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4th IEEE International Conference on data Engineering and Communication Systems, ICDECS 2024

作者： Sharanya, S. Rai, Arnav Vinod Vedanth, Sai Srm Institute of Science and Technology School of Computing Department of Data Science and Business Systems India

ISBN: (纸本)9798350393354

In this pivotal study, we delve into the imperative realm of Diabetic Retinopathy (DR), a sight-threatening eye disease, introducing a nuanced and comprehensive approach to its detection through cutting-edge deep learning techniques. Our focus centers on the meticulous comparison of four distinguished convolutional neural network (CNN) architectures: CNN, VGG16, ResNet50, and EfficientNetB5. The dataset comprises five meticulously curated image categories, delineating various DR stages. The discerning outcomes illuminate the consistent superiority of the CNN and VGG16 algorithms, showcasing a commendable edge in accuracy. To amplify DR detection precision, we introduce an innovative hybrid model amalgamating the robust attributes of both CNN and VGG16. This hybrid model triumphs with an extraordinary accuracy rate of 90.5%, surpassing individual performances of the four algorithms. In summation, our groundbreaking study unequivocally establishes the efficacy of a hybrid CNN-VGG16 model for Diabetic Retinopathy detection, underscoring its substantial superiority in accuracy when compared to each of the individual algorithms, advocating for the strategic integration of diverse deep learning models to fortify the accuracy and dependability of medical image analysis. The repercussions of our findings promise to revolutionize the landscape of early detection and holistic management of DR, fulfilling an unmet healthcare need. © 2024 IEEE.

关键词： Convolutional neural networks

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Real-time updating of dynamic social networks for COVID-19 vaccination strategies

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Journal of Ambient Intelligence and Humanized computing 2024年第3期15卷 1981-1994页

作者： Cheng, Sibo Pain, Christopher C. Guo, Yi-Ke Arcucci, Rossella Data Science Instituite Department of Computing Imperial College London London United Kingdom Department of Earth Science and Engineering Imperial College London London United Kingdom

Vaccination strategy is crucial in fighting the COVID-19 pandemic. Since the supply is still limited in many countries, contact network-based interventions can be most powerful to set an efficient strategy by identifying high-risk individuals or communities. However, due to the high dimension, only partial and noisy network information can be available in practice, especially for dynamic systems where contact networks are highly time-variant. Furthermore, the numerous mutations of SARS-CoV-2 have a significant impact on the infectious probability, requiring real-time network updating algorithms. In this study, we propose a sequential network updating approach based on data assimilation techniques to combine different sources of temporal information. We then prioritise the individuals with high-degree or high-centrality, obtained from assimilated networks, for vaccination. The assimilation-based approach is compared with the standard method (based on partially observed networks) and a random selection strategy in terms of vaccination effectiveness in a SIR model. The numerical comparison is first carried out using real-world face-to-face dynamic networks collected in a high school, followed by sequential multi-layer networks generated relying on the Barabasi-Albert model emulating large-scale social networks with several communities. © The Author(s) 2023.

关键词： COVID-19

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ChestBox: Enabling Fast State Sharing for Stateful Serverless computing with State Functions

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2024年第03期10卷 491-502页

作者： Zhang, Xinmin Wu, Song Gu, Lin He, Qiang Jin, Hai Huazhong University of Science and Technology 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 Wuhan Hubei430074 China

This paper presents ChestBox, a novel approach that utilizes state functions to facilitate low-latency state sharing for stateful serverless computing. When an application function needs to share a state, the state function creates a memory space with Linux's shared memory object to store the state. Other application functions can then read the state directly from the shared memory. ChestBox enables fast state sharing that avoids excessive memory overhead without compromising on-demand resource allocation compared to existing solutions. This effectively reduces the energy consumption of serverless computing and promotes sustainable computing. The implementation of ChestBox on Apache OpenWhisk unearths two major implementation challenges, which we address with respective optimization techniques, i.e., state function channel and state swapping. The evaluation of ChestBox with four real-world applications shows that compared with the state-of-The-Art approach, it can reduce state-sharing latency by up to 99.71%, while reducing execution costs by 24.59% and storage costs by 99.76%. © 2016 IEEE.

