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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yuyu Yuan Jinsheng Shi Jincui Yang Chenlong Li Yuang Cai Baoyu Tang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

The application of artificial intelligence to mechanical ventilation has garnered significant attention, especially with the advancement of deep reinforcement learning. Mechanical ventilation is a medical procedure used in critical care to provide life support for patients with lung injuries. Physicians must continuously diagnose the patient’s condition and adjust ventilator parameters. Existing reinforcement learning treatment models provide decision support but solely focus on treatment while neglecting diagnosis. This paper proposes the DTE-CQL(Diagnose Transformer-Encoder Conservative Q-Learning) model to address this limitation. The DTE model predicts the next time-step observation and generates informative representations for auxiliary treatment. The DTE-CQL model can provide a treatment strategy and performs 1.127 times better than physicians. We trained and validated our model using the MIMIC-III dataset, demonstrating its ability to accurately predict the next time-step observation for diagnosis and provide physicians with a safe, effective, and reasonable treatment strategy.

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FIRE: combining multi-stage filtering with taint analysis for scalable recurring vulnerability detection 24

FIRE: combining multi-stage filtering with taint analysis fo...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Siyue Feng Yueming Wu Wenjie Xue Sikui Pan Deqing Zou Yang Liu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Cyber Science and Engineering Huazhong University of Science and Technology China Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Cyber Science and Engineering Huazhong University of Science and Technology China and Jinyinhu Laboratory China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133441

With the continuous development of software open-sourcing, the reuse of open-source software has led to a significant increase in the occurrence of recurring vulnerabilities. These vulnerabilities often arise through the practice of copying and pasting existing vulnerabilities. Many methods have been proposed for detecting recurring vulnerabilities, but they often struggle to ensure both high efficiency and consideration of semantic information about vulnerabilities and patches. In this paper, we introduce FIRE, a scalable method for large-scale recurring vulnerability detection. It utilizes multi-stage filtering and differential taint paths to achieve precise clone vulnerability scanning at an extensive scale. In our evaluation across ten open-source software projects, FIRE demonstrates a precision of 90.0% in detecting 298 recurring vulnerabilities out of 385 ground truth instance. This surpasses the performance of existing advanced recurring vulnerability detection tools, detecting 31.4% more vulnerabilities than VUDDY and 47.0% more than MOVERY. When detecting vulnerabilities in large-scale software, FIRE outperforms MOVERY by saving about twice the time, enabling the scanning of recurring vulnerabilities on an ultra-large scale.

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WBRP: Consensus Algorithm Based on Weak Proof of Work

WBRP: Consensus Algorithm Based on Weak Proof of Work

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IEEE International Conference on Blockchain (Blockchain)

作者： Jiarui Li Dongbin Wang Hui Zhi Wencheng Zhu Jianing Li Zedong Wang School of Cyberspace Engineering Beijing University of Post and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service Beijing China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Nakamoto Consensus (NC) is a classic Proof of Work (PoW) consensus algorithm where the probability of miners earning the right to a block is proportional to their computational power. To prevent blockchain forks, the interval between block outputs is restricted, which results in lower throughput for PoW. To address blockchain performance issues, a transaction ledger scheme based on weak PoW consensus has been proposed. This approach combines strong and weak blocks, allowing miners to submit weak blocks with lower mining difficulty. Nodes can then use these weak blocks to organize transaction information on the main chain and ensure strict linear ordering of blocks and transactions through sorting rules. Experimental results demon-strate that the consensus algorithm based on weak PoW can significantly enhance system performance, allowing transaction throughput to increase linearly with bandwidth. Specifically, with a node bandwidth of 1 Gb/s, the performance can exceed NC by more than 300 times.

关键词： System performance Consensus algorithm Bandwidth Throughput Proof of Work Extensibility Blockchains

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DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for Highly-Effective Drug-Protein Interaction Representation

DrugProtKGE: Weakly Supervised Knowledge Graph Embedding for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yanlong Qiu Siqi Wang Xi Yang Xinyuan Qiu Chengkun Wu Yingbo Cui Canqun Yang Institute for Quantum Information & State Key Laboratory of High-Performance Computing College of Computer Science National University of Defense Technology Changsha P. R. China National Supercomputer Center in Tianjin Tianjin P. R. China National Key Laboratory of Parallel and Distributed Computing College of Computer Science National University of Defense Technology Changsha P. R. China Department of Biology and Chemistry College of Science National University of Defense Technology Changsha P. R. China

With the exponential growth of biomedical knowledge in unstructured text repositories such as PubMed, it is imminent to establish a knowledge graph-style, efficient searchable and targeted database that can support the need of information retrieval from researchers and clinicians. To mine knowledge from graph databases, most previous methods view a triple in a graph (see Fig. 1) as the basic processing unit and embed the triplet element (i.e. drugs/chemicals, proteins/genes and their interaction) as separated embedding matrices, which cannot capture the semantic correlation among triple elements. To remedy the loss of semantic correlation caused by disjoint embeddings, we propose a novel approach to learn triple embeddings by combining entities and interactions into a unified representation. Furthermore, traditional methods usually learn triple embeddings from scratch, which cannot take advantage of the rich domain knowledge embedded in pre-trained models, and is also another significant reason for the fact that they cannot distinguish the differences implied by the same entity in the multi-interaction triples. In this paper, we propose a novel fine-tuning based approach to learn better triple embeddings by creating weakly supervised signals from pre-trained knowledge graph embeddings. The method automatically samples triples from knowledge graphs and estimates their pairwise similarity from pre-trained embedding models. The triples are then fed pairwise into a Siamese-like neural architecture, where the triple representation is fine-tuned in the manner bootstrapped by triple similarity scores. Finally, we demonstrate that triple embeddings learned with our method can be readily applied to several downstream applications (e.g. triple classification and triple clustering). We evaluated the proposed method on two open-source drug-protein knowledge graphs constructed from PubMed abstracts, as provided by BioCreative. Our method achieves consistent improvement in both t

