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The Immense Impact of Reverse Edges on Large Hierarchical Networks

Engineering 2024年第5期36卷 240-249页

作者： Haosen Cao Bin-Bin Hu Xiaoyu Mo Duxin Chen Jianxi Gao Ye Yuan Guanrong Chen Tamás Vicsek Xiaohong Guan Hai-Tao Zhang MOE Engineering Research Center of Autonomous Intelligent Unmanned Systems State Key Laboratory of Intelligent Manufacturing Equipment and TechnologySchool of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Jiangsu Key Laboratory of Networked Collective Intelligence School of MathematicsSoutheast UniversityNanjing 210096China Department of Computer Science Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electronic Engineering City University of Hong KongHong Kong 999077China Department of Biological Physics Eötvös UniversityBudapest 1117Hungary MOE Key Laboratory for Intelligent Networks and Network Security School of Automation Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Hierarchical networks are frequently encountered in animal groups,gene networks,and artificial engineering systems such as multiple robots,unmanned vehicle systems,smart grids,wind farm networks,and so *** structure of a large directed hierarchical network is often strongly influenced by reverse edges from lower-to higher-level nodes,such as lagging birds’howl in a flock or the opinions of lowerlevel individuals feeding back to higher-level ones in a social *** study reveals that,for most large-scale real hierarchical networks,the majority of the reverse edges do not affect the synchronization process of the entire network;the synchronization process is influenced only by a small part of these reverse edges along specific *** surprisingly,a single effective reverse edge can slow down the synchronization of a huge hierarchical network by over 60%.The effect of such edges depends not on the network size but only on the average in-degree of the involved *** overwhelming majority of active reverse edges turn out to have some kind of“bunching”effect on the information flows of hierarchical networks,which slows down synchronization *** finding refines the current understanding of the role of reverse edges in many natural,social,and engineering hierarchical networks,which might be beneficial for precisely tuning the synchronization rhythms of these *** study also proposes an effective way to attack a hierarchical network by adding a malicious reverse edge to it and provides some guidance for protecting a network by screening out the specific small proportion of vulnerable nodes.

关键词： Synchronizability Large hierarchical networks Reverse edges information flows Complex networks

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Efficient survivable mapping algorithm for logical topology in IP-over-WDM optical networks against node failure

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Journal of Supercomputing 2023年第5期79卷 5037-5063页

作者： Cheng, Dun-Wei Chang, Jo-Yi Lin, Chen-Yen Lin, Limei Huang, Yanze Thulasiraman, Krishnaiyan Hsieh, Sun-Yuan Department of Computer Science and Information Engineering National Yunlin University of Science and Technology 123 University Road Yunlin64002 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University No. 1 University Road Tainan701 Taiwan College of Mathematics and Informatics and the Key Laboratory of Network Security and Cryptology Fujian Normal University Fujian Fuzhou350117 China Fujian Provincial Key Laboratory of Big Data Mining and Applications School of Mathematics and Physics Fujian University of Technology Fujian Fuzhou350118 China Department of Computer Science University of Oklahoma 660 Parrington Oval NormanOK73019 United States Department of Computer Science and Information Engineering National Chi Nan University No. 1 Daxue Rd Nantou County Puli Township54561 Taiwan

The survivable mapping problem in an IP-over-WDM network with a logical graph and physical graph is the problem of finding a mapping of a logical network in a physical layer so that any failure in physical topology does not break the logical topology’s connection. Determining whether a survivable mapping against failure exists is an NP-complete problem, and therefore many heuristic algorithms have been proposed in the literature. In this paper, a heuristic mapping design strategy is proposed to enable the lightpaths to more efficiently endure a node failure. We individually consider the logical topology by building a Hamiltonian cycle and then deal with the mapping problem for disjoint physical paths. Experimental results demonstrate that the proposed algorithm can provide efficient survivable mapping in IP-over-WDM networks. The simulation shows that the survivability of previous methods (SNF and SMART-Node) is around 60–80%, but our proposed algorithm can build survivable mapping in all kinds of situations. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Heuristic algorithms

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Static Analysis of Source Code Vulnerability Using Machine Learning Techniques: A Survey 4

Static Analysis of Source Code Vulnerability Using Machine L...

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4th International Conference on Artificial Intelligence and Big Data, ICAIBD 2021

作者： Wang, Jingjing Huang, Minhuan Nie, Yuanping Li, Jin National Key Laboratory of Science and Technology on Information System Security Beijing China

ISBN: (纸本)9780738131702

With the rapid increase of practical problem complexity and code scale, the threat of software security is increasingly serious. Consequently, it is crucial to pay attention to the analysis of software source code vulnerability in the development stage and take efficient measures to detect the vulnerability as soon as possible. Machine learning techniques have made remarkable achievements in various fields. However, the application of machine learning in the domain of vulnerability static analysis is still in its infancy and the characteristics and performance of diverse methods are quite different. In this survey, we focus on a source code-oriented static vulnerability analysis method using machine learning techniques. We review the studies on source code vulnerability analysis based on machine learning in the past decade. We systematically summarize the development trends and different technical characteristics in this field from the perspectives of the intermediate representation of source code and vulnerability prediction model and put forward several feasible research directions in the future according to the limitations of the current approaches. © 2021 IEEE.

