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Attacks Against Cross-Chain Systems and Defense Approaches:A Contemporary Survey

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1647-1667页

作者： Li Duan Yangyang Sun Wei Ni Weiping Ding Jiqiang Liu Wei Wang IEEE the Beijing Key Laboratory of Security and Privacy in Intelligent Transportation Beijing Jiaotong UniversityBeijing 100044China the Guangxi Key Laboratory of Cryptography and Information Security Guilin 541004China the Communications and Signal Processing Team Data61 Business UnitCommonwealth Scientific and Industrial Research Organisation(CSIRO)Sydney NSW 2109Australia the School of Information Science and Technology Nantong UniversityNantong 226019China

The blockchain cross-chain is a significant technology for inter-chain interconnection and value transfer among different blockchain ***-chain overcomes the“information island”problem of the closed blockchain network and is increasingly applied to multiple critical areas such as finance and the internet of things(IoT).Blockchain can be divided into three main categories of blockchain networks:public blockchains,private blockchains,and consortium ***,there are differences in block structures,consensus mechanisms,and complex working mechanisms among heterogeneous *** fragility of the cross-chain system itself makes the cross-chain system face some potential security and privacy *** paper discusses security defects on the cross-chain implementation mechanism,and discusses the impact of the structural features of blockchain networks on cross-chain *** terms of cross-chain intercommunication,a cross-chain attack can be divided into a multi-chain combination attack,native chain attack,and inter-chain attack *** various security threats and attack paths faced by the cross-chain system are *** last,the corresponding security defense methods of cross-chain security threats and future research directions for cross-chain applications are put forward.

关键词： Blockchain cross-chain defense distributed private key control hash-locking notary security threats sidechain/relay

MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification

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arXiv 2024年

作者： Li, Binghua Mao, Jie Sun, Zhe Li, Chao Zhao, Qibin Tanaka, Toshihisa Tokyo Japan Tokyo Japan Graduate School of Medicine Juntendo University Tokyo Japan Faculty of Health Data Science Juntendo University Tokyo Japan Image Processing Research Team RIKEN Tokyo Japan

Automated diagnosis with artificial intelligence has emerged as a promising area in the realm of medical imaging, while the interpretability of the introduced deep neural networks still remains an urgent concern. Although contemporary works, such as XProtoNet (Kim et al., 2021) and MProtoNet (Wei et al., 2024), has sought to design interpretable prediction models for the issue, the localization precision of their resulting attribution maps can be further improved. To this end, we propose a Multi-scale Attentive Prototypical part Network, termed MAProtoNet, to provide more precise maps for attribution. Specifically, we introduce a concise multi-scale module to merge attentive features from quadruplet attention layers, and produces attribution maps. The proposed quadruplet attention layers can enhance the existing online class activation mapping loss via capturing interactions between the spatial and channel dimension, while the multi-scale module then fuses both fine-grained and coarse-grained information for precise maps generation. We also apply a novel multi-scale mapping loss for supervision on the proposed multi-scale module. Compared to existing interpretable prototypical part networks in medical imaging, MAProtoNet can achieve state-of-the-art performance in localization on brain tumor segmentation (BraTS) datasets, resulting in approximately 4% overall improvement on activation precision score (with a best score of 85.8%), without using additional annotated labels of segmentation. Our code will be released in https://***/TUAT-Novice/maprotonet. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

A modified iterative algorithm for solving split equality problems

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UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2021年第3期83卷 45-54页

作者： Fu, Yuanmin Zhu, Li-Jun Wei, Haicheng School of Mathematics and Information Science North Minzu University Yinchuan750021 China The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China Health Big Data Research Institute of North Minzu University Yinchuan750021 China Advanced Intelligent Perception Control Technology Innovative Team of NingXia North Minzu University Yinchuan750021 China

In this paper, we investigate the split equality problems in Hilbert spaces. We propose an iterative algorithm with self-adaptive step-size for solving split equality prob-lems. Convergent analysis of the suggested algorithm is proved under some suitable conditions. © 2021, Politechnica University of Bucharest. All rights reserved.

关键词： Iterative methods

Procedural Text Understanding via Scene-Wise Evolution

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arXiv 2022年

作者： Tang, Jialong Lin, Hongyu Liao, Meng Lu, Yaojie Han, Xianpei Sun, Le Xie, Weijian Xu, Jin Chinese Information Processing Laboratory Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Procedural text understanding requires machines to reason about entity states within the dynamical narratives. Current procedural text understanding approaches are commonly entity-wise, which separately track each entity and independently predict different states of each entity. Such an entity-wise paradigm does not consider the interaction between entities and their states. In this paper, we propose a new scene-wise paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner. Based on this paradigm, we propose Scene Graph Reasoner (SGR), which introduces a series of dynamically evolving scene graphs to jointly formulate the evolution of entities, states and their associations throughout the narrative. In this way, the deep interactions between all entities and states can be jointly captured and simultaneously derived from scene graphs. Experiments show that SGR not only achieves the new state-of-the-art performance but also significantly accelerates the speed of reasoning. © 2022, CC0.

