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VTensor:Using Virtual Tensors to Build a Layout-Oblivious AI Programming Framework

Journal of computer science & technology 2023年第5期38卷 1074-1097页

作者：俞峰赵家程崔慧敏冯晓兵薛京灵 Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100080China School of Computer Science and Engineering University of New South WalesSydney 1466Australia

Tensors are a popular programming interface for developing artificial intelligence(AI)*** refers to the order of placing tensor data in the memory and will affect performance by affecting data locality;therefore the deep neural network library has a convention on the *** AI applications can use arbitrary layouts,and existing AI systems do not provide programming abstractions to shield the layout conventions of libraries,operator developers need to write a lot of layout-related code,which reduces the efficiency of integrating new libraries or developing new ***,the developer assigns the layout conversion operation to the internal operator to deal with the uncertainty of the input layout,thus losing the opportunity for layout *** on the idea of polymorphism,we propose a layout-agnostic virtual tensor programming interface,namely the VTensor framework,which enables developers to write new operators without caring about the underlying physical layout of *** addition,the VTensor framework performs global layout inference at runtime to transparently resolve the required layout of virtual tensors,and runtime layout-oriented optimizations to globally minimize the number of layout transformation *** results demonstrate that with VTensor,developers can avoid writing layout-dependent *** with TensorFlow,for the 16 operations used in 12 popular networks,VTensor can reduce the lines of code(LOC)of writing a new operation by 47.82%on average,and improve the overall performance by 18.65%on average.

关键词： artificial intelligence(AI)programming layout-oblivious tensor processing

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Automated face recognition using deep learning technique and center symmetric multivariant local binary pattern

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Neural Computing and Applications 2025年第1期37卷 263-281页

作者： Sekhar, J.C. Josephson, P. Joel Chinnasamy, A. Maheswari, M. Sankar, S. Kalangi, Ruth Ramya Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering Malla Reddy Engineering College Telangana Hyderabad India Department of Data Science and Business Systems School of Computing SRMIST Kattankulathur Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vijayawada India

Researchers have recently created several deep learning strategies for various tasks, and facial recognition has made remarkable progress in employing these techniques. Face recognition is a noncontact, nonobligatory, acceptable, and harmonious biometric recognition method with a promising national and social security future. The purpose of this paper is to improve the existing face recognition algorithm, investigate extensive data-driven face recognition methods, and propose a unique automated face recognition methodology based on generative adversarial networks (GANs) and the center symmetric multivariable local binary pattern (CS-MLBP). To begin, this paper employs the center symmetric multivariant local binary pattern (CS-MLBP) algorithm to extract the texture features of the face, addressing the issue that C2DPCA (column-based two-dimensional principle component analysis) does an excellent job of removing the global characteristics of the face but struggles to process the local features of the face under large samples. The extracted texture features are combined with the international features retrieved using C2DPCA to generate a multifeatured face. The proposed method, GAN-CS-MLBP, syndicates the power of GAN with the robustness of CS-MLBP, resulting in an accurate and efficient face recognition system. Deep learning algorithms, mainly neural networks, automatically extract discriminative properties from facial images. The learned features capture low-level information and high-level meanings, permitting the model to distinguish among dissimilar persons more successfully. To assess the proposed technique’s GAN-CS-MLBP performance, extensive experiments are performed on benchmark face recognition datasets such as LFW, YTF, and CASIA-WebFace. Giving to the findings, our method exceeds state-of-the-art facial recognition systems in terms of recognition accuracy and resilience. The proposed automatic face recognition system GAN-CS-MLBP provides a solid basis for a

关键词： Principal component analysis

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A Recursive High Payload Reversible Data Hiding Using Integer Wavelet and Arnold Transform

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Intelligent Automation & Soft Computing 2023年第1期35卷 537-552页

作者： Amishi Mahesh Kapadia P.Nithyanandam School of Computer Science and Engineering Vellore Institute of TechnologyChennai600012TamilnaduIndia

Reversible data hiding is an information hiding technique that requires the retrieval of the error free cover image after the extraction of the secret *** suggested a technique in this research that uses a recursive embedding method to increase capacity substantially using the Integer wavelet transform and the Arnold *** notion of Integer wavelet transforms is to ensure that all coefficients of the cover images are used during embedding with an increase in *** scrambling the cover image,Arnold transform adds security to the information that gets embedded and also allows embedding more information in each *** hybrid combination of Integer wavelet transform and Arnold transform results to build a more efficient and secure *** proposed method employs a set of keys to ensure that information cannot be decoded by an *** experimental results show that it aids in the development of a more secure storage system and withstand few tampering attacks The suggested technique is tested on many image formats,including medical *** performance metrics proves that the retrieved cover image and hidden image are both *** System is proven to withstand rotation attack as well.

