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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

IEEE Transactions on Consumer Electronics 2024年第1期71卷 1571-1583页

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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A Multi-path Droplet Routing Protocol for Digital Microfluidic Biochip

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Informatica (Slovenia) 2024年第7期48卷 95-102页

作者： Swain, Jyotiranjan Pyne, Sumanta School of Computing and Information Technology REVA University Bangalore India Department of Computer Science and Engineering National Institute of Technology Rourkela India

Digital microfluidic biochip provides an alternative platform to synthesize the biochemical protocols. Droplet routing in biochemical synthesis involves moving multiple droplets across the biochip simultaneously. It involves challenges like moving droplets without violating the fluid constraints. This article proposed a multi-path droplet routing protocol. Multiple routes are explored and validated using special packets. These routes are then classified based on user-defined heuristics. A single route is sequentially selected for each droplet. The routes are then compacted to generate a parallel moving sequence. The simulation result shows 2.087% and 4.952% improvement in the latest arrival time for free and virtual topology, respectively. © 2024 Slovene Society Informatika. All rights reserved.

关键词： Biochips

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Optimized featured swarm convolutional neural network (OFSCNN) model based dialect recognition system for Bagri Rajasthani language

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International Journal of Information technology (Singapore) 2025年第1期17卷 121-133页

作者： Kukana, Poonam Sharma, Pooja Bhardwaj, Neeru Department of Computer Science and Engineering University School of Engineering & amp Technology Rayat-Bahra University Punjab Mohali India Department of Computer Science and Engineering Chandigarh University Punjab Mohali India

The dialects of a language hold a significant place in speech processing (SP) applications. The objective of dialect identification is to categorize speech sample data into a specific dialect of a speaker's spoken language. A dialect recognition system must effectively distinguish between different dialects of a standard language, as they tend to possess many similarities. The dialect of a language is not a distinct characteristic, as it can be impacted by the utterer's birthplace. Researchers in the domain of automatic speech recognition (ASR) face difficulties in identifying the speech patterns unique to each dialect or language. The proposed work recognizes the dialects of the Bagri Rajasthani language from undefined expressions of speech. Rajasthani language is one of the eldest and most famous languages in the Indo-Aryan languages. It comprises the different dialects and for recognizing the dialects, it used dissimilar phases of acoustic and spectral characteristics of the speech signal (SS). The spectral and acoustic features of SSs are measured to design the system. As there is no specific speech dataset for Bagri dialects, the database is built, to verify the Bagri dialects of the Rajasthani language. To improve the accuracy rate, and error rate in recognizing the Bagri dialects, the acoustic and spectral characteristics of speech expressions are joined. To verify several Bagri dialects of the Rajasthani language, different simulations for classification and investigation are carried out i.e., OFSCNN model, GA-NN, etc. The outcomes are important and the accuracy of 96.95% for the OFSCNN model, 80.63% for GA-NN, and 93.45% for the SVM method is an achievement. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： CNN (convolutional neural network Dialects MFCC (Mel frequency co-efficient cepstral) OFSCNN (optimized featured swarm convolutional neural network) PSO (particle swarm optimization) Speech Dialect Recognition

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PathActMarker:an R package for inferring pathway activity of complex diseases

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Frontiers of computer science 2025年第3期19卷 123-124页

作者： Xingyi LI Jun HAO Zhelin ZHAO Junming LI Xingyu LIAO Min LI Xuequn SHANG School of Computer Science Northwestern Polytechnical UniversityXi’an 710072China Research&Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen 518063China School of Software Northwestern Polytechnical UniversityXi’an 710072China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South UniversityChangsha 410083China

1 Introduction The process of complex diseases is closely linked to the disruption of key biological pathways,it is crucial to identify the dysfunctional pathways and quantify the degree of dysregulation at the indivi... 详细信息

关键词： pathway activity dysregulation complex diseases biological pathways disruption key biological pathwaysit identify dysfunctional pathways

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Opponent Modeling Based Dynamic Auction for UAV-Assisted Task Offloading 16

Opponent Modeling Based Dynamic Auction for UAV-Assisted Tas...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Wang, Shuyu Wang, Zhe Shi, Long Li, Bin Li, Jun School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350390643