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Short-term nitisinone discontinuation of hereditary tyrosinemia type 1 mice causes metabolic alterations in glutathione metabolism/biosynthesis and multiple amino acid degradation pathways

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Genes & Diseases 2023年第5期10卷 1759-1762页

作者： Colemonts-Vroninks Haaike Norman P.Brendan Van Laere Sven Davison S.Andrew Marcélis Lionel Casimir Georges Goyens Philippe Claes Paul De Bundel Dimitri Martens Geert Ranganath Lakshminarayan Rao Vanhaecke Tamara Gallagher James A De Kock Joery Vrije Universiteit Brussel Liver Therapy&Evolution TeamIn Vitro Toxicology and Dermato-Cosmetology(IVTD)Research GroupFaculty of Medicine and PharmacyLaarbeeklaan 103Brussels B-1090Belgium Vrije Universiteit Brussel Experimental Pharmacology(EFAR)Research GroupFaculty of Medicine and PharmacyLaarbeeklaan 103Brussels B-1090Belgium University of Liverpool Dept.of Musculoskeletal&Ageing ScienceInstitute of Life Course&Medical SciencesLiverpool L78TXUnited Kingdom Vrije Universiteit Brussel Interfaculty Center Data Processing and StatisticsFaculty of Medicine and PharmacyLaarbeeklaan 103Brussels B-1090Belgium Liverpool University Hospitals NHS Foundation Trust Clinical Biochemistry and Metabolic MedicineLiverpool L35PSUnited Kingdom UniversitéLibre de Bruxelles Laboratoire de PédiatrieBrussels B-1050Belgium Vrije Universiteit Brussel In Vitro Liver Disease Modelling TeamIn Vitro Toxicology and Dermato-Cosmetology(IVTD)Research GroupFaculty of Medicine and PharmacyLaarbeeklaan 103Brussels B-1090Belgium AZ Delta General Hospital Department of Laboratory MedicineRoeselare 8800Belgium Ghent University Department of Biomolecular MedicineGhent 9052Belgium

Hereditary tyrosinemia type 1(HT1)is a life-threatening disease caused by the patient's inability to break down tyrosine due to loss-of-function mutations in the fumarylacetoacetate hydrolase(FAH)enzyme(Fig.S1).Currently,the only available life-saving treatment is nitisinone(NTBC).However,nitisinone therapy comes with debilitating side effects and requires a strict drug regime combined with a tyrosine-and phenylalanine-restricted ***,therapy adherence is often experienced as an additional *** this study,transcriptional profiling was conducted parallel to high-resolution metabolomics on,respectively,liver tissue and serum samples of Fah-deficient *** experimental workflow of our study is shown in Figure *** pathway analyses showed manifest activation of the NRF2-stress response,associated with hepatocellular damage and mainly driven by a markedly increased oxidative *** altered canonical pathways were primarily a downstream consequence of the ***,we observed an overall aminoacidemia and changes related to aminoacyl-tRNA biosynthesis,which postulates that short-term nitisinone discontinuation can already put the patient at risk for hepatocellular carcinoma ***,we did not only identify changes in numerous pathways,directly and indirectly,related to the metabolism of tyrosine and glutathione but also the presence of“side-chain”metabolites,such as y-glutamyl-and N-acetyl-coupled amino acids,indicating that non-directly related pathways like phase II biotransformation reactions are also affected.

关键词： continuation Figure hereditary

ECO v1: Towards Event-Centric Opinion Mining

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arXiv 2022年

作者： Xu, Ruoxi Lin, Hongyu Liao, Meng Han, Xianpei Xu, Jin Tan, Wei Sun, Yingfei Sun, Le Chinese Information Processing Laborator Institute of Software Chinese Academy of Sciences Beijing China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies. © 2022, CC BY.

关键词： data mining

Knowledgeable or educated guess? Revisiting language models as knowledge bases

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arXiv 2021年

作者： Cao, Boxi Lin, Hongyu Han, Xianpei Sun, Le Yan, Lingyong Liao, Meng Xue, Tong Xu, Jin Chinese Information Processing Laboratory State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases. © 2021, CC BY.

关键词： Forecasting

TEXT2EVENT: Controllable sequence-to-structure generation for end-to-end event extraction

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arXiv 2021年

作者： Lu, Yaojie Lin, Hongyu Xu, Jin Han, Xianpei Tang, Jialong Li, Annan Sun, Le Liao, Meng Chen, Shaoyi Chinese Information Processing Laboratory China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Event extraction is challenging due to the complex structure of event records and the semantic gap between text and event. Traditional methods usually extract event records by decomposing the complex structure prediction task into multiple subtasks. In this paper, we propose TEXT2EVENT, a sequence-to-structure generation paradigm that can directly extract events from the text in an end-to-end manner. Specifically, we design a sequence-to-structure network for unified event extraction, a constrained decoding algorithm for event knowledge injection during inference, and a curriculum learning algorithm for efficient model learning. Experimental results show that, by uniformly modeling all tasks in a single model and universally predicting different labels, our method can achieve competitive performance using only record-level annotations in both supervised learning and transfer learning settings. © 2021, CC BY.

关键词： Extraction

From discourse to narrative: Knowledge Projection for event relation extraction

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arXiv 2021年

作者： Tang, Jialong Lin, Hongyu Liao, Meng Lu, Yaojie Han, Xianpei Sun, Le Xie, Weijian Xu, Jin Chinese Information Processing Laboratory China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Data Quality Team WeChat Tencent Inc. China

Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The lack of high-quality labelled corpora further exacerbates that problem. In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them. Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multi-tier discourse knowledge effectively for event relation extraction. In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted. Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance, and extrinsic experimental results verify the value of the extracted event relations. © 2021, CC0.

关键词： Extraction