and divided by Early Treatment Diabetic Retinopathy Study(ETDRS) ***: In fluid segmentation, with the best DCNN constructed and loss function, the dice similarity coefficients(DSC) of segmentation reached 0.78(CME), 0.82(SRF), and 0.95(retina).In fovea detection, the average deviation between the predicted fovea and the ground truth reached 145.7±117.8 μ*** generated macular edema thickness maps are able to discover center-involved DME by intuitive morphometry and fluid volume, which is ignored by the traditional definition of CRT>250 μ*** maps could also help to discover fluid above or below the fovea center ignored or underestimated by a single OCT ***: Compared to the traditional unidimensional indicator-CRT, 3D macular edema thickness maps are able to display more intuitive morphometry and detailed statistics of DME, supporting more accurate diagnoses and follow-up of DME patients.

关键词： diabetic macular edema fluid segmentation fovea detection 3D macular edema thickness maps deep learning

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SLMP: A Scientific Literature Management Platform Based on Large Language Models 15

SLMP: A Scientific Literature Management Platform Based on L...

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15th IEEE International Conference on knowledge Graph, ICKG 2024

作者： Guo, Menghao Jiang, Jinling Wu, Fan Sun, Shanxin Zhang, Chen Li, Wenhui Sun, Zeyi Chen, Guangyong Wu, Xindong Research Center for Life Sciences Computing Zhejiang Lab Hangzhou China Research Center for Data Hub and Security Zhejiang Lab Hangzhou China Research Center for High Efficiency Computing System Zhejiang Lab Hangzhou China Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Hefei China

ISBN: (纸本)9798331508821

This paper presents a Scientific Literature Management Platform (SLMP, demo link1 ) based on large language models (LLMs). The platform consists of four modules: literature management, literature extraction, literature retrieval, and question answering. The core techniques used to support the four modules across the platform include a fine-tuned model PaperExtractGPT and a continual pre-training model ChatPaperGPT based on ChatGLM2 using the data from scientific research literature, responsible for information extraction and communication, respectively. Due to their powerful capabilities in natural language understanding and generation, LLMs can understand complex scientific concepts based on the provided contexts, and thus generate high-quality texts and conduct in-depth information retrieval and question answering. Our platform can help researchers manage and utilize literature more effectively and efficiently for finding relevant literature, obtaining required information, and generating new knowledge. © 2024 IEEE.

关键词： Question answering

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Filtering Redundant RFID data Based on Sliding Windows

Filtering Redundant RFID Data Based on Sliding Windows

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International Conference on Management of e-Commerce and e-Government, ICMECG

作者： Wu Rui Liao Guoqiong Di Guoqiang School of Information Technology Jiangxi University of Finance and Economics Nanchang China School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Nanchang China

Filtering redundancy data is an important task of radio frequency identification (RFID) middleware. In order to ensure RFID middleware can effectively filter the time redundant data and identify tagged object location change timely, this paper proposed a Temporal-Spatial bloom filter based on the sliding window model. The filter extends the one-dimension array in the standard bloom filter to a two-dimension array. Meanwhile, in order to guarantee the false positive rate doesn't increase due to the reason that the storage unit of the filter becomes full, we proposed a random decay strategy for deleting the expiration elements. The experimental results show that TSBF algorithm can filter time redundant data effectively and had a good performance to deal with location movement.

关键词： Radiofrequency identification Filtering algorithms Information filters data models Algorithm design and analysis

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A Tale of HodgeRank and Spectral Method: Target Attack Against Rank Aggregation is the Fixed Point of Adversarial Game

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023年第4期45卷 4090-4108页

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Li, Guorong Cao, Xiaochun Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Sun Yat sen Univ Sch Cyber Sci & Technol Shenzhen Campus Shenzhen 518107 Guangdong Peoples R China Univ Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China