This paper proposes a dynamic auction based resource trading scheme for unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) network, where multiple mobile users (MUs) compete to purchase the task offloading service from the UAV by bidding strategically. The UAV-assisted MEC server aims to maximize its long-term profit at the least energy costs by adaptively adjusting its service location via path planning. With dynamic task arrivals, each MU optimizes its sequential bidding strategy to minimize its task computation delay and payment under the budget constraint. Since each MU's remaining budget is a private information, we model this dynamic auction as a partially observable stochastic game, where the MU with the highest bid and sufficient remaining budget in each round wins the UAV's exclusive computation service and pays the second-highest price to the UAV. To tackle the issue of non-stationarity in a multi-agent environment, we propose an opponent modeling based reinforcement learning algorithm, namely neural fictitious self-play with dueling double deep Q Network (NFSP-D3QN), to optimize the trading policies for both the UAV and MUs. Specifically, the proposed algorithm incorporates fictitious self-play to implicitly predict the opponents' behavior, assisting each agent's sequential decision making in a fully decentralized manner. Simulation results demonstrate that, compared with the baseline algorithm, our proposed algorithm enhances both the UAV's and MUs' utilities, achieving efficient utilization of computing resources. © 2024 IEEE.

关键词： Stochastic systems

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Task Offloading in AIoT-Enabled UAV-Assisted MEC Network: A Digital Twin-Empowered Approach with FedRL

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1945-1956页

作者： Consul, Prakhar Budhiraja, Ishan Garg, Deepak Bennett University School of Computer Science Engineering and Technology Greater Noida India Sr Unicersity Telangana Hanumakonda India

The computationally intensive tasks generated by AIoTD face challenges due to its limitations in battery power and computing capabilities. These devices typically operate in resource-constrained environments where energy efficiency (EE), computational efficiency, and real-time responsiveness are critical factors. To address these challenges, MEC is a promising solution as it allows tasks to be transferred from IoT devices to MEC for processing. By utilizing UAVs in MEC systems, computing and storage resources are brought closer to end devices, particularly in remote areas. This study investigates the TO issue within a AIoT-enabled UAV-assisted MEC system empowered by DT technologies. The DT concept is employed to provide environmental information and facilitate data exchange for agents linked to AIoT devices. The TO problem is structured to optimize EE and distribute workloads effectively among edge servers. Subsequently, the problem is reformulated as a MDP and an energy-efficient TO strategy, termed FedRL-EETO (FedRL based), is proposed. Moreover, a DT-enabled FedRL-EETO approach is implemented to enhance privacy protection and train decentralized RA strategies for communication networks, further improving network efficiency. Experimental evaluations are conducted to represents the benefits of the FedRL-EETO algorithm in enhancing EE and workload distribution, demonstrating its superior performance in comparison to existing method. © 1975-2011 IEEE.

关键词： Energy efficiency

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AR-Light: Enabling Fast and Lightweight Multi-User Augmented Reality via Semantic Segmentation and Collaborative View Synchronization

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IEEE Transactions on computers 2025年第6期74卷 2073-2086页

作者： Wen, Yu Shah, Aamir Bader Cao, Ruizhi Zhang, Chen Chen, Jiefu Wu, Xuqing Xie, Chenhao Fu, Xin University of Houston Department of Electrical and Computer Engineering HoustonTX77204 United States Beihang University School of Computer Science and Engineering Beijing100191 China University of Houston Department of Information Science Technology HoustonTX77204 United States

Multi-user Augmented Reality (MuAR) allows multiple users to interact with shared virtual objects, facilitated by exchanging environment information. Current MuAR systems rely on 3D point clouds for real-world analysis, view synchronization, object rendering, and movement tracking. However, the complexity of 3D point clouds leads to significant processing delays, with approximately 80% of overhead in commercial frameworks. This hampers usability and degrades user experience. Our analysis reveals that maintaining the facing side of the real-world scene in a stable environment provides sufficient information for virtual object placement and rendering. To address this, we introduce a lightweight quadtree structure, representing 2D scenes through semantic segmentation and geometry, as an alternative to 3D point clouds. Additionally, we propose a novel correction method to handle potential shifts in virtual object placement during view synchronization among users. Combining all designs, we implement a fast and lightweight MuAR framework named AR-Light and test our framework on commercial AR devices. The evaluation results on real-world applications demonstrate that AR-Light can achieve high performance in various real-world scenes while maintaining a comparable virtual object placement accuracy. © 1968-2012 IEEE.

