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arXiv 2023年

作者： Xiao, Yong Liao, Yiwei Li, Yingyu Shi, Guangming Poor, H. Vincent Saad, Walid Debbah, Mérouane Bennis, Mehdi The School of Electronic Information and Communications The Huazhong University of Science and Technology Wuhan430074 China The Peng Cheng Laboratory Guangdong Shenzhen518055 China Guangdong Guangzhou510555 China The School of Mech. Eng. and Elect. Inform. The China University of Geosciences Wuhan430074 China The School of Artificial Intelligence The Xidian University Shaanxi Xi’an710071 China The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24061 United States Cyber Security Systems and Applied AI Research Center Lebanese American University Beirut Lebanon Khalifa University of Science and Technology P O Box 127788 Abu Dhabi United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France The University of Oulu Oulu90014 Finland

—Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users’ quality-of-experience (QoE). Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information, e.g., hidden relations, concepts and implicit reasoning mechanisms of users, that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A graph-inspired structure is first developed to represent the complete semantics, including both explicit and implicit, of a message. A projection-based semantic encoder is then proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible;but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. By applying G-RML, we prove that the destination user can accurately imitate the reasoning process of the source u

关键词： Semantics

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DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences

arXiv

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arXiv 2023年

作者： Liu, Wentao Tian, Tong Wang, Lemeng Xu, Weijin Li, Lei Li, Haoyuan Zhao, Wenyi Tian, Siyu Pan, Xipeng Deng, Yiming Gao, Feng Yang, Huihua Wang, Xin Su, Ruisheng School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China State Key Laboratory of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Aeronautics and Astronautics Dalian University of Technology Dalian China Department of Interventional Neuroradiology Beijing Tiantan Hospital Capital Medical University Beijing China Ultrasonic Department The Fourth Hospital of Hebei Medical University Hebei Tumor Hospital Shijiazhuang China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Department of Radiology The Netherlands Cancer Institute Amsterdam Netherlands Department of Radiology & Nuclear Medicine Erasmus MC University Medical Center Rotterdam Netherlands Medical Image Analysis group Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands

The automated segmentation of Intracranial Arteries (IA) in Digital Subtraction Angiography (DSA) plays a crucial role in the quantification of vascular morphology, significantly contributing to computer-assisted stroke research and clinical practice. Current research primarily focuses on the segmentation of single-frame DSA using proprietary datasets. However, these methods face challenges due to the inherent limitation of single-frame DSA, which only partially displays vascular contrast, thereby hindering accurate vascular structure representation. In this work, we introduce DIAS, a dataset specifically developed for IA segmentation in DSA sequences. We establish a comprehensive benchmark for evaluating DIAS, covering full, weak, and semi-supervised segmentation methods. Specifically, we propose the vessel sequence segmentation network, in which the sequence feature extraction module effectively captures spatiotemporal representations of intravascular contrast, achieving intracranial artery segmentation in 2D+Time DSA sequences. For weakly-supervised IA segmentation, we propose a novel scribble learning-based image segmentation framework, which, under the guidance of scribble labels, employs cross pseudo-supervision and consistency regularization to improve the performance of the segmentation network. Furthermore, we introduce the random patch-based self-training framework, aimed at alleviating the performance constraints encountered in IA segmentation due to the limited availability of annotated DSA data. Our extensive experiments on the DIAS dataset demonstrate the effectiveness of these methods as potential baselines for future research and clinical applications. The dataset and code are publicly available at https://***/10.5281/zenodo.11396520 and https://***/lseventeen/DIAS. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Practical Iterative Quantum Consensus Protocol with Sharding Construction

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IEEE Journal on Selected Areas in Communications 2025年

作者： Ying, Chenhao Du, Yuxuan Zhang, Weiting Jiang, Xikun Wang, Gang Jin, Haiming Li, Jie Luo, Yuan Tao, Dacheng Shanghai Jiao Tong University Department of Computer Science Shanghai China Zhejiang University Shanghai Jiao Tong University State Key Laboratory of Blockchain and Data Security Blockchain Advanced Research Center Jiangsu Wuxi214101 China Nanyang Technological University College of Computing&Data Science Singapore Beijing Jiaotong University School of Electronic and Information Engineering Beijing China University of Copenhagen Department of Computer Science Denmark Northeastern University School of Computer Science and Engineering Shenyang China Shanghai Key Laboratory of Trusted Data Circulation and Governance Web3 China

