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NeurDB: an AI-powered autonomous data system

Science China(Information Sciences) 2024年第10期67卷 129-150页

作者： Beng Chin OOI Shaofeng CAI Gang CHEN Yanyan SHEN Kian-Lee TAN Yuncheng WU Xiaokui XIAO Naili XING Cong YUE Lingze ZENG Meihui ZHANG Zhanhao ZHAO School of Computing National University of Singapore College of Computer Science and Technology Zhejiang University Department of Computer Science and Engineering Shanghai Jiao Tong University School of Information Renmin University of China School of Computer Science and Technology Beijing Institute of Technology

In the wake of rapid advancements in artificial intelligence(AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB(AI×DB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, and selfdriving capabilities for improved system performance. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

关键词： AI$\times$DB in-database AI intelligent data system

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Resource investment for DDoS attack resistant SDN: a practical assessment

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Science China(Information Sciences) 2023年第7期66卷 112-129页

作者： Bin YUAN Fan ZHANG Jun WAN Huan ZHAO Shui YU Deqing ZOU Qiangsheng HUA Hai JIN School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science University of Technology Sydney

Software-defined networks(SDNs) present a novel network architecture that is widely used in various datacenters. However, SDNs also suffer from many types of security threats, among which a distributed denial of service(DDoS) attack, which aims to drain the resources of SDN switches and controllers,is one of the most common. Once the switch or controller is damaged, the network services can be *** defense schemes against DDoS attacks have been proposed from the perspective of attack detection;however, such defense schemes are known to suffer from a time consuming and unpromising accuracy, which could result in an unavailable network service before specific countermeasures are taken. To address this issue through a systematic investigation, we propose an elaborate resource-management mechanism against DDoS attacks in an SDN. Specifically, by considering the SDN topology, we leverage the M/M/c queuing model to measure the resistance of an SDN to DDoS attacks. Network administrators can therefore invest a reasonable number of resources into SDN switches and SDN controllers to defend against DDoS attacks while guaranteeing the quality of service(QoS). Comprehensive analyses and empirical data-based experiments demonstrate the effectiveness of the proposed approach.

关键词： SDN security DDoS attacks resource management queueing theory QoS

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When Embodied AI Meets Industry 5.0:Human-Centered Smart Manufacturing

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 485-501页

作者： Jing Xu Qiyu Sun Qing-Long Han Yang Tang IEEE the Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology the School of Science Computing and Engineering Technologies Swinburne University of Technology

As embodied intelligence(EI), large language models(LLMs), and cloud computing continue to advance, Industry5.0 facilitates the development of industrial artificial intelligence(Ind AI) through cyber-physical-social systems(CPSSs) with a human-centric focus. These technologies are organized by the system-wide approach of Industry 5.0, in order to empower the manufacturing industry to achieve broader societal goals of job creation, economic growth, and green production. This survey first provides a general framework of smart manufacturing in the context of Industry 5.0. Wherein, the embodied agents, like robots, sensors, and actuators, are the carriers for Ind AI, facilitating the development of the self-learning intelligence in individual entities, the collaborative intelligence in production lines and factories(smart systems), and the swarm intelligence within industrial clusters(systems of smart systems). Through the framework of CPSSs, the key technologies and their possible applications for supporting the single-agent, multi-agent and swarm-agent embodied Ind AI have been reviewed, such as the embodied perception, interaction, scheduling, multi-mode large language models, and collaborative training. Finally, to stimulate future research in this area, the open challenges and opportunities of applying Industry 5.0 to smart manufacturing are identified and discussed. The perspective of Industry 5.0-driven manufacturing industry aims to enhance operational productivity and efficiency by seamlessly integrating the virtual and physical worlds in a human-centered manner, thereby fostering an intelligent, sustainable, and resilient industrial landscape.

