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Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

Computers, Materials & Continua 2022年第5期71卷 2599-2616页

作者： Muhammad Ibrahim Faisal Jamil YunJung Lee DoHyeun Kim Big Data Research Center Department of Computer Science and StatisticsJeju National UniversityKoreaJeju-si63243Korea Department of Computer Engineering(and Advanced Technology Research Institute) Jeju National UniversityJeju 63243Korea

In recent times,the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security *** IoT has shown wide adoption in various applications including smart cities,healthcare,trade,business,*** these applications,fitness applications have been widely considered for smart fitness *** users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness ***,scheduling such a huge number of requests for fitness exercise is a big ***,the user fitness data is critical thus securing the user fitness data from unauthorized access is also *** overcome these issues,this work proposed a blockchain-based load-balanced task scheduling approach.A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling *** proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’requests and improve resource *** performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection *** obtained results confirm the efficiency of the proposed scheduling *** investigating the performance of the blockchain,various experiments are performed using the Hyperledger Caliper concerning latency,throughput,resource *** Solo approach has shown an improvement of 32%and 26%in throughput as compared to Raft and Solo-Raft approaches *** obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.

关键词： Load balancing resource scheduling task scheduling Internet of things blockchain fitness applications

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Blockchain-Based Smart Kitchen Platform for the Catering Industry 5

Blockchain-Based Smart Kitchen Platform for the Catering Ind...

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5th IEEE International Conference on Hot Information-Centric Networking, HotICN 2022

作者： Dai, Sida Zhou, Hongyang Han, Tao Yang, Mingchuan Zhang, Yuan College of Big Data and Ai China Telecom Research Institute Beijing China College of Information Science and Electronic Engineering Zhejiang University College of Big Data and Ai China Telecom Research Institute Hangzhou China

ISBN: (纸本)9798350335200

This paper presents an intelligent smart kitchen platform based on blockchain systems that are suited for intelligent kitchens. Different from traditional kitchen management systems that are deployed in single places in a duplicated way, the novel platform divides the related characters into distinct roles, namely, the data side, supply side, demand side, and platform side. This paper uses SSD multi-task learning algorithm to solve the problem that traditional plans usually need to train and deploy the model several times. This paper stores the data in BaaS and divides the data operation rules into different scenarios with different BaaS endorsement strategies according to the business process. With a combination with the k-anonymity mechanism, a smart kitchen platform solution that balances security, privacy, and efficiency is achieved. Business statistics give feedback that by implementing such a platform, users can save costs on buying heavy computers, instead, only thin clients are needed. So real-scene deployment and operation show that the proposed platform is quite environmental-friendly and effective. © 2022 IEEE.

关键词： Blockchain

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RTUNet++: Assessment of Osteosarcoma MRI Image Segmentation leveraging Hybrid CNN-Transformer Approach with Dense Skip Connection 8

RTUNet++: Assessment of Osteosarcoma MRI Image Segmentation ...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Zou, Binfeng Chen, Yifei Chen, Zhanghao Sun, Yifei Huang, Yifan Qin, Feiwei Wang, Changmiao HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Shenzhen Research Institute of Big Data Medical Big Data Lab Shenzhen China

ISBN: (纸本)9798350397932

Orthopedic osteosarcoma is a prevalent malignant bone tumor. Preoperative planning, efficacy evaluation, and metastasis detection of osteosarcoma necessitate the use of magnetic resonance imaging (MRI). Due to the varying 10-cations, structures, sizes, and shapes of osteosarcomas among patients, as well as the high degree of tumor heterogeneity, segmenting osteosarcoma images manually presents a significant challenge for clinicians. In the meantime, the grayscale and texture features within the tumor in MRI images are not homogeneous, resulting in small grayscale differences between the tumor tissue and the surrounding normal tissue, which also presents a significant challenge for deep learning-based image segmentation methods. This paper proposes RTUNet++, a novel method for segmenting osteosarcoma MRI images. This method integrates the ResNet residual module, Transformer attention mechanism, and UNet++ Dense Skip Connection structure. We employ a CNN-Transformer hybrid architecture to prevent the loss of spatial feature data. Features with different semantic scales are aggregated at the decoder using multi-fusion dense jump connections to achieve flexible feature fusion. Consequently, in comparison experiments with various classical models, our proposed RTUNet++ achieves superior segmentation results on an osteosarcoma MRI image dataset. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information Engineering Beihang UniversityBeijing 100191China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore School of Science and Engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China Department of Electrical and Computer Engineering University of HoustonHoustonTX 77004USA Department of Computer Science and Engineering Kyung Hee UniversitySeoulSouth Korea446-701 Department of Electrical and Computer Engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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Phase prediction for high-entropy alloys using generative adversarial network and active learning based on small datasets