关键词： Reversible data hiding(RDH) integer wavelet transforms(IWT) arnold transform payload embedding and extraction

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More than just a name?From magnetic to optical skyrmions and the topology of light

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Light(science & Applications) 2025年第1期14卷 26-29页

作者： Jian Chen Andrew Forbes Cheng-Wei Qiu School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore117583Singapore School of Physics University of the WitwatersrandJohannesburgSouth Africa

Topology is usually perceived intrinsically immutable for a given *** argue that optical topologies do not immediately enjoy such ***'optical skyrmions'as an example,we show that they will exhibit varying textures and topological invariants(skyrmion numbers),depending on how to construct the skyrmion vector when projecting from real to parameter *** demonstrate the fragility of optical skyrmions under a ubiquitous scenario-simple reflection off an optical *** topology is not without benefit,but it must not be assumed.

关键词： numbers space. topology

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Review on distributed denial of service attack detection in software defined network

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International Journal of Wireless and Mobile Computing 2023年第2期25卷 128-146页

作者： Karthika, P. Karmel, A. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

This paper has shown the enhancement of networks, and the ability to withstand DDoS attacks. Thus, this survey plans to make a review on 65 papers which concern DDoS attack detection in SDN. Therefore, the systematic analyses on the proposed method are analysed from each reviewed paper. In addition, the performance metrics and their best achievements in detecting the attack in each research paper are also analysed in this work. In addition, the mitigation technique used in each paper is examined in this work. Also, the chronological assessment and various tools utilised for implementing DDoS attack detection in SDN are considered and reviewed in this work. Finally, the survey depicts numerous research gaps and challenges that are more supportive for researchers to develop novel methods for detecting DDoS attacks in SDN. © 2023 Inderscience Enterprises Ltd.

关键词： Software defined networking

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Heterogeneous Task Allocation Model and Algorithm for Intelligent Connected Vehicles

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computers, Materials & Continua 2024年第9期80卷 4281-4302页

作者： Neng Wan Guangping Zeng Xianwei Zhou School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China School of Mechanical and Transportation Southwest Forestry UniversityKunming650224China

With the development of vehicles towards intelligence and connectivity,vehicular data is diversifying and growing dramatically.A task allocation model and algorithm for heterogeneous Intelligent Connected Vehicle(ICV)applications are proposed for the dispersed computing network composed of heterogeneous task vehicles and Network Computing Points(NCPs).Considering the amount of task data and the idle resources of NCPs,a computing resource scheduling model for NCPs is *** the heterogeneous task execution delay threshold as a constraint,the optimization problem is described as the problem of maximizing the utilization of computing resources by *** proposed problem is proven to be NP-hard by using the method of reduction to a 0-1 knapsack problem.A many-to-many matching algorithm based on resource preferences is *** algorithm first establishes the mutual preference lists based on the adaptability of the task requirements and the resources provided by *** enables the filtering out of un-schedulable NCPs in the initial stage of matching,reducing the solution space *** solve the matching problem between ICVs and NCPs,a new manyto-many matching algorithm is proposed to obtain a unique and stable optimal matching *** simulation results demonstrate that the proposed scheme can improve the resource utilization of NCPs by an average of 9.6%compared to the reference scheme,and the total performance can be improved by up to 15.9%.

关键词： Task allocation intelligent connected vehicles dispersed computing matching algorithm

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ARCHER:a ReRAM-based accelerator for compressed recommendation systems

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Frontiers of computer science 2024年第5期18卷 147-160页

作者： Xinyang SHEN Xiaofei LIAO Long ZHENG Yu HUANG Dan CHEN Hai JIN 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

Modern recommendation systems are widely used in modern data *** random and sparse embedding lookup operations are the main performance bottleneck for processing recommendation systems on traditional platforms as they induce abundant data movements between computing units and ***-based processing-in-memory(PIM)can resolve this problem by processing embedding vectors where they are ***,the embedding table can easily exceed the capacity limit of a monolithic ReRAM-based PIM chip,which induces off-chip accesses that may offset the PIM ***,we deploy the decomposed model on-chip and leverage the high computing efficiency of ReRAM to compensate for the decompression performance *** this paper,we propose ARCHER,a ReRAM-based PIM architecture that implements fully yon-chip recommendations under resource ***,we make a full analysis of the computation pattern and access pattern on the decomposed *** on the computation pattern,we unify the operations of each layer of the decomposed model in multiply-and-accumulate *** on the access observation,we propose a hierarchical mapping schema and a specialized hardware design to maximize resource *** the unified computation and mapping strategy,we can coordinatethe inter-processing elements *** evaluation shows that ARCHER outperforms the state-of-the-art GPU-based DLRM system,the state-of-the-art near-memory processing recommendation system RecNMP,and the ReRAM-based recommendation accelerator REREC by 15.79×,2.21×,and 1.21× in terms of performance and 56.06×,6.45×,and 1.71× in terms of energy savings,respectively.

