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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Shi, Zhuan Yao, Zhenyu Yi, Liping Yu, Han Zhang, Lan Li, Xiang-Yang University of Science and Technology of China School of Computer Science and Technology China Univerisity of Liverpool Department of Mathematical Sciences United Kingdom Nankai University College of Computer Science China Nanyang Technological University School of Computer Science and Engineering Singapore

ISBN: (纸本)9798350304855

Federated crowdsourcing, as a dynamic privacy-preserving distributed machine learning approach, has attracted significant research attention recently. Compared to federated learning (FL), clients can dynamically collect and label fresh data as required, and train model on the updated data. Existing research has mainly focused on incentivizing clients to spend more effort on data collection and labelling in order to improve FL model performance. However, as data collection and labeling require human effort, they need to balance work and rest. This need has been overlooked by existing federated crowdsourcing research. In this paper, we propose the Federated Workforce Management (FedWM) approach to bridge this important gap. It first measures the contribution of each client to the FL model, and estimates the urgency collecting new labelled data based on the rate of change of the contribution. Then, FedWM computes the working time taking into account of the client's maximum productivity and self-reported mood. Finally, it takes both the urgency level of obtaining new data and clients' productivity into consideration to provide scheduling services that advise the clients on work-rest balance in a given time slot based on Lyapunov optimization. Through theoretical analysis, we provide the performance bounds of FedWM. Through extensive experiments based on real-world datasets, we demonstrate that FedWM achieves significantly more advantageous tradeoffs between client rest and FL model performance compared to existing approaches. To the best of our knowledge, it is the first federated crowdsourcing framework designed to achieve productive laziness. © 2023 IEEE.

关键词： Crowdsourcing

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Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagnostic Accuracy

Skin Lesion Decode A CNN-SVM Convergence for Enhanced Diagno...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Suryavanshi, Ankita Mehta, Shiva Rana, Samir Manwal, Manika Jain, Vishal Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Computer Science and Engineering Uttarakhand Dehradun India Graphic Era Deemed to Be University Computer Science and Engineering Uttarakhand Dehradun India Sharda University Computer Science and Engineering Sharda School of Engineering and Technology U.P. Greater Noida India

ISBN: (纸本)9798350361537

This research study investigates the development and evaluation of a merged CNN and SVM model for detecting and classifying skin lesions. The research systematically divides skin lesions into five categories that allow a thorough analysis of the model's diagnostic skills. The CNN-SVM model is developed and tested using a high-quality dataset, and its accuracy, recall, and F1 scores provide insights into specific performance aspects of the model. The results show a high level of precision for all the classes that vary from 92.95% for Class 1 to 95.95% for Class 5, which suggests that the model is good at making optimistic predictions. The recall measure, a sensitivity measure of the model, varies from 91.44% for Class 3 to 98.47% for Class 1, indicating the model's capability to recognize true positives correctly. The F1-score, which balances precision and recall, is always high, fluctuating from 93.00% for Class 3 to 96.28% for Class 5, reflecting the model's well-balanced categorization. Several types of averages are computed to assess the model's performance thoroughly. The macro average indicates accuracy of 94.70%, recall of 94.81%, and F1-score of 94.73%, with equal weight given to each class. With class distribution, the weighted average is as follows: accuracy - 94.74%, recall - 94.72%, F1-score - 94.71%. The micro average indicating the sum of contributions from all classes is 94.72% for all three parameters across the board. The model is entirely accurate, with an overall accuracy of 94.72397, showing the model's strong ability to generalize across different skin lesion types. The statistical measures confirm the CNN-SVM model as a practical approach to accurate and reliable classification of skin lesions, revealing high clinical potential. The paper's impressive experimental and statistical analyses demonstrate the power of the model and its ability to improve dermatological diagnostic procedures. © 2024 IEEE.

关键词： Neural networks

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Deep Learning Based Encoder-Decoder Model for Skin Cancer Classification 7

Deep Learning Based Encoder-Decoder Model for Skin Cancer Cl...

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7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Murmu, Anita Swati, Shipra Kumar, Naween Murmu, Rajiv Dash, Yajnaseni Bennett University School of Computer Science Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Jharkhand Dhanbad India Bennett University School of Artificial Intelligence Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350350067

In the medical field, identifying skin cancerous areas and characteristics in dermoscopy images enhances automatic skin cancer classification and diagnosis. However, due to an absence of training data, imbalanced classes, variances within classes, and a failure to concentrate on semantically significant lesion parts, accurately classifying MCS cancer is still challenging. To address these problems, the proposed novel UNet with MobileNet model has been utilized to divide skin cancer into seven different classes. The MobileNet approach is used as a classifier instead of a UNet decoder to avoid overfitting. Additionally, the expert learning framework based on specific domain functions is implemented by the classification model. The performance of test findings with a UNet-MobileNet across seven different Human Machine classes using 10,000 training images (HAM10000) dataset. The overall performance of the HAM10000 dataset's seven various classes is determined by UNet-MobileNet experiments. The accuracy, Dice, and recall evaluation matrices have been used to assess the proposed scheme. The proposed approach outperforms existing techniques on the skin lesion dataset. © 2024 IEEE.

