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51st International Conference on Very Large Data Bases, VLDB 2025

作者： Zhang, Shijie Cheng, Ru Liu, Xinpeng Xiao, Jiang Jin, Hai Li, Bo National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

Increasingly popular decentralized applications (dApps) with complex application logic incur significant overhead for executing smart contract transactions, which greatly limits public block chain performance. Pre-executing transactions off the critical path can mitigate substantial I/O and computation costs during execution. However, pre-execution does not yield any state transitions, rendering the system state inconsistent with actual execution. This inconsistency can lead to deviations in pre-execution paths when processing smart contracts with multiple state-related branches, thus diminishing pre-execution effectiveness. In this paper, we develop Seer, a novel public block chain execution engine that incorporates fine-grained branch prediction to fully exploit pre-execution effectiveness. Seerpredicts state-related branches using a two-level prediction approach, reducing inconsistent execution paths more efficiently than executing all possible branches. To enable effective reuse of pre execution results, Seer employs checkpoint-based fast-path execution, enhancing transaction execution for both successful and unsuccessful predictions. Evaluations with realistic block chain workloads demonstrate that Seer delivers an average of 27.7× transaction-level speedup and an overall 20.6× speedup in the execution phase over vanilla Ethereum, outperforming existing block chain execution acceleration solutions. © 2025, VLDB Endowment, All rights reserved.

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Backup subscription scheme for differential protection using IEC61850-9-2 sampled values

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Energy Informatics 2024年第1期7卷 110页

作者： Katoulaei, Mohammad Khalili Rahmani, Aamir Høidalen, Hans Kristian Oleinikova, Irina Mork, Bruce Department of energy systems Norwegian University of Science and Technology Trondheim Norway Department of electrical power system Michigan Technical University Michigan United States Department of Electrical Power Engineering Norwegian University of Science and Technology Trondheim Norway Department of Electrical and Computer Engineering Michigan Technological University HoughtonMI49931 United States

In IEC-61850-based digital substations, the protection IED’s performance is dependent on merging unit’s vendor implementation, communication networks, and measurement circuit’s health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection. © The Author(s) 2024.

关键词： Hardware-in-the-loop simulation

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Optimization Accuracy of MRI Brain Image Based Tumor Detection Using Deep Learning LSTM Technique 2

Optimization Accuracy of MRI Brain Image Based Tumor Detecti...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Chugh, Aarti Yadav, Ranjeet Kumar Agarwal, Tushar School of Computer Science and Engineering IILM University Gurugram India Shree Guru Gobind Singh Tricentenary University Department of Computer Science and Engineering Gurugram India The Galaxy Education System Gujrat India

ISBN: (纸本)9798331519056

Benign brain tumors (BT) are the least dangerous type and arise from brain cells or cells surrounding the brain. Benign tumors grow slowly and do not spread to other body parts. Benign brain tumors have distinct borders. They put the normal brain under stress and interfere with physical and mental function. It is also referred to as a non-cancerous BT. The acquisition of magnetic resonance images introduces noise, and removing this noise is complex. BT has tentacles and diffuse structures, making precise segmentation difficult. Another critical task is to select and extract the best features and the appropriate number of training and test samples to improve the classification. Deep learning (DL) models have received much attention because feature learning happens automatically. However, it necessitates a substantial amount of computing power and memory. As a result, lightweight models with high accuracy and fast computation times were developed. This paper is present on ML and DL model for analysis of BT detection and calculated accuracy. The ML classification i.e. K-NN achieves 78.74% accuracy, LR achieves 81.53% accuracy, VC achieves 86.9% accuracy and DL LSTM achieves 94.07% accuracy. It is clearly that the LSTM technique is provides good accuracy compared to other ML technique. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Arabic Abstractive Summarization Using the Multilingual T5 Model 6

Arabic Abstractive Summarization Using the Multilingual T5 M...

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6th International Conference on Computing and Informatics, ICCI 2024

作者： Karim, Abdelrhman A. Usama, Mohammed Ibrahim, Manar A. Hatem, Youssef Wael, Waleed Mazrua, Ahmad T. El-Monayer, Ghada K. Elbanhawy, Magy Foad, Khaled Moawad, Ibrahim F. Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Artificial Intelligence Science Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9798350373875

Natural language processing (NLP) is a branch of artificial intelligence (Al) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual TS (MTS) model AASMTS to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MTS architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MTS. © 2024 IEEE.