Rank aggregation with pairwise comparisons has shown promising results in elections, sports competitions, recommendations, and information retrieval. However, little attention has been paid to the security issue of such algorithms, in contrast to numerous research work on the computational and statistical characteristics. Driven by huge profit, the potential adversary has strong motivation and incentives to manipulate the ranking list. Meanwhile, the intrinsic vulnerability of the rank aggregation methods is not well studied in the literature. To fully understand the possible risks, we focus on the purposeful adversary who desires to designate the aggregated results by modifying the pairwise data in this paper. From the perspective of the dynamical system, the attack behavior with a target ranking list is a fixed point belonging to the composition of the adversary and the victim. To perform the targeted attack, we formulate the interaction between the adversary and the victim as a game-theoretic framework consisting of two continuous operators while Nash equilibrium is established. Then two procedures against HodgeRank and RankCentrality are constructed to produce the modification of the original data. Furthermore, we prove that the victims will produce the target ranking list once the adversary masters the complete information. It is noteworthy that the proposed methods allow the adversary only to hold incomplete information or imperfect feedback and perform the purposeful attack. The effectiveness of the suggested target attack strategies is demonstrated by a series of toy simulations and several real-world data experiments. These experimental results show that the proposed methods could achieve the attacker's goal in the sense that the leading candidate of the perturbed ranking list is the designated one by the adversary.

关键词： Voting Sports Electric potential Training Stochastic processes Security Optimization Adversarial learning pairwise comparison ranking aggregation

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A new BP algorithm for network marketing performance evaluation of agricultural products

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Journal of Applied Sciences 2013年第20期13卷 4332-4335页

作者： Zou, Chun-Fang Huang, Li-Juan School of of Business Administration Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China School of Information Technology Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics 330013 Nanchang Jiangxi China

Network marketing performance evaluation can sum up and improve the network marketing activities for network enterprises. So, how to evaluate network marketing performance has become a matter of concern in the field related. The study mainly takes agricultural products for example and presents a new algorithm for evaluating network marketing performance based on improved BP neural network algorithm. First, a evaluation indicator system for evaluating network marketing performance of agricultural products is constructed;Second the study analyzes the reasons of the defects of BP neural network algorithm and then designs a new BP algorithm and improves it through changing its learning rate to speed up the calculation and simplify the structure of the original BP algorithm;Finally, the improved algorithm is realized with three e_commerce enterprises are taken for examples to evaluate the network marketing performance and verify the validity and feasibility of the model and the experimental results show that the model can evaluate the network marketing performance of different enterprises practically. © 2013 Asian Network for Scientific Information.

关键词： Agricultural products

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A Query-Oriented Summarization System for XML Elements

A Query-Oriented Summarization System for XML Elements

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International Conference on Management of e-Commerce and e-Government, ICMECG

作者： Dexi Liu Shihan Wu Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China Songjiang Branch of Shanghai Rural Commercial Bank Shanghai China

In document-center XML dataset, an element may contain so many text that users have to spend enough time to judge the elements returned by XML search engine are valuable or not. Query-orient XML summarization system aim to provide users a brief and readable substitution of the original retrieved elements according to the user's query, which can relieve user's reading burden effectively. In this work, we extract sentences from the results of XML search engine, and combine them as a summary. Experiments on the IEEE-CS datasets used in INEX show that, the query-oriented XML summary generated by our method is reasonable.

关键词： XML Feature extraction Sections Search engines Computational modeling Electronic mail

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Suicidal Ideation Cause Extraction From Social Texts

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IEEE ACCESS 2020年 8卷 169333-169351页

作者： Liu, Dexi Fu, Qi Wan, Changxuan Liu, Xiping Jiang, Tengjiao Liao, Guoqiong Qiu, Xiangqing Liu, Renqiang Jiangxi Univ Finance & Econ Sch Informat Management Nanchang 330013 Jiangxi Peoples R China Jiangxi Univ Finance & Econ Jiangxi Prov Key Lab Data & Knowledge Engn Nanchang 330013 Jiangxi Peoples R China Jiangxi Sci & Technol Normal Univ Sch Commun & Elect Nanchang 330013 Jiangxi Peoples R China

Suicide has become a major public health and social concern in the world. Suicidal ideation cause extraction (SICE) in social texts can provide support for suicide prevention. This article summarizes the research on suicidal ideation causes (SICs) through the use of psychological and sociological analysis. Then, a social text-based SIC dataset is constructed and analyzed statistically for various features. A CRF model is provided along with Char-BiLSTM-CRF, which uses concatenation of word embeddings and character embeddings as word representation inputs. Then, the effects on the task are explored by the word (W), part of speech (POS), dependence relationship (DP), suicidal psychology (PCS), emotion (ET), and language (LG) features in the CRF model. The experiment shows that the word features worked best. POS and DP can be somehow covered by word features. PCS, ET and LG features can improve the effect of SICE. It also shows that Char-BiLSTM-CRF is better than CRF in general, but CRF still has advantages in terms of precision. Adding character embeddings and CRF layers can significantly improve the extraction using Char-BiLSTM-CRF. The experiment also compared three-word embeddings with Word2vec, ELMo and BERT. Compared with Word2vec, the F-value of ELMo is increased by approximately 5% on average, and compared with ELMo, the C_F and E_F of BERT are increased by 3.5% and 2.3%, respectively. Finally, the challenge of SICE is discussed based on the experimental results of Char-BiLSTM-CRF.

