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Service Level Agreement Monitoring as a Service: An Independent Monitoring Service for Service Level Agreements in Clouds

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Big Data 2023年第5期11卷 339-354页

作者： Badshah, Afzal Jalal, Ateeqa Farooq, Umar Rehman, Ghani-Ur Band, Shahab S. Iwendi, Celestine Department of Computer Science and Software Engineering International Islamic University Islamabad Pakistan Department of Computer Science University of Science and Technology Bannu Pakistan Department of Computer Science and Bioinformatics Khushal Khan Khattak University Karak Pakistan Future Technology Research Center College of Future National Yunlin University of Science and Technology Douliou Taiwan School of Creative Technologies University of Bolton Bolton United Kingdom

The cloud network is rapidly growing due to a massive increase in interconnected devices and the emergence of different technologies such as the Internet of things, fog computing, and artificial intelligence. In response, cloud computing needs reliable dealings among the service providers, brokers, and consumers. The existing cloud monitoring frameworks such as Amazon Cloud Watch, Paraleap Azure Watch, and Rack Space Cloud Kick work under the control of service providers. They work fine;however, this may create dissatisfaction among customers over Service Level Agreement (SLA) violations. Customers’ dissatisfaction may drastically reduce the businesses of service providers. To cope with the earlier mentioned issue and get in line with cloud philosophy, Monitoring as a Service (MaaS), completely independent in nature, is needed for observing and regulating the cloud businesses. However, the existing MaaS frameworks do not address the comprehensive SLA for customer satisfaction and penalties management. This article proposes a reliable framework for monitoring the provider’s services by adopting third-party monitoring services with clearcut SLA and penalties management. Since this framework monitors SLA as a cloud monitoring service, it is named as SLA-MaaS. On violations, it penalizes those who are found in breach of terms and condition enlisted in SLA. Simulation results confirmed that the proposed framework adequately satisfies the customers (as well as service providers). This helps in developing a trustworthy relationship among cloud partners and increases customer attention and retention. © Mary Ann Liebert, Inc.

关键词： Sales

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Construction of PCOS Prediction Model based on BP Neural Network 6

Construction of PCOS Prediction Model based on BP Neural Net...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Hu, Xianqing Yadav, Amit Khan, Asif Sah, Abhishek Pratap Azam, Sami School of Computer and Software Chengdu Neusoft University Chengdu China Faculty of Science and Technology Charles Darwin University NT Australia Department of Computer Application Integral University Lucknow India School of Management Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331522667

Polycystic ovary syndrome (PCOS), a common endocrine-metabolic disorder affecting about 10-13% of women during reproductive age worldwide, often leads to irregular menstruation, infertility, obesity, and long-term health risks such as diabetes and cardiovascular disease. In this paper, clinical data from PCOS patients and healthy control group were collected from ten hospitals containing 37 key indicators such as age, weight, BMI, menstrual cycle, etc. BP (Back Propagation) Neural Network prediction models were constructed and compared using the collected data and the cross-validation method was used for parameter tuning. It was found that BP Neural Network performed particularly well on the test set, and both demonstrated high prediction accuracy and generalization ability which provided strong evidence for the early identification and early valuable intervention opportunities for PCOS patients. © 2025 IEEE.

关键词： Prediction models

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A Static Bi-dimensional Sample Selection for Federated Learning with Label Noise 28th

A Static Bi-dimensional Sample Selection for Federated Lear...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Rong, Qian Yuan, Ling Li, Guohui Li, Jianjun Zhang, Lu Ding, Xuanang School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

In real-world Federated learning(FL), client training data may contain label noise, which can harm the generalization performance of the global model. Most existing noisy label learning methods rely on sample selection strategies that treat small-loss samples as correctly labeled ones, and large-loss samples as mislabeled ones. However, large-loss samples may also be valuable hard samples with correct labels. Also, these sample selection strategies have an issue in FL setting: Moreover sample selection strategies require training two models simultaneously and are executed in every mini-batch, which increases the communication cost and client computation. In this paper, we propose an efficient multi-stage federated learning framework to tackle label noise. Firstly, we use self-supervision pre-training to extract category-independent features without using any labels. Secondly, we propose a federated static two-dimensional sample selection(FedSTSS) method, which uses category probability entropy and loss as separation metrics to identify samples. Notably, to improve the accuracy of recognition samples, we add a weight entropy minimization term to the cross-entropy loss function. Finally, we use a semi-supervised method to finetune the global model on identified clean samples and mislabeled samples. Extensive experiments on multiple synthetic and real-world noisy datasets demonstrate that our method outperforms the state-of-the-art methods. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Supervised learning

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Progressive Stepwise Diffusion Model with Dual Decoders for Semi-Supervised Medical Image Segmentation

