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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Sugandh, Urvashi Nigam, Swati Khari, Manju Banasthali Vidyapith Faculty of Mathematics and Computing Department of Computer Science Banasthali India School of Computing Science Engineering Galgotias University Uttar Pradesh Greater Noida India School of Computer and System Sciences Jawaharlal Nehru University New Delhi India

ISBN: (纸本)9789380544472

An increasing population demands more food, but the available resources are shrinking. The agriculture sector in India is facing several challenges such as climate change, water scarcity, soil degradation, and declining farm profitability. To address these challenges, there is a growing need for the adoption of smart agriculture practices that leverage technology. This paper presents an overview of the ecosystem of technologies for smart agriculture and their potential to improve the efficiency and profitability of Indian farmers. The paper explores the role of various technologies such as precision agriculture, IoT, artificial intelligence, drones, and robotics in improving crop yields, reducing input costs, and enhancing overall farm management. Additionally, the paper discusses the challenges that need to be overcome to ensure the successful adoption of smart agriculture technologies in India. These include the lack of awareness and access to technology, inadequate infrastructure, and limited availability of financing. The paper concludes that the ecosystem of technologies for smart agriculture holds significant promise for Indian farmers to overcome the challenges and improve the efficiency and profitability of their farms. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Profitability

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Multi-Dimensional Training Optimization for Efficient Federated Synergy Learning

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IEEE Transactions on Mobile Computing 2025年

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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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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A reliable method for data aggregation on the industrial internet of things using a hybrid optimization algorithm and density correlation degree

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Cluster Computing 2024年第6期27卷 7521-7539页

作者： Heidari, Arash Shishehlou, Houshang Darbandi, Mehdi Navimipour, Nima Jafari Yalcin, Senay Department of Software Engineering Haliç University Istanbul34060 Turkey Department of Computer Engineering Tabriz Branch Islamic Azad University Tabriz Iran Pôle Universitaire Léonard de Vinci Paris France Department of Computer Engineering Kadir Has University Istanbul Turkiye and Future Technology Research Center National Yunlin University of Science and Technology Yunlin Douliou64002 Taiwan Department of Energy System Engineering School of Engineering and Natural Sciences Bahçeşehir University Istanbul Turkey

The Internet of Things (IoT) is a new information technology sector in which each device may receive and distribute data across a network. Industrial IoT (IIoT) and related areas, such as Industrial Wireless Networks (IWNs), big data, and cloud computing, have made significant strides recently. Using IIoT requires a reliable and effective data collection system, such as a spanning tree. Many previous spanning tree algorithms ignore failure and mobility. In such cases, the spanning tree is broken, making data delivery to the base station difficult. This study proposes an algorithm to construct an optimal spanning tree by combining an artificial bee colony, genetic operators, and density correlation degree to make suitable trees. The trees’ fitness is measured using hop count distances of the devices from the base station, residual energy of the devices, and their mobility probabilities in this technique. The simulation outcomes highlight the enhanced data collection reliability achieved by the suggested algorithm when compared to established methods like the Reliable Spanning Tree (RST) construction algorithm in IIoT and the Hop Count Distance (HCD) based construction algorithm. This proposed algorithm shows improved reliability across diverse node numbers, considering key parameters including reliability, energy consumption, displacement probability, and distance. © The Author(s) 2024.

关键词： Reliability

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Arrayed Waveguide Grating for Visible Light Application 14

Arrayed Waveguide Grating for Visible Light Application

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Holography, Diffractive Optics, and Applications XIV 2024

作者： Sun, Quancheng Feng, Zhihua Li, Yuhong Wang, Dongcheng Wang, Yan Liu, Haipeng Li, Junhao Yang, Cunliang Feng, Jijun Shanghai Key Laboratory of Modern Optical System Engineering Research Center of Optical Instrument and System Ministry of Education School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China School of Physics Xidian University Shaanxi Xi'an710071 China Nanjing Electronic Devices Institute Jiangsu210016 China

ISBN: (纸本)9781510682085

Miniaturized spectrometers, owing to their compact dimensions, high sensitivity, and low cost, have been extensively applied in environmental monitoring, biomedical diagnostics, industrial inspection, and other fields. However, conventional miniature spectrometers often face challenges in simultaneously achieving high spectral resolution, miniaturization, and a high degree of integration. As a pivotal component in integrated optics, the Arrayed Waveguide Grating (AWG) possesses advantages such as exceptional resolution, broad spectral bandwidth, and superior integrability, thus serving as a highly effective means for the design and realization of miniature spectrometers. Here, a highly uniform visible light arrayed waveguide grating with a central wavelength of 633nm was designed and fabricated. The device was based on a 300 nm silicon nitride platform, with the waveguide width of 300nm. The beam diverges at the input star coupler, then propagates through the arrayed waveguide, and finally converges on the image plane of the output star coupler. There are 4 output channels with a channel spacing of 10nm, and the spectral resolution is 5nm. This configuration can provide a uniform spectral output with a high stability, making it suitable for applications in the field of spectral analysis. © 2024 SPIE. All rights reserved.

关键词： Spectral resolution

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DRCTL: A Disorder-Resistant Computation Translation Layer Enhancing the Lifetime and Performance of Memristive CIM Architecture 57

DRCTL: A Disorder-Resistant Computation Translation Layer En...