关键词： Silicon carbide Psychology Social network services data mining Economics Sociology Biological system modeling Suicidal ideation suicidal ideation cause extraction (SICE) social text CRF char-BiLSTM-CRF

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Poisoning Attack Against Estimating From Pairwise Comparisons

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第10期44卷 6393-6408页

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Peng Cheng Lab Artificial Intelligence Res Ctr Shenzhen 518055 Guangdong Peoples R China Chinese Acad Sci Inst Comp Technol Key Lab Intelligent Informat Proc Beijing 100190 Peoples R China Jiangxi Normal Univ Sch Comp & Informat Engn Nanchang 330022 Jiangxi Peoples R China Chinese Acad Sci Inst Informat Engn State Key Lab Informat Secur SKLOIS Beijing 100093 Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Key Lab Big Data Min & Knowledge Management Beijing 100049 Peoples R China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically.

关键词： Optimization Heuristic algorithms Sports Voting Uncertainty Games data models Adversarial learning poisoning attack pairwise comparison rank aggregation robust game distributionally robust optimization

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knowledge Graph based Mutual Attention for Machine Reading Comprehension over Anti-Terrorism Corpus

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data Intelligence 2023年第3期5卷 685-706页

作者： Feng Gao Jin Hou Jinguang Gu Lihua Zhang School of Computer Science and Technology Wuhan University of Science and TechnologyWuhan 430065Hubei The Key Laboratory of Rich-Media Knowledge Organization and Service of Digital Publishing Content Insitute of Scientic and Technical Information of ChinaBeijing 100038China Wuhan University of Science and Technology Big Data Science and Engineering Research Institute Wuhan 430065Hubei Eastchina Jiaotong University Nanchang 330013Jiangxi

Machine reading comprehension has been a research focus in natural language processing and intelligence ***,there is a lack of models and datasets for the MRC tasks in the anti-terrorism ***,current research lacks the ability to embed accurate background knowledge and provide precise *** address these two problems,this paper first builds a text corpus and testbed that focuses on the anti-terrorism domain in a semi-automatic ***,it proposes a knowledge-based machine reading comprehension model that fuses domain-related triples from a large-scale encyclopedic knowledge base to enhance the semantics of the *** eliminate knowledge noise that could lead to semantic deviation,this paper uses a mixed mutual ttention mechanism among questions,passages,and knowledge triples to select the most relevant triples before embedding their semantics into the *** results indicate that the proposed approach can achieve a 70.70%EM value and an 87.91%F1 score,with a 4.23%and 3.35%improvement over existing methods,respectively.

关键词： Machine reading comprehension Anti-terrorism domain knowledge embedding knowledge attention Mutual attention

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Efficient Processing of Global Top-k Aggregate Queries in Uncertain database

Efficient Processing of Global Top-k Aggregate Queries in Un...

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International Conference on Information engineering and Computer Science (ICIECS)

作者： Xianli Xing Dexi Liu Principal's Office Jiangxi University of Finance and Economics Nanchang China School of Information Technology Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China

ISBN: (纸本)9781424449934

Top-k queries in uncertain databases are quite popular and useful due to its wide application usage. However, compared to top-k in traditional databases, queries over uncertain database are more complicated because of the existence of exponential possible worlds. A Top-k aggregate query ranks groups of tuples by their aggregate values, sum or average for example, and returns k groups with the highest aggregate values. As a powerful semantic of top-k, global top-k, returns A highest-ranked tuples according to their probabilities of being in the top-k anTopswers in possible worlds. We propose a dynamic programming based method to process global top-k aggregate queries in uncertain database, where the number of retrieved tuples and group states generated on these tuples are minimized. Experiment results show that our algorithm is effective.

关键词： Aggregates databases Finance Power generation economics Dynamic programming Information technology laboratories knowledge engineering Information retrieval Performance analysis

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