关键词： Surveys

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Research on Image Captioning Based on Vision-language Pre-trained Models 9

Research on Image Captioning Based on Vision-language Pre-tr...

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9th International Conference on Big Data and information Analytics, BigDIA 2023

作者： Zou, Shiwei Xie, Yuxiang Yan, Jie Wei, Yingmei Luan, Xidao National University of Defense Technology Department of System Engineering Hunan China National University of Defense Technology Key Laboratory of Big Data Department of System Engineering Changsha Hunan China Changsha University Department of Computer Science Hunan China

ISBN: (纸本)9798350330076

Image captioning is a vision-language task that targets at describing an image by generating a coherent sentence automatically. This technology allows computers to understand and describe images like humans, enabling further processing of images. It combines computer vision and natural language processing, which has broad applications. This paper investigates the use of vision-language pre-trained models for image captioning. In this article, an encoder-decoder hybrid model based on PVT and BERT is pre-trained on a large number of image-text pairs, possessing both understanding and generation capabilities. It consists of three components: 1) an image encoder and a text encoder to extract image and text features respectively;2) a multi-modal encoder to align the two modalities;and 3) a text decoder to generate text descriptions. This paper focuses on enhancing the image encoder of the model, which is crucial for image captioning models as it extracts high-resolution image features. Reducing training time and computational costs can also improve the applicability of pre-trained models. Improvements are made to the original model by modifying the ViT model architecture of the image encoding module. A feature pyramid structure and a spatial reduction attention mechanism is introduced to extract high-resolution image features while reducing computational complexity and memory usage, thereby enhancing the universality of the model. The model adopts a pure Transformer architecture, discarding the convolutional structure, and achieves excellent performance. © 2023 IEEE.

关键词： Image enhancement

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Joint Optimization of Transmission and Computation for Multi-source MEC system Based on Deep Reinforcement Learning 20th

Joint Optimization of Transmission and Computation for Mu...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Zhang, Qi Zhu, Jianhang Gong, Jie School of Computer Science and Engineering Sun Yat-sen University Guangzhou51006 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Guangdong Key Laboratory of Information Security and Technology Sun Yat-sen University Guangzhou51006 China

ISBN: (纸本)9789819628636

Due to the limitation of computing resources in end nodes, computation tasks in Internet of Things (IoT) can be offloaded to mobile edge servers. To measure the effectiveness and timeliness of data, Age of information (AoI) is proposed as a timeliness metric. In this paper, we consider the AoI minimization problem for computation-intensive status update. Our former work has designed optimal policy for Peak AoI (PAoI) optimization in single source case. This paper further studies the average AoI optimization in multi-source case, which is challenging. We focus on a multi-source Mobile Edge Computing (MEC) system and study when to generate a new packet. The system operates in two modes: preemptive and non-preemptive computing server. Firstly, we formulate this problem as a Markov Decision Process (MDP). Then, we adopt Deep Reinforcement Learning (DRL) algorithm to optimize average AoI and PAoI respectively, considering both random transmission and computation time. Simulation results demonstrate that preemptive server gets better performance than non-preemptive server. We further extend the PAoI-optimal policy in single source case to the multi-source case, which is shown to be optimal for minimizing average PAoI with the DRL algorithm approaching its performance. When minimizing average AoI, the DRL algorithm achieves better performance than the PAoI-optimal policy. © IFIP International Federation for information Processing 2025.

关键词： Deep reinforcement learning

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Differential privacy protection method for trip-oriented shared data

Differential privacy protection method for trip-oriented sha...

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作者： Du, Danlei Luo, Entao Yi, Yang Peng, Tao Li, Xubin Zhang, Shaobo Jiang, Xu Wang, Tian School of Information Engineering Hunan University of Science and Engineering YongZhou China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin China Department of Computer Science Northeastern Illinois University ChicagoIL United States School of Computer Science and Engineering Guangzhou University Guangzhou China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Computer Science and Engineering Nanjing University of Science and Technology NanJing China Zhuhai China Zhuhai China

While location information sharing technology provides convenience for unmanned driving and journey navigation, user journey information sharing has also become a disaster for privacy information leakage. The traditional differential privacy method can only perturb the data entirely and cannot consider the design of data availability. In this paper, the difference privacy algorithm is improved by combining it with the Apriori algorithm, and the relevant perturbation is carried out after mining the associated data of the user's trip. In the face of possible data attacks, the privacy protection of the sensitive information of the user's actual data is ensured while the availability of the data is ensured. By testing 3000 trip data generated by experimental simulation, the results show that the correlation information between the original datasets is destroyed. However good availability is maintained after the Laplace data perturbation of the proposed algorithm for both simultaneous and multi-person trips. © 2022 John Wiley & Sons, Ltd.