关键词： Storage allocation (computer)

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile computing 2025年第7期24卷 6243-6258页

作者： Fu, Shucun Dong, Fang Chen, Runze Shen, Dian Zhang, Jinghui He, Qiang Southeast University School of Computer Science and Engineering Nanjing China Huazhong University of Science and Technology 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 Wuhan China

Edge learning (EL) is an end-to-edge collaborative learning paradigm enabling devices to participate in model training and data analysis, opening countless opportunities for edge intelligence. As a promising EL framework, federated synergy learning (FSyL) mitigates the computation and communication overhead on resource-constrained devices by offloading partial model layers to the edge server for synergistic training. Nevertheless, due to the system and statistical heterogeneity, naively using existing FSyL methods is significantly time-consuming and causes accuracy degradation. Motivated by this issue, this paper introduces a novel FSyL framework that integrates multi-dimensional training optimization and formulates the edge learning cost minimization (ELCM) problem. To tackle the ELCM efficiently, we design OL-MG, an OnLine Model Splitting and Resource Provisioning Game. Specifically, we first reformulate and decompose the original ELCM based on data quality evaluation. Then, given a model splitting decision, we determine the optimal resource provisioning in Sub-problem1, based on which optimal model splitting in Sub-problem2 is modeled as a potential game. Subsequently, we introduce a decentralized algorithm to find a Nash equilibrium (NE) solution. Furthermore, we further extend OL-MG to support a budget-aware multi-edge scenario. Extensive experiments demonstrate that the proposed mechanism significantly outperforms state-of-the-art methods in cost-saving and accuracy improvement. © 2025 IEEE.

关键词： Federated learning

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Demystifying XAI: Requirements for Understandable XAI Explanations

Demystifying XAI: Requirements for Understandable XAI Explan...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Stodt, Jan Reich, Christoph Knahl, Martin Institute for Data Science Cloud Computing and IT Security Furtwangen University Furtwangen Germany

ISBN: (纸本)9781643685335

This paper establishes requirements for assessing the usability of Explainable Artificial Intelligence (XAI) methods, focusing on non-AI experts like healthcare professionals. Through a synthesis of literature and empirical findings, it emphasizes achieving optimal cognitive load, task performance, and task time in XAI explanations. Key components include tailoring explanations to user expertise, integrating domain knowledge, and using non-propositional representations for comprehension. The paper highlights the critical role of relevance, accuracy, and truthfulness in fostering user trust. Practical guidelines are provided for designing transparent and user-friendly XAI explanations, especially in high-stakes contexts like healthcare. Overall, the paper's primary contribution lies in delineating clear requirements for effective XAI explanations, facilitating human-AI collaboration across diverse domains. © 2024 The Authors.

关键词： Requirements engineering

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Two genetic algorithms for final exam scheduling 24

Two genetic algorithms for final exam scheduling

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6th Asia Conference on Machine Learning and computing, ACMLC 2024

作者： Mixer, Mark Morrow, Steven Sukherman, Lev School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

ISBN: (纸本)9798400710018

The scheduling of final exams at a university is a problem which can be improved with artificial intelligence techniques. In this paper we explain and compare two algorithms used to solve the exam scheduling problem at Wentworth Institute of Technology. The first technique is an implementation of a pure genetic algorithm, while the second combines some graph theoretical algorithms with two variations of a genetic algorithm. We will compare the performance of these approaches to each other and to historical results. © 2024 Copyright held by the owner/author(s).

