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Path signature-based XAI-enabled network time series classification

Science China(Information Sciences) 2024年第7期67卷 91-106页

作者： Le SUN Yueyuan WANG Yongjun REN Feng XIA department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science and Technology School of computing technologies Royal Melbourne Institute of Technology

Classifying network time series(NTS) is crucial for automating network administration and ensuring cyberspace security. It enables the detection of anomalies, the identification of network attacks,and the monitoring of performance issues, thereby providing valuable support for network protection and optimization. However, modern communication networks pose challenges for NTS classification *** include handling large-scale and complex NTS data, extracting features from intricate datasets, and addressing explainability requirements. These challenges are particularly pronounced for complex 5G networks. Notably, explainability has become crucial for the widespread deployment of network automation for 5G networks and beyond. To tackle these challenges, we propose a path-signature-based NTS classification model called recurrent signature(Recur Sig). This innovative model is designed to overcome the time-consuming feature selection problem by utilizing deep-learning(DL) techniques. Additionally, it provides a solution for addressing the black-box issue associated with DL models in network automation systems(NAS) by incorporating an explainable classification approach. Extensive experimentation on various public datasets demonstrates that Recur Sig outperforms existing models in accuracy and explainability. The results indicate its potential for application in cyberspace security and automated network management, offering an explainable solution for network protection and optimization.

关键词： network time series classification explainable artificial intelligence path signature automated network management recurrent neural network

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Physical Implementation of the Eight-Core Godson-3B Microprocessor

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Journal of Computer Science & Technology 2011年第3期26卷 520-527页

作者：王茹范宝峡杨梁高燕萍刘动肖斌王江嵋张译夫王宏胡伟武 Institute of computing Technology Chinese Academy of Sciences Graduate School of Chinese Academy of Sciences Loongson technologies Corporation Limited

The Godson-3B processor is a powerful processor designed for high performance servers including Dawning Servers. It offers significantly improved performance over previous Godson-3 series CPUs by incorporating eight CPU cores and vector computing units. It contains 582.6 M transistors within 300 mm2 area in 65 nm technology and is implemented in parallel with full hierarchical design flows. In Godson-3B, advanced clock distribution mechanisms including GALS （Globally Asynchronous Locally Synchronous） and clock mesh are adopted to obtain an OCV tolerable clock network. Custom-designed de-skew modules are also implemented to afford further latency balance after fabrication. The power reduction of Godson- 3B is maintained by MLMM （Multi Level Multi Mode） clock gating and multi-threshold-voltage cells substitution schemes. The highest frequency of Godson-3B is 1.05 GHz and the peak performance is 128 GFlops （double-precision） or 256 GFlops （single-precision） with 40 W power consumption.

关键词： physical implementation hierarchical design flow GALS clock mesh low power

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Novel Homomorphic Encryption for Mitigating Impersonation Attack in Fog computing

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Intelligent Automation & Soft computing 2023年第2期35卷 2015-2027页

作者： V.Balaji P.Selvaraj department of computing technologies College of Engineering and TechnologyFaculty of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203TamilnaduIndia

Fog computing is a rapidly growing technology that aids in pipelining the possibility of mitigating breaches between the cloud and edge *** facil-itates the beneﬁts of the network edge with the maximized probability of offering interaction with the ***,the fog computing characteristics are suscep-tible to counteract the challenges of *** issues present with the Physical Layer Security(PLS)aspect in fog computing which included authentication,integrity,and conﬁdentiality has been considered as a reason for the potential issues leading to the security *** this work,the Octonion Algebra-inspired Non-Commutative Ring-based Fully Homomorphic Encryption Scheme(NCR-FHE)was proposed as a secrecy improvement technique to overcome the impersonation attack in cloud *** proposed approach was derived through the beneﬁts of Octonion algebra to facilitate the maximum security for big data-based *** major issues in the physical layer security which may potentially lead to the possible security issues were identiﬁ*** potential issues causing the impersonation attack in the Fog computing environment were identiﬁ*** proposed approach was compared with the existing encryption approaches and claimed as a robust approach to identify the impersonation attack for the fog and edge *** computation cost of the proposed NCR-FHE is identiﬁed to be signiﬁcantly reduced by 7.18%,8.64%,9.42%,and 10.36%in terms of communication overhead for varying packet sizes,when compared to the benchmarked ECDH-DH,LHPPS,BF-PHE and SHE-PABF schemes.