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57th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO 2024

作者： Zhou, Heng Wu, Bing Cheng, Huan Liu, Jinpeng Lei, Taoming Feng, Dan Tong, Wei School of Computer Science and Technology Huazhong University of Science and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology China

ISBN: (纸本)9798350350579

The memristive Computing-in-Memory (CIM) sys-Tem can efficiently accelerate matrix-vector multiplication (MVM) operations through in-situ computing. The data layout has a significant impact on the communication performance of CIM systems. Existing software-level communication opti-mizations aim to reduce communication distance by carefully designing static data layouts, while wear-leveling (WL) and error mitigation methods use dynamic scheduling to enhance system reliability, resulting in randomized data layouts and increased communication overhead. Besides, existing CIM compilers di-rectly map data to physical crossbars and generate instructions, which causes inconvenience for dynamic scheduling. To address these challenges of balancing communication performance and reliability while coordinating existing CIM compilers and dy-namic scheduling, we propose a disorder-resistant computation translation layer (DRCTL), which improves system lifetime and communication performance through co-optimization of data layout and dynamic scheduling. It consists of three parts: (1) We propose an address conversion method for dynamic scheduling, which updates the addresses in the instruction stream after dynamic scheduling, thereby avoiding recompilation. (2) Dynamic scheduling strategy for reliability improvement. We propose a hierarchical wear-leveling (HWL) strategy, which reduces communication by increasing scheduling granularity. (3) Communication optimization for dynamic scheduling. We propose data layout-Aware selective remapping (LASR), which helps dynamic scheduling methods improve communication lo-cality and reduce latency by exploiting data dependencies. The experiments demonstrate that HWL extends lifetime by 100.3-205.9 x compared to not using WL. Even with a slight lifetime decrease compared to the state-of-The-Art WL (TIWL), it still supports continuous neural network training for 7 years. After applying LASR to HWL, the number of execution cycles, energy consumption

关键词： Program compilers

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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Fast mode decomposition for few-mode fiber based on lightweight neural network

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Chinese Optics Letters 2024年第2期22卷 88-95页

作者：赵佳佳陈国辉毕轩蔡汪洋岳磊唐明 School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha 410114China Wuhan National Laboratory for Optoelectronics(WNLO)and National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic InformationHuazhong University of Science and TechnologyWuhan 430074China

In this paper,we present a fast mode decomposition method for few-mode fibers,utilizing a lightweight neural network called *** method can quickly and accurately predict the amplitude and phase information of different modes,enabling us to fully characterize the optical field without the need for expensive experimental *** train the MobileNetV3-Light using simulated near-field optical field maps,and evaluate its performance using both simulated and reconstructed near-field optical field *** validate the effectiveness of this method,we conduct mode decomposition experiments on a few-mode fiber supporting six linear polarization(LP)modes(LP01,LP11e,LP11o,LP21e,LP21o,LP02).The results demonstrate a remarkable average correlation of 0.9995 between our simulated and reconstructed near-field lightfield *** the mode decomposition speed is about 6 ms per frame,indicating its powerful real-time processing *** addition,the proposed network model is compact,with a size of only 6.5 MB,making it well suited for deployment on portable mobile devices.

关键词： deep learning lightweight neural network few-mode fiber mode decomposition

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Intelligent Contract Timestamp Vulnerability Detection Based on Key Control Flow Graph 36

Intelligent Contract Timestamp Vulnerability Detection Based...

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36th International Conference on Software engineering and Knowledge engineering, SEKE 2024

作者： Quan, Qing Yu Zeng, Cheng Wang, Yi Feng Wang, Jiao Ran School of Computer Science and Information Engineering Hubei University Wuhan China School of Artificial Intelligence Hubei University Wuhan China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Wuhan China

ISBN: (纸本)1891706594

The extensive application of smart contract technology in the blockchain domain has positioned it as a key component of the digital economy. However, as the application scope of smart contracts expands, security issues have become increasingly evident, resulting in substantial economic losses. Current vulnerability detection methods largely depend on analyzing complete smart contract source code, which struggles to filter out extensive redundant information when identifying individual vulnerabilities. This leads to an insufficient exploration of the characteristics of smart contract code, which in turn results in lower overall accuracy of detection methods. In this context, the study introduces a method that transforms smart contract source code into a control flow graph, filtering out the main basic blocks based on key information about timestamp vulnerabilities. Subsequently, individual operation audits are conducted on these basic blocks, combined with the graphical structure of the control flow graph, utilizing gated graph neural networks for detecting timestamp vulnerabilities in smart contracts. Comparative experiments were performed on two public datasets, and the results indicate that compared to state-of-the-art detection methods, the proposed approach has improved the accuracy and F1 score for detecting timestamp vulnerabilities in smart contracts by 1.65% and 6.51%, providing valuable application prospects for the security of smart contracts. © 2024 Knowledge systems Institute Graduate school. All rights reserved.

关键词： Graph neural networks

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A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

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Frontiers of Information Technology & Electronic engineering 2024年第5期25卷 664-684页

作者： Xiaojun BAI Yang ZHANG Haixing WU Yuting WANG Shunfu JIN School of Information Science and Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan UniversityQinhuangdao 066004China

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research *** this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance *** to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant *** meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,*** establish a four-dimensional continuous-time Markov chain as the system *** using the Gauss-Seidel method,we derive the stationary probability distribution of the system ***,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of *** experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading ***,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

关键词： Edge computing Ofloading scheme Cloud-edge device collaboration Markov chain Cost function

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