Progressive Stepwise Diffusion Model with Dual Decoders for ...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Huang, Xiaolin Lin, Jingchun Chen, Jiacheng Ma, Xiao Chen, Bingzhi Lu, Guangming South China Normal University School of Software Foshan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798350386226

Semi-supervised medical image segmentation tasks aim to harness the potential of vast amounts of unlabeled data using a limited amount of annotated data. Denoising Diffusion Probabilistic Models, which have achieved significant success in image generation, are gradually being explored for their potential in semantic image segmentation. However, their application in semi-supervised medical image segmentation is still in its early stages. Initially, due to the high randomness of diffusion models, the pseudo-labels generated during the early training phase may mislead the processing of unlabeled data. Additionally, the use of fixed-time steps for random sampling during training limits the ability of the model to learn effective denoising functions at an early stage. To address these issues, we propose an innovative framework named Progressive Stepwise Diffusion Network with Dual Decoders (PSDD) for semi-supervised medical image segmentation. This framework incorporates an additional normal decoder into the denoising diffusion encoder-decoder structure to provide more accurate labels and employs a Progressive Incremental Step strategy to gradually train the model for longer generation processes. Evaluated on two 2D colon polyp segmentation datasets and a 3D Left Atrium dataset, the experimental results demonstrate significant performance improvements over current advanced methods, thereby validating the effectiveness and potential of this framework in handling complex semi-supervised learning scenarios. © 2024 IEEE.

关键词： Semantic Segmentation

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LBPA: A Latent Behavior Patterns Augmentation Model for ID-based Recommendation 2

LBPA: A Latent Behavior Patterns Augmentation Model for ID-b...

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2nd International Conference on Intelligent Communication and Networking, ICN 2024

作者： Bai, Yu Wang, Yuanlai Li, Jun Lian, Peng School of Computer Science Shenyang Aerospace University Shenyang China Shenyang Northern Software College of Information Technology Shenyang China

ISBN: (纸本)9798331519162

ID-based recommendation is a classic method that relies on unique identifiers of users or items for recommendation. Current methods often encounter issues, such as sparse interaction data between users and items, and the lack of semantic information in symbolic IDs. To address these challenges, this paper assumes the existence of latent pattern correlations between user-item interaction behaviors across different domains, and proposes LBPA, a latent behavior patterns augmentation model. This model incorporates auxiliary-domain information into the target-domain recommendation task by introducing semantic patterns of interaction behavior. This is achieved through the joint encoding of information from both domains by using graph neural networks. Experimental results on four public datasets demonstrate that, compared to current state-of-the-art (SOTA) models, the proposed model yields superior recommendation performance, with improvements in Recall@20 ranging from 3.09% to 32.93% and NDCG@20 ranging from 1.59% to 40%. This indicates that our proposed model can transfer user-item interaction information and item description information from the auxiliary domain to the target domain, and this transfer of information can enhance the effectiveness of ID-based recommendation in the target domain. © 2024 IEEE.

关键词： Graph neural networks

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Sentiment Analysis of Low-Resource Language Literature Using Data Processing and Deep Learning

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computers, Materials & Continua 2024年第4期79卷 713-733页

作者： Aizaz Ali Maqbool Khan Khalil Khan Rehan Ullah Khan Abdulrahman Aloraini Department of IT and Computer Science Pak-Austria Fachhochschule:Institute of Applied Sciences and TechnologyHaripur22620Pakistan Software Competence Center Hagenberg Softwarepark 32aHagenberg4232Austria Department of Computer Science School of Engineering and Digital SciencesNazarbayev UniversityAstana010000Kazakhstan Department of Information Technology College of ComputerQassim UniversityP.O.Box 1162BuraydahSaudi Arabia

Sentiment analysis, a crucial task in discerning emotional tones within the text, plays a pivotal role in understandingpublic opinion and user sentiment across diverse *** numerous scholars conduct sentiment analysisin widely spoken languages such as English, Chinese, Arabic, Roman Arabic, and more, we come to grapplingwith resource-poor languages like Urdu literature which becomes a challenge. Urdu is a uniquely crafted language,characterized by a script that amalgamates elements from diverse languages, including Arabic, Parsi, Pashtu,Turkish, Punjabi, Saraiki, and more. As Urdu literature, characterized by distinct character sets and linguisticfeatures, presents an additional hurdle due to the lack of accessible datasets, rendering sentiment analysis aformidable undertaking. The limited availability of resources has fueled increased interest among researchers,prompting a deeper exploration into Urdu sentiment analysis. This research is dedicated to Urdu languagesentiment analysis, employing sophisticated deep learning models on an extensive dataset categorized into fivelabels: Positive, Negative, Neutral, Mixed, and Ambiguous. The primary objective is to discern sentiments andemotions within the Urdu language, despite the absence of well-curated datasets. To tackle this challenge, theinitial step involves the creation of a comprehensive Urdu dataset by aggregating data from various sources such asnewspapers, articles, and socialmedia comments. Subsequent to this data collection, a thorough process of cleaningand preprocessing is implemented to ensure the quality of the data. The study leverages two well-known deeplearningmodels, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), for bothtraining and evaluating sentiment analysis performance. Additionally, the study explores hyperparameter tuning tooptimize the models’ efficacy. Evaluation metrics such as precision, recall, and the F1-score are employed to assessthe effectiv