关键词： Fog computing physical layer security non-commutative ring-based fully homomorphic encryption impersonation attack

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Hybrid Algorithm-Driven Smart Logistics Optimization in IoT-Based Cyber-Physical Systems

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Computers, Materials & Continua 2023年第12期77卷 3921-3942页

作者： Abdulwahab Ali Almazroi Nasir Ayub College of computing and Information Technology at Khulais Department of Information TechnologyUniversity of JeddahJeddah21959Saudi Arabia department of Creative technologies Air University IslamabadIslamabad44000Pakistan

Effectively managing complex logistics data is essential for development sustainability and growth,especially in optimizing distribution *** article addresses the limitations of current logistics path optimization methods,such as inefficiencies and high operational *** overcome these drawbacks,we introduce the Hybrid Firefly-Spotted Hyena Optimization(HFSHO)algorithm,a novel approach that combines the rapid exploration and global search abilities of the Firefly Algorithm(FO)with the localized search and region-exploitation skills of the Spotted Hyena Optimization Algorithm(SHO).HFSHO aims to improve logistics path optimization and reduce operational *** algorithm’s effectiveness is systematically assessed through rigorous comparative analyses with established algorithms like the Ant Colony Algorithm(ACO),Cuckoo Search Algorithm(CSA)and Jaya Algo-rithm(JA).The evaluation also employs benchmarking methodologies using standardized function sets covering diverse objective functions,including Schwefel’s,Rastrigin,Ackley,Sphere and the ZDT and DTLZ Function *** outperforms these algorithms,achieving a minimum path distance of 546 units,highlighting its prowess in logistics path *** comprehensive evaluation authenticates HFSHO’s exceptional performance across various logistic optimization *** findings emphasize the critical significance of selecting an appropriate algorithm for logistics path navigation,with HFSHO emerging as an efficient *** the synergistic use of FO and SHO,HFSHO achieves a 15%improvement in convergence,heightened operational efficiency and substantial cost reductions in logistics *** presents a promising solution for optimizing logistics paths,offering logistics planners and decision-makers valuable insights and contributing substantively to sustainable sectoral growth.

关键词： Hybrid optimization internet of things intelligent transport system optimal path finding smart logistics traffic congestion supply chain

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Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

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Computer Systems Science & Engineering 2023年第9期46卷 3883-3899页

作者： Amarendra Reddy Panyala M.Baskar department of Computer Science and Engineering School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia department of Information Technology MLR Institute of TechnologyHyderabad500043TelanganaIndia department of computing technologies School of ComputingSRM Institute of Science and TechnologyKattankulathurChengalpattuChennai603203TamilnaduIndia

The deep learning models are identified as having a significant impact on various *** same can be adapted to the problem of brain tumor ***,several deep learning models are presented earlier,but they need better classification *** efficient Multi-Feature Approximation Based Convolution Neural Network(CNN)model(MFACNN)is proposed to handle this *** method reads the input 3D Magnetic Resonance Imaging(MRI)images and applies Gabor filters at multiple *** noise-removed image has been equalized for its quality by using histogram ***,the features like white mass,grey mass,texture,and shape are extracted from the *** features are trained with deep learning Convolution Neural Network(CNN).The network has been designed with a single convolution layer towards dimensionality *** texture features obtained from the brain image have been transformed into a multi-dimensional feature matrix,which has been transformed into a single-dimensional feature vector at the convolution *** neurons of the intermediate layer are designed to measure White Mass Texture Support(WMTS),GrayMass Texture Support(GMTS),WhiteMass Covariance Support(WMCS),GrayMass Covariance Support(GMCS),and Class Texture Adhesive Support(CTAS).In the test phase,the neurons at the intermediate layer compute the support as mentioned above values towards various classes of *** on that,the method adds a Multi-Variate Feature Similarity Measure(MVFSM).Based on the importance ofMVFSM,the process finds the class of brain image given and produces an efficient result.

关键词： CNN deep learning brain tumor classification MFA-CNN MVFSM 3D MRI texture Gabor

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浅陷阱态调控增强绿色发光钙钛矿微球的负热猝灭效应（英文）

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Science China Materials 2025年第1期 149-155页

作者：林圳旭黄锐宋捷张毅林泽文李洪亮吴海霞侯得健郭艳青王静朱剑豪 School of Materials Science and Engineering Hanshan Normal University Ministry of Education Key Laboratory of Bioinorganic and Synthetic Chemistry State Key Laboratory of Optoelectronic Materials and Technologies Sun YatSen University department of Physics Department of Materials Science and Engineering and Department of Biomedical Engineering City University of Hong Kong

发光材料中的负热猝灭（NTQ）现象,表现为随温度升高发光强度增加,在照明和显示技术中具有广泛应用前景.然而,在金属卤化物钙钛矿中实现NTQ具有一定挑战性,其机制尚不完全清楚.本研究通过低温光致发光、长余辉和热释光谱技术,系统研究了...

发光材料中的负热猝灭（NTQ）现象,表现为随温度升高发光强度增加,在照明和显示技术中具有广泛应用前景.然而,在金属卤化物钙钛矿中实现NTQ具有一定挑战性,其机制尚不完全清楚.本研究通过低温光致发光、长余辉和热释光谱技术,系统研究了CsPbBr3微球中NTQ现象的起源.结果表明, NTQ现象与浅陷阱态中载流子的释放密切相关.实验与理论分析揭示,这些浅缺陷态的能级位于导带下方约0.135 eV处.快速热处理可显著增加浅陷阱的密度,从而增强NTQ效应,使室温下的光致发光强度较150 K时提高了60%以上.同时,该处理将受激自发辐射的阈值降低至约45 W/cm2.本研究不仅加深了对CsPbBr3微球中NTQ机制的理解,也为通过能态调控提升钙钛矿光电子器件性能开辟了新途径.