关键词： Deep neural networks

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Forecasting Mental Stress Using Machine Learning Algorithms

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computers, Materials & Continua 2022年第9期72卷 4945-4966页

作者： Elias Hossain Abdulwahab Alazeb Naif Almudawi Sultan Almakdi Mohammed Alshehri M.Gazi Golam Faruque Wahidur Rahman Department of Software Engineering Daffodil UniversityDhakaBangladesh Collage of Computer Science and Information System University of NajranNajranSaudi Arabia Department of Computer Science and Engineering Khwaja Yunus Ali UniversitySirajganj 6751Bangladesh

Depression is a crippling affliction and affects millions of individuals around the *** general,the physicians screen patients for mental health disorders on a regular basis and treat patients in collaboration with psychologists and other mental health experts,which results in lower costs and improved patient ***,this strategy can necessitate a lot of buy-in from a large number of people,as well as additional training and logistical ***,utilizing the machine learning algorithms,patients with depression based on information generally present in a medical file were analyzed and *** methodology of this proposed study is divided into six parts:Proposed Research Architecture(PRA),Data Pre-processing Approach(DPA),Research Hypothesis Testing(RHT),Concentrated Algorithm Pipeline(CAP),Loss Optimization Stratagem(LOS),and Model Deployment Architecture(MDA).The Null Hypothesis and Alternative Hypothesis are applied to test the *** addition,Ensemble Learning Approach(ELA)and Frequent Model Retraining(FMR)have been utilized for optimizing the loss ***,the Features Importance Interpretation is also delineated in this *** forecasts could help individuals connect with expert mental health specialists more quickly and *** to the findings,71%of people with depression and 80%of those who do not have depression can be appropriately *** study obtained 91%and 92%accuracy through the Random Forest(RF)and Extra Tree *** after applying the Receiver operating characteristic(ROC)curve,79%accuracy was found on top of RF,81%found on Extra Tree,and 82%recorded for the eXtreme Gradient Boosting(XGBoost)***,several factors are identified in terms of predicting depression through statistical data *** the additional effort is needed to develop a more accurate model,this model can be adjustable in the healthcare sector for diagnosing depression.

关键词： Depression analysis mental health machine learning model deployment XGBoost ROC-AUC

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Identifying The Coconut Leaf Disease Using Deep Transfer Learning Approach 4

Identifying The Coconut Leaf Disease Using Deep Transfer Lea...

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4th IEEE International Conference on Smart Information systems and Technologies, SIST 2024

作者： Tuhin, Hemayet Hossain Kayum, Md Abdul Rahmatul Kabir Rasel Sarker, Md. Tanvir Siddiquee, Shah Md Pandey, Bishwajeet Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Intelligent System and Cyber Security Astana IT University Kazakhstan

ISBN: (纸本)9798350374865

Coconut is one of the main economic crops in Bangladesh. It is a tree whose every part is useful in one way or another in public life. The leaves, flowers, fruits, stems, and roots of this tree are used as raw materials for various small and large industries, materials for making various delicious foods, delicious drinks, and food for patients. This is the world’s most beautiful tree and is well known and appreciated by all as the’heavenly tree’. However, it has recently become known that most coconut trees suffer from illnesses that gradually weaken the trees’ health and coconut yield. Pest illnesses and nutrient deficits have an impact on the majority of the tree’s leaves. The main reason is that most of the farmers in our country are unaware. They do not know what kind of measures to take in the case of any disease. They are using pesticides, assuming ancient principles, sometimes benefiting and sometimes counterproductively, and making the farmers poor. As a result, farmers are showing disinterest in coconut cultivation. Our main objective is to increase the viability of coconut leaves and detect problems early on so that farmers can benefit more from the cultivation of coconuts. The study suggests examining diseases and detecting insect attacks and nutritional deficiencies in coconut leaves. This will help grow more coconuts. It is expected that this model will help the agricultural field on both an economic and ecological level. This study focuses on the three coconut diseases: WCLWD Yellowing, WCLWD Flaccidity, and CCI Caterpillars. Three neural networks have_ been chosen in this study to identify the best model. After evaluating the VGG19, MobileNet v2, Inception v3, and ResNet50 models, the accuracy for a collection of 3166 images was determined to be 96%, 96.02%, 97.77%, and 99.36%, respectively. As a result of this technology, farmers will be able to produce more coconuts, surely bringing about a revolution in the agricultural industry. © 2024 IEEE.