关键词： Semantic Segmentation

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Adaptive Marine Predator Optimization Algorithm(AOMA)-Deep Supervised Learning Classification(DSLC)based IDS framework for MANET security

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Intelligent and Converged Networks 2024年第1期5卷 1-18页

作者： M.Sahaya Sheela A.Gnana Soundari Aditya Mudigonda C.Kalpana K.Suresh K.Somasundaram Yousef Farhaoui Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and TechnologyChennai 600069India Department of Computer Science and Engineering Saveetha School of EngineeringSIMATSChennai 602105India JNIAS School of Planning and Architecture Hyderabad 500034India Department of Computer Science and Engineering NPR College of Engineering and TechnologyNatham Dindigul 624401India Department of Computer Science and Engineering PSNA College of Engineering and TechnologyPoolangulathupatti 620009India Department of Computer Science and Engineering Sri Muthukumaran Institute of TechnologyChennai 600069India Department of Computer Science Moulay Ismail UniversityMeknes 5003Morocco

Due to the dynamic nature and node mobility,assuring the security of Mobile Ad-hoc Networks(MANET)is one of the difficult and challenging tasks *** MANET,the Intrusion Detection System(IDS)is crucial because it aids in the identification and detection of malicious attacks that impair the network’s regular *** machine learning and deep learning methodologies are used for this purpose in the conventional works to ensure increased security of ***,it still has significant flaws,including increased algorithmic complexity,lower system performance,and a higher rate of ***,the goal of this paper is to create an intelligent IDS framework for significantly enhancing MANET security through the use of deep learning ***,the min-max normalization model is applied to preprocess the given cyber-attack datasets for normalizing the attributes or fields,which increases the overall intrusion detection performance of ***,a novel Adaptive Marine Predator Optimization Algorithm(AOMA)is implemented to choose the optimal features for improving the speed and intrusion detection performance of ***,the Deep Supervise Learning Classification(DSLC)mechanism is utilized to predict and categorize the type of intrusion based on proper learning and training *** evaluation,the performance and results of the proposed AOMA-DSLC based IDS methodology is validated and compared using various performance measures and benchmarking datasets.

关键词： Intrusion Detection System(IDS) Security Mobile Ad-hoc Network(MANET) min-max normalization Adaptive Marine Predator Optimization Algorithm(AOMA) Deep Supervise Learning Classification(DSLC)

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Machine Learning Based Diabetes Detection Model for False Negative Reduction

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Biomedical Materials and Devices 2024年第1期2卷 427-443页

作者： Uddin, Md. Ashraf Islam, Md. Manowarul Talukder, Md. Alamin Hossain, Md. Al Amin Akhter, Arnisha Aryal, Sunil Muntaha, Maisha School of Information Technology Deakin University Department of Computer Science and Engineering Jagannath University

Diabetes is a chronic disease characterized by the inability of the pancreas to produce enough insulin or the body’s inability to use insulin efficiently. This disease is becoming increasingly prevalent worldwide and can result in severe complications such as blindness, kidney failure, and stroke. Early detection of diabetes can potentially save millions of lives globally, making it a crucial focus of research. In this study, we propose a machine learning model to aid in predicting diabetes. The model comprises several machine learning methods: Linear Regression (LnR), Logistic Regression (LR), k-nearest neighbor (KNN), Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), and Decision Tree (DT). Prior to feeding the pre-processed data into the machine learning model for evaluation, we conducted several pre-processing steps, such as removing null values, standardizing data using normalization, and labeling data using the label encoding process. Imbalanced datasets can adversely affect the accuracy of machine learning algorithms, and we address this problem by balancing the datasets using the Synthetic Minority Oversampling Technique (SMOTE) method. We assessed the model’s performance on two datasets and found that the random forest algorithm produced optimal results, with 97% accuracy on the diabetes dataset 2019 and 80% accuracy on the Pima Indian dataset. However, using a balanced dataset, we can significantly reduce the number of false-negative detections. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2023.

关键词： Balance data Features selection Machine learning Pre-processed data Random forest SMOTE

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A Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries 24th

A Dynamic Symmetric Searchable Encryption Scheme for Rapid ...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Zou, Zekun Gao, Ruirui Li, Xiong Wang, Xiaofen Zhang, Xiaosong School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789819615445

Dynamic Symmetric Searchable Encryption (DSSE) enables secure storage of encrypted data on a cloud server while supporting dynamic updates and keyword searches. Two key security concepts, forward privacy and backward privacy, ensure that searchable encryption minimizes the exposure of sensitive information to the server. Currently, most existing searchable encryption schemes only support single-keyword queries, and reusing single-keyword searches to achieve conjunctive keyword queries will significantly decrease query efficiency. This paper proposes a novel approach to tackle this issue, which enhances the efficiency of conjunctive keyword queries by including all keyword information of documents within a single encrypted entry. Furthermore, by merging the previously updated keys into subsequent updated data, the client’s computational overhead during queries is reduced as well as the amount of state information that the client needs to maintain. Compared with ODXT, our scheme achieves less information leakage, higher query efficiency, and lower communication overhead. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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