关键词： Embodied AI human-centered manufacturing Industry 5.0 internet of things large multi-mode language models

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ReCSA:a dedicated sort accelerator using ReRAM-based content addressable memory

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Frontiers of Computer Science 2023年第2期17卷 1-13页

作者： Huize LI Hai JIN Long ZHENG Yu HUANG Xiaofei LIAO National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

With the increasing amount of data,there is an urgent need for efficient sorting algorithms to process large data *** sorting algorithms have attracted much attention because they can take advantage of different hardware's *** the traditional hardware sort accelerators suffer“memory wall”problems since their multiple rounds of data transmission between the memory and the *** this paper,we utilize the in-situ processing ability of the ReRAM crossbar to design a new ReCAM array that can process the matrix-vector multiplication operation and the vector-scalar comparison in the same array *** this designed ReCAM array,we present ReCSA,which is the first dedicated ReCAM-based sort *** hardware designs,we also develop algorithms to maximize memory utilization and minimize memory exchanges to improve sorting *** sorting algorithm in ReCSA can process various data types,such as integer,float,double,and *** also present experiments to evaluate the performance and energy efficiency against the state-of-the-art sort *** experimental results show that ReCSA has 90.92×,46.13×,27.38×,84.57×,and 3.36×speedups against CPU-,GPU-,FPGA-,NDP-,and PIM-based platforms when processing numeric data *** also has 24.82×,32.94×,and 18.22×performance improvement when processing string data sets compared with CPU-,GPU-,and FPGA-based platforms.

关键词： ReCAM parallel sorting architecture design processing-in-memory

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Transfer learning based epileptic seizure classification using scalogram images of EEG signals

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Multimedia Tools and Applications 2024年第36期83卷 84179-84193页

作者： Pattnaik, Sasweta Rao, B. Nageswara Rout, Nirmal Kumar Sabut, Sukanta Kumar Sensing & amp Computing Lab School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India

Epilepsy is a common neurological disorder that occurs due to an abnormality of the nerve cells in the brain. Electroencephalogram (EEG) analysis is one of the most vital tools used to detect seizure events. Normally, EEG analysis is done manually by expert neurologists, which is time-consuming, tedious and error-prone, hence an automatic detection approach based on deep learning (DL) is essential in the dignosis process. In this study, we focused on automated epileptic seizure classification using a transfer learning. A pre-trained network ResNet50 is used for the feature extraction and classification using a deep neural network (dense layer) on 2D scalogram images of EEG signal. The time-frequency scalogram images were generated using continuous wavelet transform (CWT) from EEG signals collected from the CHBMIT scalp EEG database. The method was evaluated in terms of accuracy, sensitivity, specificity and computational time. The results are compared with the tested results of standard pre-trained models such as VGG16 and InceptionV3 which are also implemented in this study to classify the EEG events. The proposed approach achieved a best classification with an accuracy of 95.23%, sensitivity of 99.54%, and specificity of 90.28% respectively, which is better than the result obtained from the VGG16 and InceptionV3 networks. The training time for ResNet50 + DNN, VGG16, InceptionV3 are 2340, 2520, and 1440 s respectively, which indicates the computational complexity is more in transfer learning but classification accuracy is better than the standard models. Therefore, ResNet50 based transfer learning using 2D-scalogram images of EEG signals has evidently proved to be a better choice to a neurologist for fast anticipation of epileptic seizure. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Neurons

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Effortless learning: leveraging summarization and tabulation for automated note making

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Multimedia Tools and Applications 2024年 1-26页

作者： Arivarasi, A. Srinivasan, R. Rajeswari, D. Govindasamy, Alagiri School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai600127 India Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Department of Data Science and Business Systems School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur603203 India Future Connect Technologies Pvt. Ltd Tamil Nadu Kanchipuram600034 India

In this fast-paced Digital Age, Natural Language Processing (NLP) can prove beneficial in consuming quality information efficiently. With the ever-growing number of learning resources, it is becoming onerous for students to choose the best-suited learning resource. Furthermore, many students find it difficult to understand and retain highly factual information. To overcome these issues, this paper proposes a novel Automated TExt Summarization of scholastic text in Table format (Auto-TEST) to provide information at a glance by mimicking the student’s style of note-making. The Auto-TEST framework is achieved through two steps which is Summarization and Tabulation. Initially, the Extractive Single Document Text Summarization (ESDTS) method is adopted to identify the salient information from the non-fictional (scholastic) text which is extracted and grouped together to form a concise summary. A Named Entity Recognition (NER) method is implemented to extract noun chunks and then arrange them in a tabulation format that aids in memorizing essential facts and figures by enabling effortless retrieval. The performance of the Auto-TEST framework is gauged by using scholastic domain-specific dataset using ROUGE as an evaluation metric. The extracted entities gives an average Recall of 71.40%, an average Precision score of 75.80% and an average F-score of 73.54% for the dataset respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Students