关键词： Scheduling algorithms

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of Computer science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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Dynamic Distributed Model Compression for Efficient Decentralized Federated Learning and Incentive Provisioning in Edge computing Networks

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IEEE Transactions on Mobile computing 2025年第07期24卷 6293-6314页

作者： Asheralieva, Alia Niyato, Dusit Wei, Xuetao Southern University of Science and Technology Department of Computer Science and Engineering 1088 Xueyuan Avenue Shenzhen China Nanyang Technological University College of Computing and Data Science 50 Nanyang Avenue Singapore639798 Singapore

We study decentralized federated learning (DFL) in edge computing networks where edge nodes (ENs) collaboratively train their artificial intelligence (AI) models in a serverless manner without sharing local data. We consider the following critical DFL challenges: i) scarce bandwidth resources of ENs;ii) dynamic, heterogeneous edge environment;iii) incentive provisioning and complex tradeoffs between the DFL performance and training costs. To resolve these challenges, we develop a new model compression method where ENs utilize dynamic, non-identical compression rates to improve the communication efficiency of DFL under time-varying, heterogeneous resource constraints. We show that our method can be formulated as a graphical Markov potential game where ENs act as players deciding on their compression factors and the number of data samples used for model updates. Each EN is incentivized to participate in DFL through rewards based on the EN's contribution to training. We prove that our game has a dominant pure-strategy Nash equilibrium (NE) maximizing its potential function and propose a dynamic distributed compression algorithm in which each EN can find its dominant strategy independently. We show that this algorithm converges to the Pareto-optimal NE, representing the most efficient solution of our game enhancing the DFL performance with minimal costs. © 2002-2012 IEEE.

关键词： Federated learning

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Patterns Recognition of Sentimental Patterns from the X-dataset using Deep Learning Algorithm 4

Patterns Recognition of Sentimental Patterns from the X-Data...

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4th International Conference on Soft computing for Security Applications, ICSCSA 2024

作者： Konduri, Praveen S R Department of Data Science School of Computing Mohan Babu University Tirupati India

ISBN: (纸本)9798331515720

This research investigates the application of deep learning techniques to sentiment analysis, a field focused on mining online platforms for subjective assessments. By combining deep neural networks (DNNs) with particle swarm optimization (PSO), we propose a hybrid method to improve feature selection and enhance the accuracy of sentiment classification. The study employs a dataset collected from Twitter using the Ruby Twitter API and evaluates the performance of the proposed method using k-fold cross-validation. The results are compared with the previously used firefly search (FS) method to demonstrate the effectiveness of the hybrid approach. This research contributes to the advancement of sentiment analysis techniques and their application in various domains, such as social media monitoring and market research. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Cube Attacks on Round-Reduced Grain-128AEAD 6th

Cube Attacks on Round-Reduced Grain-128AEAD

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6th International Conference on Emerging Technologies in computing, iCETiC 2023

作者： Teng, Wil Liam Salam, Iftekhar Yau, Wei-Chuen Teh, Jia Yew School of Computing and Data Science Xiamen University Malaysia Sepang43900 Malaysia

ISBN: (纸本)9783031502149

Lightweight cryptography aims to design secure and efficient cryptographic algorithms for resource-constrained devices. Traditional cryptographic algorithms may not be readily usable in resource-constrained environments. To standardise cryptographic solutions tailored for such resource-constraint environments, the National Institute of Standards and Technology (NIST) launched the Lightweight Cryptography (LWC) project. Grain-128AEAD is a stream cipher-based finalist in the NIST LWC project. In this work, we examine the security of the initial version of Grain-128AEAD against cube attacks. We present distinguishing attacks on a reduced-round version of the cipher, assuming that the keystream can be observed immediately after the reduced-round initialisation of the pre-output generator. We obtained various cubes of sizes 25 to 45 for reduced-round Grain-128AEAD. The best cube reported in this work can distinguish the output of a 165-round initialisation of Grain-128AEAD with a cube size of 35. The complexity of the distinguishing attack is O(235). The results are confirmed experimentally. We conclude that even with fewer rounds of initialisation for the first version of Grain-128AEAD, the cipher still has a good security margin against cube attacks. © 2024, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Geometry

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