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Editorial:Driving into Future with Reliable,Secure,Efficient and Intelligent MetaVehicles

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IEEE/CAA Journal of Automatica Sinica 2023年第6期10卷 1355-1356页

作者： Qing-Long Han School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourneVIC 3122Australia

Welcome to the sixth issue of the IEEE/CAA Journal of Automatica Sinica(JAS)in 2023!In the first issue in 2023,I briefly discussed about MetaVehicles,a technological expression of intelligent vehicles,robotics,AI,blockchain,knowledge automation,networking and communication[1].As the concept of the Metaverse continues to evolve and expand,the range of vehicle entities is diversely enlarged,including passenger vehicles,heavy duty vehicles,rail vehicles,marine vehicles,aerial vehicles,and other types of *** development and use of MetaVehicles are likely to become more ***,from a systems and control perspective,new methods of control,estimation and optimization should be developed to achieve the improved vehicular control system stability and reliability in the physical space.

关键词： estimation likely evolve

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Few-shot learning for screening 2D Ga2CoS4-x supported single-atom catalysts for hydrogen production

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Journal of Energy Chemistry 2025年第1期100卷 665-673页

作者： Nabil Khossossi Poulumi Dey department of Mechanical Engineering (ME) Delft University of Technology

Hydrogen generation and related energy applications heavily rely on the hydrogen evolution reaction（HER）,which faces challenges of slow kinetics and high *** electrocatalysts,particularly single-atom catalysts （SACs） on two-dimensional （2D） materials,are *** study presents a few-shot machine learning （ML） assisted high-throughput screening of 2D septuple-atomic-layer Ga2CoS4-xsupported SACs to predict HER catalytic ***,density functional theory （DFT）calculations showed that 2D Ga2CoS4is inactive for ***,defective Ga2CoS4-x（x=0–0.25）monolayers exhibit excellent HER activity due to surface sulfur vacancies （SVs）,with predicted overpotentials （0–60 mV） comparable to or lower than commercial Pt/C,which typically exhibits an overpotential of around 50 m V in the acidic electrolyte,when the concentration of surface SV is lower than 8.3%.SVs generate spin-polarized states near the Fermi level,making them effective HER *** demonstrate ML-accelerated HER overpotential predictions for all transition metal SACs on 2D *** DFT data from 18 SACs,an ML model with high prediction accuracy and reduced computation time was *** intrinsic descriptor linking SAC atomic properties to HER overpotential was *** study thus provides a framework for screening SACs on 2D materials,enhancing catalyst design.

关键词： Hydrogen production Electrocatalyst 2D material Density functional theory Machine learning Surface sulfur vacancy

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Optimal Formation Problems of Robots by the Lagrangian Multiplier Method 36

Optimal Formation Problems of Robots by the Lagrangian Multi...

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第36届中国控制会议

作者： Jun Liu Xiaoning Ma Ping Li Tianyun Shi Institute of computing technologies Chinese Academy of Railway Sciences

ISBN: (纸本)9781538629185

Optimal formation of robots is one of the most important and interesting topics in the fields of robotics,multi-agents,intelligent control and evolutionary computation,*** objective of this paper is mainly to discuss the formation problems by solving the Lagrangian method when the number of robots is only equal to 3 and the number of constraints is larger than *** considered objective function is selected to be the sum square distance from points in the initial shape to points in the desired *** importantly,two theorems and the analytic solution on three typical cases are provided to analyze and discuss the typical formation problems with three ***,one theorem is to highlight that the center of the initial shape is equal to that of the desired shape,and the corresponding theorem can improve the efficiency and reduce the optimization dimension of formation problem.

关键词： Optimal formation Lagrangian method Objective function Many constraints

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A Buffer Management Scheme for Packet Queues in MANET

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Tsinghua Science and Technology 2013年第6期18卷 543-553页

作者： Muhammad Aamir Mustafa A.Zaidi department of Technology Support National Bank department of computing SZABIST

We introduce a new scheme of buffer management to handle packet queues in Mobile Ad hoc Networks （MANETs） for fixed and mobile nodes. In this scheme, we try to achieve efficient queuing in the buffer of a centrally communicating MANET node through an active queue management strategy by assigning dynamic buffer space to all neighboring nodes in proportion to the number of packets received from neighbors and hence controlling packet drop probabilities. Our simulation study reveals that the proposed scheme is a way to improve the buffer management for packet queues in MANET nodes in terms of packet loss ratio, transmission efficiency, and some other important system parameters.

关键词： Active Queue Management (AQM) Mobile Ad hoc Network (MANET) queue management packet drop

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