关键词： Lung cancer

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Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

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China Communications 2024年第11期21卷 56-74页

作者： Yang Jie He Jingchao Cheng Nan Yin Zhisheng Han Dairu Zhou Conghao Sun Ruijin School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210000China Department of Electrical and Computer Engineering University of WaterlooWaterloo ON N2L 3G1Canada

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has *** emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user *** with the MEC and satellite networks,the network is empowered popular content ubiquitously and *** the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this ***,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale *** the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content ***,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale ***,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching ***,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

关键词： cache placement coded caching graph convolutional network(GCN) mobile edge caching(MEC) multilayer satellite network

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Demystifying the Transfer Learning based Detection of Animal Diseases from Images

Demystifying the Transfer Learning based Detection of Animal...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Khan, Asif Paliwal, Dev Jaikar, Ritank Attri, Shree Harsh Sharda University Sharda School of Engineering & Technology Department of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350323887

An animal's normal state is altered by sickness which can stop or change critical processes. Concerns over animal diseases have existed as animal lovers interacted with animals and this concern is reflected in the first ideas about religion and magic. Animal illnesses still pose a threat, primarily due to the potential financial costs and risk of human transmission. The study, prevention, and treatment of diseases in animals including wild animals and those utilized in scientific research are the focus of the medical specialty known as veterinary medicine. This research examines recent developments in image-based animal illness detection and predicting the best deep learning model to detect the animal diseases. People now have a better grasp of machine learning and its potential uses in treating animal diseases as a result of the discussion of this paper. Regarding accuracy, DenseNet169 has performed remarkably better than other models whereas ResNet50V2 has least accuracy. These models are trained on the dataset which is built using images collected by the Authors. © 2023 IEEE.

关键词： Animals

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Touch-less Remedial Game Prototype for Hand Rehabilitation - Feedback Results 35

Touch-less Remedial Game Prototype for Hand Rehabilitation -...

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35th Australian computer-Human Interaction Conference, OzCHI 2023

作者： Zsolczay, Rodney G. Perrin, Dimitri Maire, Frederic Türkay, Selen School of Electrical Engineering and Computer Science Queensland University of Technology BrisbaneQLD Australia

ISBN: (纸本)9798400717079

Remedial hand therapy can be required for many reasons, so burn injuries were used as a specific case for development of the remedial prototype of this paper. With burns, 80% of injuries involve the hands. Infection is also a constant concern with burns patients. In this paper, we sought feedback on our remedial prototype, from both professional hand therapists and past patients. Both groups gave very positive responses. With regard to burn injuries, the prototype had been developed with the following three considerations: 1) Touch-less hand control to reduce chances of infection. 2) computer vision datasets for damaged/bandaged hands, as most existing datasets target healthy hands. 3) Ensure that the remedial game is engaging to help distract from pain and provide motivation, thereby encouraging patients to perform their required remedial exercises. Our contribution is the continued development of an application for hand rehabilitation, where hand detection and classification were specifically developed for a minority group of damaged/bandaged hands. The feedback obtained here helps to focus further iterative development. © 2023 ACM.

关键词： Feedback

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Q2Logic: A Coarse-Grained FPGA Overlay targeting Schrödinger Quantum Circuit Simulations

Q2Logic: A Coarse-Grained FPGA Overlay targeting Schrödinge...

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2023 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2023

作者： Podobas, Artur KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350311990

Quantum computing is emerging as an important (but radical) technology that might take us beyond Moore's law for certain applications. Today, in parallel with improving quantum computers, computer scientists are relying heavily on quantum circuit simulators to develop algorithms. Most existing quantum circuit simulators run on general-purpose CPUs or GPUs. However, at the same time, quantum circuits themselves offer multiple opportunities for parallelization, some of which could map better to other architecture- architectures such as reconfigurable systems. In this early work, we created a quantum circuit simulator system called Q2Logic. Q2Logic is a coarse-grained reconfigurable architecture (CGRA) implemented as an overlay on Field-Programmable Gate Arrays (FPGAs), but specialized towards quantum simulations. We described how Q2Logic has been created and reveal implementation details, limitations, and opportunities. We end the study by empirically comparing the performance of Q2Logic (running on a Intel Agilex FPGA) against the state-of-the-art framework SVSim (running on a modern processor), showing improvements in three large circuits(#qbit≥27), where Q2Logic can be up-to 7x faster. © 2023 IEEE.