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science China(Technological sciences) 2023年第12期66卷 3615-3627页

作者： CHEN Cun ZHOU HengRu LONG WeiMin WANG Gang REN JingLi Henan Academy of Big Data School of Mathematics and StatisticsZhengzhou UniversityZhengzhou 450001China State Key Laboratory of Advanced Brazing Filler Metals and Technology Zhengzhou Research Institute of Mechanical EngineeringZhengzhou 450001China Institute of Materials Shanghai UniversityShanghai 200444China

In this paper,a new machine learning(ML)model combining conditional generative adversarial networks(CGANs)and active learning(AL)is proposed to predict the body-centered cubic(BCC)phase,face-centered cubic(FCC)phase,and BCC+FCC phase of high-entropy alloys(HEAs).Considering the lack of data,CGANs are introduced for data augmentation,and AL can achieve high prediction accuracy under a small sample size owing to its special sample selection ***,we propose an ML framework combining CGAN and AL to predict the phase of *** arithmetic optimization algorithm(AOA)is introduced to improve the artificial neural network(ANN).AOA can overcome the problem of falling into the locally optimal solution for the ANN and reduce the number of training *** AOA-optimized ANN model trained by the AL sample selection strategy achieved high prediction accuracy on the test *** improve the performance and interpretability of the model,domain knowledge is incorporated into the feature ***,considering that the proposed method can alleviate the problem caused by the shortage of experimental data,it can be applied to predictions based on small datasets in other fields.

关键词： high-entropy alloys phase prediction machine learning conditional generative adversarial networks active learning

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A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning

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Intelligent and Converged Networks 2024年第4期5卷 317-335页

作者： Chao Wang Yiyun Shi Sumin Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

Currently,the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning *** identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult,ultimately preventing the achievement of the most optimal *** paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive *** reasoning aims to enhance performance in scenarios with poor system robustness,complex tasks,and diverse goals.A deep learning model is constructed,trained to extract scene features,and combined with expert knowledge,then transformed into a quantum annealable *** final strategy is obtained using a D-wave quantum computer with quantum tunneling effect,which helps in finding optimal solutions by jumping out of local suboptimal *** on 400000 real data,four algorithms are compared:minimum-cost flow,sequential markov decision process,hot-dot strategy,and driver-prefer *** average total revenue increases by about 10%and vehicle utilization by about 15%in various *** summary,the proposed architecture effectively solves the e-hailing reposition problem,offering new directions for robust artificial intelligence in big data decision problems.

关键词： intuitive reasoning reinforcement learning quantum annealing

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Text Classification for Social Governance: A Novel Strategy with Adaptive Reward Mechanisms 6

Text Classification for Social Governance: A Novel Strategy ...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Kong, Xu Zhu, Xiaomin Wang, Maoli Wang, Xinming Zou, Min School of Computer Science Qufu Normal University Rizhao China Shandong Institute of Big Data Jinan China School of Cyber Science and Engineering Qufu Normal University Qufu China

ISBN: (纸本)9798350353174

Given the increasing need for societal governance systems to effectively address public grievances, classifying texts from public service hotlines and other governance communications has become challenging, especially for texts with complex hierarchical structures. This study explores how adaptive reward mechanisms can be applied to classify complaint texts in societal governance. We propose a novel hierarchical text classification model that overcomes the limitations of traditional methods in handling hierarchical data. We designed a hierarchical loss function that considers inter-label hierarchical relationships to enhance the model's learning of hierarchical information. Additionally, we used a simple label encoding method to reduce computational complexity and improve training efficiency. Experimental results show that our model significantly outperforms existing methods in classification accuracy, both flat and hierarchical. Furthermore, ablation studies confirmed the hierarchical loss function's crucial role in enhancing model performance. This research offers an effective strategy for classifying complex hierarchical data, significantly improving classification tasks in natural language processing. © 2024 IEEE.