With the development of quantum blockchain, the quantum consensus protocols have garnered increasing attention, which play a crucial role in driving the implementation of quantum blockchains. However, existing protocols, derived from the classical consensus algorithms, face practical application challenges due to current quantum technology limitations. The first challenge is the bottleneck in generating large-scale entangled quantum states. The second challenge arises from the generation of malicious quantum states. The final challenge involves privacy concerns. To address these challenges, we propose a practical iterative QUantum consensus protocol with sharding construction, namely, Q-Union. In fact, Q-Union employs an iterative consensus algorithm where participating nodes are divided into multiple smaller shards, with the consensus process occurring within the current shard, and new shards are involved only if consensus is not achieved. Leveraging Greenberger-Horne- Zeilinge states and Aharonov states, Q-Union harnesses the advantages of quantum mechanics to achieve anonymous consensus, protecting the private information of participating nodes. Additionally, by integrating state verification, Q-Union ensures the correctness of the consensus procedure in the presence of malicious nodes generating adversarial quantum states. Finally, it is proven that Q-Union can also defend against Byzantine attacks from adversarial nodes, maintaining the same security level as traditional non-sharded consensus protocols. Specifically, it consistently outputs the correct consensus when the fraction of adversaries among participating nodes is less than 1/2 with synchronous communication. Both the theoretical analysis and performance illustration demonstrate the superior performance of the proposed Q-Union compared to state-of-the-art protocols. © 1983-2012 IEEE.

关键词： Quantum entanglement

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Event extraction by associating event types and argument roles

arXiv

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arXiv 2021年

作者： Li, Qian Guo, Shu Wu, Jia Li, Jianxin Sheng, Jiawei Wang, Lihong Dong, Xiaohan Peng, Hao Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computing Macquarie University Sydney Australia Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Event extraction (EE), which acquires structural event knowledge from texts, can be divided into two sub-tasks: event type classification and element extraction (namely identifying triggers and arguments under different role patterns). As different event types always own distinct extraction schemas (i.e., role patterns), previous work on EE usually follows an isolated learning paradigm, performing element extraction independently for different event types. It ignores meaningful associations among event types and argument roles, leading to relatively poor performance for less frequent types/roles. This paper proposes a novel neural association framework for the EE task. Given a document, it first performs type classification via constructing a document-level graph to associate sentence nodes of different types, and adopting a graph attention network to learn sentence embeddings. Then, element extraction is achieved by building a universal schema of argument roles, with a parameter inheritance mechanism to enhance role preference for extracted elements. As such, our model takes into account type and role associations during EE, enabling implicit information sharing among them. Experimental results show that our approach consistently outperforms most state-of-the-art EE methods in both sub-tasks. Particularly, for types/roles with less training data, the performance is superior to the existing methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Extraction

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In-situ data scheduling optimization based on rainbow DQN for IIoT

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Future Generation computer Systems 2026年 174卷

作者： Peiying Zhang Siyi Li Lizhuang Tan Neeraj Kumar Jian Wang Kai Liu Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao 266580 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Patiala 147004 India Department of Computer Science University of Economics and Human Sciences Warszawa 01-043 Poland Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam 31441 Saudi Arabia College of Science China University of Petroleum (East China) Qingdao 266580 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing 100084 China State Key Laboratory of Space Network and Communications Tsinghua University Beijing 100084 China

In industrial systems, in-situ computing refers to a mode of local or proximate data processing within the cloud–edge-terminal architecture. In the Industrial Internet of Things (IIoT) environment, servers are typically deployed near terminal devices to coordinate and manage the scheduling of in-situ data, forming a comprehensive in-situ server system. With the rapid growth of IIoT terminal devices, challenges such as high processing delays and severe demand congestion arise in the in-situ data scheduling process. To address this challenge, this paper proposes a scheduling optimization scheme based on Rainbow DQN. The scheme designs a scheduling buffer and classification strategy to distinguish in-situ data based on storage demand characteristics, thereby optimizing the scheduling process. At the same time, a multi-level storage structure is introduced to store in-situ data classified according to the scheduling strategy. Additionally, the Rainbow DQN algorithm is utilized to assist in the scheduling decision-making of in-situ data retrieval, effectively alleviating system congestion while ensuring certain throughput performance. Extensive simulation experiments show that the Rainbow DQN algorithm achieves a reduction of 60.82% in average waiting delay, 62.94% in average unprocessed demand, and a 58.27% reduction in average completion delay.