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Toward Wisdom-Evolutionary and Primitive-Concise 6G:A New Paradigm of Semantic Communication Networks

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Engineering 2022年第1期8卷 60-73页

作者： Ping Zhang Wenjun Xu Hui Gao Kai Niu Xiaodong Xu Xiaoqi Qin Caixia Yuan Zhijin Qin Haitao Zhao Jibo Wei Fangwei Zhang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing 100876China Peng Cheng Laboratory Shenzhen 518066China Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China The School of Electronic Engineering and Computer Science Queen Mary University of LondonLondon E14NSUK College of Electronic Science and Technology National University of Defense TechnologyChangsha 410073China School of Humanities Beijing University of Posts and TelecommunicationsBeijing 100876China

The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological *** this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G *** particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network *** the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information *** proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in *** features a lower bandwidth requirement,less redundancy,and more accurate intent *** also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.

关键词： 6G Semantic information Semantic communication Intelligent communication

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Enhancement for Versatile Video Coding in Vehicular Ad Hoc Network Scenarios

Enhancement for Versatile Video Coding in Vehicular Ad Hoc N...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Jiacheng Wu Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Vehicular Ad hoc Network (VANET) allows infor-mation exchange between vehicles. In VANET, video is the most recommended medium for transmitting information. However, the characteristics of VANET and the requirements of video content in terms of quality have resulted in various challenges. Besides, the movements of vehicles introduce complex motions in videos, making them more difficult to compress. As a result, it is a challenging task to transmit as less bits of video streams as possible while maintaining their visual quality. Therefore, we analyze Affine Motion Estimation (AME), a new coding tool in the latest generation video coding standard H.266NV C. Subsequently, a fast algorithm is proposed to make it able to describe complex motions such as rotation, zoom, shear, etc but also with low complexity. Experimental results show that the algorithm reduces the encoding complexity of AME by 20.19% and the overall encoding complexity by 3.25% compared to anchor VTM21.0, with eligible coding performance loss under Random Access (RA) configurations.

关键词： Video coding Visualization Motion estimation Vehicular ad hoc networks Streaming media Feature extraction Encoding Complexity theory Streams Standards

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A SPECTRAL LEVENBERG-MARQUARDT-DEFLATION METHOD FOR MULTIPLE SOLUTIONS OF SEMILINEAR ELLIPTIC SYSTEMS

arXiv

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arXiv 2025年

作者： Li, Lin Zhou, Yuheng Xie, Pengcheng Li, Huiyuan School of Mathematics and Physics University of South China Hengyang China Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA94720 United States State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China

Many nonlinear differential equations arising from practical problems may permit nontrivial multiple solutions relevant to applications, and these multiple solutions are helpful to deeply understand these practical problems and to improve some applications. Developing an efficient numerical method for finding multiple solutions is very necessary due to the nonlinearity and multiple solutions of these equations. Moreover, providing an efficient iteration plays an important role in successfully obtaining multiple solutions with fast and stable convergence. In the current paper, an efficient algorithm for finding multiple solutions of semilinear elliptic systems is proposed, where the trust region Levenberg-Marquardt method is firstly used to iterate the resulted nonlinear algebraic system. When the nonlinear term in these equations has only the first derivative, our algorithm can efficiently find multiple solutions as well. Several numerical experiments are tested to show the efficiency of our algorithm, and some solutions which have not been shown in the literature are also found and *** Codes 65N35, 65N22, 65F05, 65L10 Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear equations

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Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Wang, Yijie Jian, Songlei Liao, Qing Wang, Yongjun Pang, Guansong Beijing100091 China The National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Hunan410073 China The College of Computer National University of Defense Technology Hunan410073 China Guangdong518055 China The School of Computing and Information Systems Singapore Management University Singapore178902 Singapore

Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising advanced neural network structures and new reconstruction/prediction learning objectives. However, their one-class learning process can be misled by latent anomalies in training data (i.e., anomaly contamination) under the unsupervised paradigm. Their learning process also lacks knowledge about the anomalies. Consequently, they often learn a biased, inaccurate normality boundary. To tackle these problems, this paper proposes calibrated one-class classification for anomaly detection, realizing contamination-tolerant, anomaly-informed learning of data normality via uncertainty modeling-based calibration and native anomaly-based calibration. Specifically, our approach adaptively penalizes uncertain predictions to restrain irregular samples in anomaly contamination during optimization, while simultaneously encouraging confident predictions on regular samples to ensure effective normality learning. This largely alleviates the negative impact of anomaly contamination. Our approach also creates native anomaly examples via perturbation to simulate time series abnormal behaviors. Through discriminating these dummy anomalies, our one-class learning is further calibrated to form a more precise normality boundary. Extensive experiments on ten real-world datasets show that our model achieves substantial improvement over sixteen state-of-the-art contenders. © 2022, CC BY.

关键词： Contamination

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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Feature and Performance Comparison of FaaS Platforms

Feature and Performance Comparison of FaaS Platforms

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IEEE International Conference on Software Engineering and Service Sciences (ICSESS)

作者： Penghui Ma Peichang Shi Guodong Yi National Key Laboratory of Parallel and Distributed Processing College of Computer Science National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems College of Computer Science National University of Defense Technology Changsha China Xiangjiang Lab Changsha China School of Advanced Interdisciplinary Studies Hunan University Of Technology and Business Changsha China

With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.

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