关键词： coconut leaf disease Inception v3 MobileNet v2 nutrient deficiency pest attacks ResNet50 VGG19

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Blockchain Interoperability for A Reputation-Based Drug Supply Chain Management 6

Blockchain Interoperability for A Reputation-Based Drug Supp...

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6th International Conference on Information systems and computer Networks, ISCON 2023

作者： Tanvir, Deluwar Hussen Amin, Ruhul Islam, Ashraful Islam, Mohammad Shamsul Rashid, Muhammed Mamunur Sylhet Engineering College Department of Computer Science and Engineering Sylhet Bangladesh Faridpur Engineering College Department of Computer Science and Engineering Faridpur Bangladesh System Analyst Investment Corporation of Bangladesh Dhaka Bangladesh

ISBN: (纸本)9798350346961

The pharmaceutical drug industry faces considerable challenges in supply chain management, particularly in regard to tracking and tracing medications. Counterfeit drugs pose a significant global threat, and existing supply chain management practices often prove insufficient to prevent adulteration. Blockchain technology offers a potential solution to these issues by establishing a decentralized and distributed ecosystem where stakeholders can foster a trustworthy relationship. To address these limitations, we propose a blockchain-based system for tracking drugs in the supply chain, incorporating a reputation mechanism to ensure transparency and accountability among entities. To mitigate the interoperability challenges between blockchain networks, we implement inter-blockchain communication using the burn-to-claim protocol. Hyperledger Fabric is used as the primary blockchain because it ensures confidentiality between multiple organizations, and Quorum is the other blockchain. Our solution prioritizes trust, traceability, transparency, and accountability to deliver safe, authentic medication to consumers.. © 2023 IEEE.

关键词： Blockchain

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Fusion Strategy for Improving Medical Image Segmentation

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computers, Materials & Continua 2023年第2期74卷 3627-3646页

作者： Fahad Alraddady E.A.Zanaty Aida HAbu bakr Walaa M.Abd-Elhafiez Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Information System Department Faculty of Computers and InformationSohag UniversitySohagEgypt Mathematical and Computer Science Department Faculty of ScienceAswan UniversityAswanEgypt College of Computer Science&Information Technology Jazan UniversityJazanKingdom of Saudi Arabia Computer Science Department Faculty of Computers and Artificial IntelligenceSohag UniversitySohagEgypt

In this paper,we combine decision fusion methods with four metaheuristic algorithms(Particle Swarm Optimization(PSO)algorithm,Cuckoo search algorithm,modification of Cuckoo Search(CS McCulloch)algorithm and Genetic algorithm)in order to improve the image *** proposed technique based on fusing the data from Particle Swarm Optimization(PSO),Cuckoo search,modification of Cuckoo Search(CS McCulloch)and Genetic algorithms are obtained for improving magnetic resonance images(MRIs)*** algorithms are used to compute the accuracy of each method while the outputs are passed to fusion *** order to obtain parts of the points that determine similar membership values,we apply the different rules of incorporation for these *** proposed approach is applied to challenging applications:MRI images,gray matter/white matter of brain segmentations and original black/white images Behavior of the proposed algorithm is provided by applying to different medical *** is shown that the proposed method gives accurate results;due to the decision fusion produces the greatest improvement in classification accuracy.

关键词： Decision fusion particle swarmoptimization(PSO) cuckoo search algorithm CS McCulloch algorithm genetic algorithm CT and MRI

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Behaviour-diverse automatic penetration testing:a coverage-based deep reinforcement learning approach

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Frontiers of computer science 2025年第3期19卷 15-24页

作者： Yizhou YANG Longde CHEN Sha LIU Lanning WANG Haohuan FU Xin LIU Zuoning CHEN Zhongguancun Laboratory Beijing 100081China Zhejiang Lab Hangzhou 311121China National Research Centre of Parallel Computer Engineering and Technology Wuxi 214000China Faculty of Geographical Science Beijing Normal UniversityBeijing 100875China Department of Earth System Science Tsinghua UniversityBeijing 100084China

Reinforcement Learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonous strategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-Based Reinforcement Learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across various scenarios demonstrate that CLAP outperforms state-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.

关键词： network security penetration testing reinforcement learning artificial intelligence

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Towards optimized tensor code generation for deep learning on sunway many-core processor

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Frontiers of computer science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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