关键词： Circuit simulation

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Ensemble Deep Learning Framework for Classification of Skin Lesions

Ensemble Deep Learning Framework for Classification of Skin ...

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2023 International Conference on Circuit Power and Computing Technologies, ICCPCT 2023

作者： Patil, Shantakumar B Ramana Rao, H.V. Chatrapathy, K. Kiran, Ajmeera Kumar, A Shiva Shaker Reddy, Pundru Chandra Sai Vidya Institute of Technology Computer Science and Engineering Bengaluru Karnataka Rajanukunte India Hyderabad India School of Computing &information Technology REVA University Karnataka Bengaluru India MLR Institute of Technology Computer Science and Engineering Hyderabad Telangana Dundigal India CMR College of Engineering & Technology Computer Science and Engineering Telangana Hyderabad India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350333244

Skin cancer is one of the majority prevalent malignancies that can be diagnosed visually and then further investigated with dermoscopic analysis and other procedures. Several skin-lesion (SL) classification methods using deep learning (DL) based on convolution-neural-network (CNN) and annotated skin photos illustrate modified results because visual observation provides the opportunity to use artificial-intelligence (AI) to intercept the various skin images at an early stage. In this regard, the research proposes a trustworthy method for identifying skin cancer using dermoscopy images, with the goal of bettering the visual perception and diagnostic abilities of medical experts. SL region of interest (RoI) segmentation from dermoscopy pictures was performed using swarm intelligence (SI) algorithms, and the RoI marked as the best segmentation oucome was subjected to feature extraction using the Grasshopper Optimization Algorithm (GOA). Using CNN and the ISIC-2017, ISIC-2018, and PH-2 data sets, the SLs are divided into two categories. Classification accuracy-sensitivity,-specificity-F -measure, precision, MCC, dice coefficient, and Jaccard index are used to evaluate the results of the proposed segmentation and classification strategies, with an average of 98.42%, 977.33%, and 0.97040%, respectively. Our proposed model outperforms the state-of the-art in every metric we considered © 2023 IEEE.

关键词： Learning algorithms

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Underwater image recovery method considering target polarization characteristics 4

Underwater image recovery method considering target polariza...

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4th International Conference on computer Vision, Image and Deep Learning, CVIDL 2023

作者： Wang, Zhe Yu, Haiyang Peng, Jin School of Computer Science and Information Engineering Hefei University of Technology AnHui Hefei China

ISBN: (纸本)9798350326444

In underwater polarization imaging, the estimation of the polarization characteristics of the target is also the key to recover the original image. Most current recovery methods ignore the polarization characteristics of the target and require manual selection of the region for parameter estimation. This not only causes errors in the recovery results, but also has the limitation that a background region must exist. In this paper, we derive the reconstruction model of the original underwater image from the underwater imaging model and the polarization imaging model, considering the polarization characteristics of the target. In this paper, the background light polarization degree is obtained using the filtered median algorithm, and the bright channel principle is used to obtain the background light at infinity. Compared with the traditional method, the method in this paper can reduce the error. Finally, this paper makes improvements to the perfect reflection algorithm and enhances the recovered image. © 2023 IEEE.

关键词： Image enhancement

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A User Study on Contrastive Explanations for Multi-Effector Temporal Planning with Non-Stationary Costs 36

A User Study on Contrastive Explanations for Multi-Effector ...

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36th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2024

作者： Liu, Xiaowei McAreavey, Kevin Liu, Weiru School of Computer Science University of Bristol Bristol United Kingdom School of Engineering Mathematics and Technology University of Bristol Bristol United Kingdom

ISBN: (纸本)9798331527235

In this paper, we adopt constrastive explanations within an end-user application for temporal planning of smart homes. In this application, users have requirements on the execution of appliance tasks, pay for energy according to dynamic energy tariffs, have access to high-capacity battery storage, and are able to sell energy to the grid. The concurrent scheduling of devices makes this a multi-effector planning problem, while the dynamic tariffs yield costs that are non-stationary (alternatively, costs that are stationary but depend on exogenous events). These characteristics are such that the planning problems are generally not supported by existing PDDL-based planners, so we instead design a custom domain-dependent planner that scales to reasonable appliance numbers and time horizons. We conduct a controlled user study with 128 participants using an online crowd-sourcing platform based on two user stories. Our results indicate that users provided with contrastive questions and explanations have higher levels of satisfaction, tend to gain improved understanding, and rate the helpfulness more favourably with the recommended AI schedule compared to those without access to these features. © 2024 IEEE.

关键词： Battery storage

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