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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Proof-of-GoS: An Efficient GoS-Based Consensus Algorithm for IOT

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IEEE Internet of Things Journal 2025年

作者： Gao, Guangyong Guo, Chongtao Wan, Xinyu Xia, Zhihua Shi, Yunqing Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Jiangsu Yuchi Blockchain Technology Research Institute Nanjing210044 China Jinan University College of Cyber Security Guangzhou510632 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102-1982 United States

With the advancement of 5G networks, the deployment of Internet of Things (IoT) technology has seen significant growth. Blockchain technology, recognized for its strong security features, is increasingly utilized within the IoT domain. However, the current IoT landscape is characterized by challenges such as substantial resource consumption, limited throughput capacity, and insufficient security protocols, which hinder its optimal performance. Towards addressing such problems, we propose a consensus algorithm called Proof-of-GoS (PoG) based on the grade of service (GoS), in which a node must have a service score over a set score threshold to be allowed to join the consensus process. The correct behavior of a node results in a reward, while any malicious actions result in penalties. Finally, we simulate a network to evaluate the performance and security of PoG and compare it with several existing consensus algorithms. The experimental findings indicate that the proposed PoG consensus algorithm retains the fundamental security properties of blockchain and outperforms the state-of-the-art consensus mechanisms. © 2014 IEEE.

关键词： Consensus algorithm

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DynaComm: Accelerating distributed CNN training between edges and clouds through dynamic communication scheduling

arXiv

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arXiv 2021年

作者： Cai, Shangming Wang, Dongsheng Wang, Haixia Lyu, Yongqiang Xu, Guangquan Zheng, Xi Vasilakos, Athanasios V. Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China Big Data School Qingdao Huanghai University Qingdao266427 China College of Intelligence and Computing Tianjin University Tianjin300350 China Department of Computing Macquarie University SydneyNSW2109 Australia College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China School of Electrical and Data Engineering University of Technology Sydney Australia Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden

To reduce uploading bandwidth and address privacy concerns, deep learning at the network edge has been an emerging topic. Typically, edge devices collaboratively train a shared model using real-time generated data through the Parameter Server framework. Although all the edge devices can share the computing workloads, the distributed training processes over edge networks are still time-consuming due to the parameters and gradients transmission procedures between parameter servers and edge devices. Focusing on accelerating distributed Convolutional Neural Networks (CNNs) training at the network edge, we present DynaComm, a novel scheduler that dynamically decomposes each transmission procedure into several segments to achieve optimal layer-wise communications and computations overlapping during run-time. Through experiments, we verify that DynaComm manages to achieve optimal layer-wise scheduling for all cases compared to competing strategies while the model accuracy remains untouched. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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LR-RRA-CCA secure functional encryption for randomized functionalities from trapdoor HPS and LAF

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science China(information sciences) 2018年第5期61卷 291-305页

作者： Huige WANG Kefei CHEN Baodong QIN Ziyuan HU Department of Network Engineering Anhui Science and Technology University Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Mathematics Hangzhou Normal University Westone Cryptologic Research Center National Engineering Laboratory for Wireless Security Xi'an University of Posts and Telecommunications State Key Laboratory of Cryptology

Original public-key encryption is viewed as a tool to encrypt point-to-point *** data is aimed at a particular user,where a sender encrypts a message x under a specified public key P *** this case,only the owner of the(unique)secret key corresponding to P K can decrypt the resulting ciphertext to recover the message ***,on some occasions,encryption is necessary to implement more fine-grained control of the encrypted *** example,the sender

关键词： LAF CCA RRA

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Probabilistic margins for instance reweighting in adversarial training 21

Probabilistic margins for instance reweighting in adversaria...

引用

Proceedings of the 35th International Conference on Neural information Processing Systems

作者： Qizhou Wang Feng Liu Bo Han Tongliang Liu Chen Gong Gang Niu Mingyuan Zhou Masashi Sugiyama Department of Computer Science Hong Kong Baptist University DeSI Lab Australian Artificial Intelligence Institute University of Technology Sydney TML Lab School of Computer Science Faculty of Engineering The University of Sydney PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of MoE and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology RIKEN Center for Advanced Intelligence Project (AIP) McCombs School of Business The University of Texas at Austin RIKEN Center for Advanced Intelligence Project (AIP) and Graduate School of Frontier Sciences The University of Tokyo

ISBN: (纸本)9781713845393

Reweighting adversarial data during training has been recently shown to improve adversarial robustness, where data closer to the current decision boundaries are regarded as more critical and given larger weights. However, existing methods measuring the closeness are not very reliable: they are discrete and can take only a few values, and they are path-dependent, i.e., they may change given the same start and end points with different attack paths. In this paper, we propose three types of probabilistic margin (PM), which are continuous and path-independent, for measuring the aforementioned closeness and reweighting adversarial data. Specifically, a PM is defined as the difference between two estimated class-posterior probabilities, e.g., such a probability of the true label minus the probability of the most confusing label given some natural data. Though different PMs capture different geometric properties, all three PMs share a negative correlation with the vulnerability of data: data with larger/smaller PMs are safer/riskier and should have smaller/larger weights. Experiments demonstrated that PMs are reliable and PM-based reweighting methods outperformed state-of-the-art counterparts.

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