关键词： Machine learning

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Intelligent Food Identification system Using Improved YOLOv8 13

Intelligent Food Identification System Using Improved YOLOv8

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13th International Symposium on computer science and Educational technology, ISCSET 2024

作者： Nitiavintokana, Francia Rouf, Md Abdur Yu, Xiaoyu Wu, Haibin Wang, Aili Iwahori, Yuji Harbin University of Science and Technology Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin China College of Electron and Information University of Electronic Science and Technology of China Zhongshan Institute Zhongshan China Chubu University Department of Computer Science Aichi Japan

ISBN: (纸本)9798350390803

The field of computer vision has witnessed significant advances in object detection with real-world applications ranging from autonomous driving to surveillance systems. Due to its importance in a number of areas, such as dietary evaluation, food industry automation, and health monitoring, food identification has attracted a lot of interest in this context. The suggested approach makes use of the capabilities of two cutting-edge object recognition models, YOLOv8. Because of its speed and accuracy, YOLOv8 is a great option for real-Time food detection in a variety of settings. Our most recent YOLOv8 model is called BiTrains-YOLOv8. Supervision techniques are applied to improve the combined model's performance even more. This increases overall detection accuracy and helps the algorithm to more effectively generalize to novel food items. Based on the detection results, 96.7% of an average of detection accuracy, 96.4% for precision, 95.7% for recall, and 96.04 % for F1-Score were obtained. The results show that the combination of YOLOv8 and BiTrains-YOLOv8 improved model techniques, significantly improved the performance of food detection, outperforming the previous methods in terms of accuracy, speed and robustness. Furthermore, the paper analyzes potential real-world applications of the system, such as calorie counting, dietary assessment and smart kitchen automation. Implications of advanced intelligent food detection in healthcare, the culinary industry and personalized nutrition are also discussed. In conclusion, the results provide valuable insights for pushing the boundaries of food recognition technology, with promising applications in various fields that promote healthy lifestyles and improve efficiency in food-related processes. © 2024 IEEE.

关键词： Food industry

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Tuning the number of chiral edge channels in a fixed quantum anomalous Hall system

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Physical Review B 2024年第20期109卷 L201402-L201402页

作者： Peng Deng Yulei Han Peng Zhang Su Kong Chong Zhenhua Qiao Kang L. Wang Beijing Academy of Quantum Information Sciences Beijing 100193 China Department of Electrical and Computer Engineering University of California Los Angeles California 90095 USA Department of Physics Fuzhou University Fuzhou Fujian 350108 China International Center for Quantum Design of Functional Materials CAS Key Laboratory of Strongly-Coupled Quantum Matter Physics and Department of Physics University of Science and Technology of China Anhui 230026 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China

Quantum anomalous Hall (QAH) insulators exhibit chiral edge channels characterized by vanishing longitudinal conductance and quantized Hall conductance of Ce2/h, wherein the Chern number C is an integer equal to the number of the parallel chiral edge channels. These chiral edge channels conduct dissipationless transport in QAH insulators, making them pivotal for applications in low-consumption electronics and topological quantum computing. While the QAH effect with multiple chiral edge channels (i.e., C > 1) has been demonstrated in multilayers consisting of magnetic topological insulators and normal insulators, the channel number remains fixed for a given sample. Here, we unveil the tunability of the number of chiral edge channels within a single QAH insulator device. By tuning the magnetization of individual layers within the multilayer system, Chern insulating states with different Chern numbers are unveiled. The tunable Chern number was corroborated by our theoretical calculations. Furthermore, we conducted layer-dependent calculations to elucidate the contribution of the Chern number from different layers in the multilayer. Our findings demonstrate an extra degree of freedom in manipulating the chiral edge channels in QAH insulators. This tunability offers an extra dimension for the implementation of the QAH-based multichannel dissipationless transport.

关键词： Quantum anomalous Hall effect Quantum transport Topological insulators Molecular beam epitaxy

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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Contrastive Learning-Based Generic Audio-to-Lyrics Alignment 21

Contrastive Learning-Based Generic Audio-to-Lyrics Alignment

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International Conference on Big Data and Cloud Computing, 16th IEEE International Conference on Social Computing and Networking and 13th International Conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Huang, Shuai Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business UIniversity Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Hubei Key Laboratory of Distributed System Security Wuhan China Hubei Engineering Research Center on BigData Security Wuhan China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan China

ISBN: (纸本)9798350329223

In this research, we tackled the complex task of aligning audio and lyrics content automatically. This task entails the precise matching of lyrics with the corresponding audio segments in songs, necessitating the coordination of two distinct modalities: text and audio. To surmount this challenge, we employed a contrastive learning approach that generates cross-modal embeddings capable of establishing connections between the audio and text domains. Furthermore, we adopted a hierarchical and cascading design. Initially, the system predicts synchronization at the sentence level and subsequently refines the alignment at the word level. This design equips the system to handle extended sequences effectively, as cross-correlations leverage secondary memory that corresponds to sequence length. Through this innovative approach, we developed a trainable end-to-end system that can harness weakly annotated training data, concurrently learning a robust text model optimized for alignment tasks. Our experiments demonstrated substantial enhancements in average alignment accuracy when utilizing our proposed system, underscoring its robustness compared to previous state-of-the-art models. © 2023 IEEE.

关键词： Alignment

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