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Towards High-Performance Graph Processing: From a Hardware/Software Co-Design Perspective

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Journal of Computer Science & technology 2024年第2期39卷 245-266页

作者：廖小飞赵文举金海姚鹏程黄禹王庆刚赵进郑龙张宇邵志远 National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China

Graph processing has been widely used in many scenarios,from scientific computing to artificial *** processing exhibits irregular computational parallelism and random memory accesses,unlike traditional ***,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a significantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a specialized single-thread graph *** domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software *** article presents a graph processing ecosystem from hardware to *** start by introducing a series of hardware accelerators as the foundation of this ***,the codesigned parallel graph systems and their distributed techniques are presented to support graph ***,we introduce our efforts on novel graph applications and hardware *** results show that various graph applications can be efficiently accelerated in this graph processing ecosystem.

关键词： graph processing hardware accelerator software system high performance ecosystem

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Editorial:Secure and Safe Meta Control for Cyber Physical Systems

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IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2177-2178页

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

Welcome to the twelfth issue of 2023 in the IEEE/CAA Journal of Automatica Sinica(JAS).In the sixth issue of2023,I systematically addressed the latest development of Meta Vehicles,and sorted out some important contributions published in the IEEE/CAA JAS focusing on control,estimation,and optimization of automated vehicles with reliability,security,efficiency,and intelligence.

关键词： Secure automated estimation

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CENTRAL LIMIT THEOREM FOR TEMPORAL AVERAGE OF BACKWARD EULER-MARUYAMA METHOD

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Journal of Computational Mathematics 2025年第3期43卷 588-614页

作者： Diancong Jin School of Mathematics and Statistics Hubei Key Laboratory of Engineering Modeling and Scientific ComputingHuazhong University of Science and TechnologyWuhan 430074China

This work focuses on the temporal average of the backward Euler-Maruyama(BEM)method,which is used to approximate the ergodic limit of stochastic ordinary differential equations(SODEs).We give the central limit theorem(CLT)of the temporal average of the BEM method,which characterizes its asymptotics in *** the deviation order is smaller than the optimal strong order,we directly derive the CLT of the temporal average through that of original equations and the uniform strong order of the BEM *** the case that the deviation order equals to the optimal strong order,the CLT is established via the Poisson equation associated with the generator of original *** experiments are performed to illustrate the theoretical *** main contribution of this work is to generalize the existing CLT of the temporal average of numerical methods to that for SODEs with super-linearly growing drift coefficients.

关键词： Central limit theorem Temporal average Ergodicity Backward Euler-Maruyama method

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Adaptive whale optimization based clustering method for K- anonymization in social networks

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Multimedia Tools and Applications 2024年 1-21页

作者： Sivasankari, K. Maheswari, K. M. Uma Department of CSE SRM Institute of Science and Technology Ramapuram Chennai600089 India Department of computing technologies School of computing College of engineering and technology SRM Institute of science and technology Kattankulathur603203 India

The usage of online social networks (OSNs) has been significantly swelling over the years. Data from SNs has been shared publicly for the purpose of deeper understanding user behaviour and data mining tasks. However, securing individual identification from the publicly published data is the primary issue in sharing social network databases. K-anonymization is a well-known technique for preventing individual identification of publically available datasets. According to this method, each record in the anonymized dataset must be identical to at least k-1 other records in terms of a set of privacy-related attributes. Although anonymizing the original dataset to meet the k-anonymity criteria is straightforward, the anonymized dataset must maintain as much information from the original dataset as possible. Recently, clustering methods have been used to apply K-anonymization to the social dataset successfully. This paper applies a swarm-based clustering technique, called adaptive whale optimization algorithm (AWOA) for clustering social data. The WOA algorithm replicates humpback whale behavior and employs fewer internal parameters to support a better balance of exploitation and exploration. The method is tested on three publicly available data sets: Yelp and Facebook dataset. The experimental findings show that the proposed method attained the minimum information loss of 11% for Yelp dataset and 10% for facebook dataset. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Anonymity

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