关键词： Adversarial machine learning

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Semantic Template-based Convolutional Neural Network for Text Classification

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ACM Transactions on Asian and Low-Resource Language Information Processing 2023年第11期22卷 1-21页

作者： Chang, Yung-Chun Ng, Siu Hin Chen, Jung-Peng Liang, Yu-Chi Hsu, Wen-Lian Graduate Institute of Data Science Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan Snhcc Tigp Academia Sinica National Cheng Chi University Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Department of Computer Science and Information Engineering College of Information and Electrical Engineering Asia University Taichung Taiwan Pervasive Ai Research Labs Ministry of Science and Technology Hsinchu City Taiwan

We propose a semantic template-based distributed representation for the convolutional neural network called Semantic Template-based Convolutional Neural Network (STCNN) for text categorization that imitates the perceptual behavior of human comprehension. STCNN is a highly automatic approach that learns semantic templates that characterize a domain from raw text and recognizes categories of documents using a semantic-infused convolutional neural network that allows a template to be partially matched through a statistical scoring system. Our experiment results show that STCNN effectively classifies documents in about 140,000 Chinese news articles into predefined categories by capturing the most prominent and expressive patterns and achieves the best performance among all compared methods for Chinese topic classification. Finally, the same knowledge can be directly used to perform a semantic analysis task. © 2023 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Semantics

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Make an Image Move: Few-Shot Based Video Generation Guided by CLIP 27th

Make an Image Move: Few-Shot Based Video Generation Guided b...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Huang, Yonglong Yu, Cheng Li, Nannan Deng, Fuqin Ge, Ruiquan Wang, Changmiao School of Computer Science and Engineering Macau University of Science and Technology Macau China Chongqing University of Technology Chongqing China Wuyi University Jiangmen China Hangzhou Dianzi University Hangzhou China Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (纸本)9783031781711

The recent surge in interest surrounding text-to-video diffusion models highlights their capability to generate videos with both consistency and diversity. Current methods focus on leveraging large-scale datasets to align with training videos, while certain approaches explore the potential of zero-shot generation. Few-shot generative models adapt text-to-image model with temporal layers. They can capture the temporal motion and appearance with acceptable computational resources. However, the prevalent few-shot based approaches, which employ a singular prompt for training videos, typically result in inadequate control over complex backgrounds and multiple objects. To overcome this limitation, we introduce a novel component: the dual cross-attention layer. This component leverages CLIP for image feature extraction. The image feature and text feature will be processed independently in dual cross-attention layer, aiming to achieve image reference and enrich the training videos with additional information. This dual cross-attention mechanism empowers the diffusion model to learn both image and text information effectively, facilitating the generation of higher quality videos. Furthermore, we propose an innovative sampling method to enhance temporal consistency and stability of generative videos. Compared to other text-to-video generative models, our framework demonstrates superior efficiency in generating high quality videos with diverse styles. The code is available at https://***/FatLong666/MAIM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Video analysis

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Application of Convolutional Neural Network in Livestock Target Detection of IoT Images 5

Application of Convolutional Neural Network in Livestock Tar...

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5th International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2024

作者： Tian, Maohong Zhao, Feiyang Chen, Nengmei Zhang, Dequan Li, Jie Li, Hualin School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Shu Yi Xin Credit Management Co. Ltd Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Intelligent Application of Financial Big Data Chongqing Colleges and Universities Engineering Research Center Chongqing China Chongqing Da Tong Experimental School Chongqing China Shu Yi Xin Credit Management Co. Ltd Chongqing China

ISBN: (纸本)9798350385557

The national rural revitalization strategy has advanced, and new requirements for modern internet-of-things (IoT)-based farm supervision tools have been proposed. This study proposes a 'you-only-look-once' (YOLO) v5 convolutional neural network to optimize the candidate frame's center point algorithm. Five YOLOv5 models are used to identify and classify captive pigs. According to the experimental results, the K-median algorithm has a better effect than using the K-means algorithm, and its average intersection over union is approximately 1-2% higher. In our scenario, the commercial application value of the YOLOv5s model is higher, and the corresponding performance results of F1-score, mAP0.5, mAP0.5:0.95, FPS, and R2 are 92.21%, 95.18%, 64.85%, 75 FPS, and 85.54%, respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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