关键词： recommendation system ReRAM processing-in-memory embedding layer

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Source localization in signed networks with effective distance

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Chinese Physics B 2024年第2期33卷 577-585页

作者：马志伟孙蕾丁智国黄宜真胡兆龙 Zhejiang Normal University School of Computer Science and TechnologyJinhua 321004China Shanghai Business School School of Business and EconomicsShanghai 200235China Jinhua Polytechnic School of Information EngineeringJinhua 321016China

While progress has been made in information source localization,it has overlooked the prevalent friend and adversarial relationships in social *** paper addresses this gap by focusing on source localization in signed network *** the topological characteristics of signed networks and transforming the propagation probability into effective distance,we propose an optimization method for observer ***,by using the reverse propagation algorithm we present a method for information source localization in signed *** experimental results demonstrate that a higher proportion of positive edges within signed networks contributes to more favorable source localization,and the higher the ratio of propagation rates between positive and negative edges,the more accurate the source localization ***,this aligns with our observation that,in reality,the number of friends tends to be greater than the number of adversaries,and the likelihood of information propagation among friends is often higher than among *** addition,the source located at the periphery of the network is not easy to ***,our proposed observer selection method based on effective distance achieves higher operational efficiency and exhibits higher accuracy in information source localization,compared with three strategies for observer selection based on the classical full-order neighbor coverage.

关键词： complex networks signed networks source localization effective distance

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Leveraging EfficientNetB3 in a Deep Learning Framework for High-Accuracy MRI Tumor Classification

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computers, Materials & Continua 2024年第10期81卷 867-883页

作者： Mahesh Thyluru Ramakrishna Kuppusamy Pothanaicker Padma Selvaraj Surbhi Bhatia Khan Vinoth Kumar Venkatesan Saeed Alzahrani Mohammad Alojail Department of Computer Science&Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bengaluru562112India School of Computer Science and Engineering VIT-AP UniversityAmaravati522241India Department of Computer Science&Technology Madanapalle Institute of Technology&ScienceMadanapalle517325India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK School of Computer Science Engineering&Information Systems(SCORE) Vellore Institute of Technology(VIT)Vellore632014India Management Information System Department College of Business AdministrationKing Saud UniversityRiyadh11531Saudi Arabia Adjunct Research Faculty Centre for Research Impact&OutcomeChitkara UniversityRajpuraPunjab140401India

Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient *** different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI *** approaches predominantly rely on traditional machine learning and basic deep learning methods for image *** methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI *** the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor *** approach highlights a major advancement in employing sophisticated machine learning techniques within computer science and engineering,showcasing a highly accurate framework with significant potential for healthcare *** model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification *** successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current *** integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider *** research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.

关键词： Deep learning MRI brain tumor cassification EfficientNetB3 computational engineering healthcare technology artificial intelligence in medical imaging tumor segmentation neuro-oncology

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A telerobotic shared control architecture for learning and generalizing skills in unstructured environments

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IEEE Transactions on Automation science and engineering 2025年 22卷 15953-15965页

作者： Shao, Xingmao Xie, Lun University of Science and Technology Beijing School of Computer and Communication Engineering Beijing 100083 China

Direct teleoperation of robots in unstructured environments by non-experts often leads to low efficiency and increased risk. To this end, this paper proposes a shared control architecture where the robot can generalize demonstrations based on variable environments (start, obstacles, and goal positions) and infer user intention online to assist tasks safely and efficiently. First, the complex task is decomposed into unit actions, where the cubic-quintic-cubic Bezier curve is utilized to resolve the limitation of the classical dynamic movement primitives (DMP) algorithm in generalizing via-points. Gaussian process regression (GPR) was implemented to generalize expert demonstrations to different environments, ensuring full generalizable trajectory in the subtask space. GPR further extrapolate simple demonstrations from a subspace to the entire task space, avoiding the demand for numerous original demonstrations. Then, the online evolution of DMP is optimized: 1) an adaptive temporal scaling system is developed to synchronize evolution with human operations;2) an intention prediction and expected evolution selection method based on operational input is proposed, achieving human-led operation guidance. Experiments validate the effectiveness of the generalization, obstacle avoidance, and operation assistance. Tests with non-experts reported the designed architecture enhances safety and efficiency, and reduces collisions. © 2004-2012 IEEE.

关键词： Bezier curve dynamic movement priviments Gaussian process regression intention prediction learning from demonstration Shared control

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