关键词： Urdu sentiment analysis convolutional neural networks recurrent neural network deep learning natural language processing neural networks

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An improved pulse coupled neural networks model for semantic IoT

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Digital Communications and Networks 2024年第3期10卷 557-567页

作者： Rong Ma Zhen Zhang Yide Ma Xiping Hu Edith C.H.Ngai Victor C.M.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of Medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and Computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse Coupled Neural Network(PCNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PCNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse Coupled Neural Networks(IPCNN)model is proposed in this *** IPCNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPCNN on the gray images and natural images from the Matlab and Berkeley Segmentation *** IPCNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

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DIG: Improved DINO for Graffiti Detection

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3557-3569页

作者： Wang, Bingshu Mao, Qianchen Liu, Aifei Chen, Long Chen, C. L. Philip Northwestern Polytechnical University School of Software Xi’an710129 China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen518060 China University of Macao Department of Computer and Information Science China South China University of Technology School of Computer Science and Engineering Guangzhou510641 China

Graffiti detection is essential in historic building protection and urban neighborhood management. Graffiti detection has made significant progress in recent years based on the development of deep learning. However, small-scale graffiti, interference from the background, and the false detection of word parts in graffiti make it a challenging problem. This article proposes a Transformer-based high-precision graffiti detection method, namely DIG. Precisely, it consists of three modules: 1) Spatial query selection (SQS), scale-aware IoU loss (SIL);2) Denoising Task with binary contrastive denoising (BCDN);and 3) IoU-guided box denoising (IBD) modules. To detect small-scale graffiti, this paper proposes SIL to help the loss function to perceive small-scale graffiti and large-scale graffiti fairly. To reduce the false detection of word parts, this article presents the SQS module, which integrates spatial information into the query selection process of the Encoder to filter out falsely detected bounding boxes within the graffiti. To reduce the interference from the background, this article introduces a denoising task with BCDN and IBD modules, improving the model’s ability to distinguish graffiti from the background and accurately select appropriate bounding boxes. A large number of experimental results on the STORM dataset show that our method achieves state-of-the-art results with an AP50 of 87.9%. Moreover, DIG achieved competitive results on the FineFM dataset for mask detection. This indicates that DIG can also be conveniently transferred to detect other scenarios. © 2013 IEEE.

关键词： Historic preservation

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Legal Text Retrieval with Contrastive Representation Learning and Evolutionary Data Augmentation 13

Legal Text Retrieval with Contrastive Representation Learnin...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Zhou, Youhua Yan, Xueming Huang, Han Yan, Haowen Chen, Minghao School of Software Engineering South China University of Technology Guangzhou China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China School of Computer Science & Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350308365

Legal text retrieval holds significant importance in the audit field, posing a challenge as a semantic matching problem. Despite the success of text semantic matching methods, particularly with the advent of large language models, these approaches face challenges when applied to domain-specific tasks, like legal text retrieval. Specifically, issues arise due to the concentrated distribution of data within the specific domain and the insufficient number of training samples. To address these challenges, this paper introduces a text semantic matching model tailored for the task of legal text retrieval, leveraging contrastive learning and evolutionary algorithms. A contrastive learning-based embedding model, which learns semantic representations in a feature space, is used to minimize the distance between matched text pairs and maximize the distance between unmatched text pairs. Additionally, an evolutionary algorithm-based sample augmentation model is introduced to augment the sample set and enhance the representational capabilities of the samples. The efficacy of the proposed method is evaluated in the context of legal text retrieval in the auditing field, and the experimental results reveal promising outcomes, with the proposed method achieving a Hits@l accuracy of 53.09%, a 2.99% improvement over the best baseline model. The Hits@20 accuracy reaches 75.15%, representing a 2.69% enhancement compared to the state-of-the-art methods. © 2024 IEEE.

关键词： Contrastive Learning

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Combining Autoencoder with Adaptive Differential Privacy for Federated Collaborative Filtering 28th

Combining Autoencoder with Adaptive Differential Privacy fo...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Ding, Xuanang Li, Guohui Yuan, Ling Zhang, Lu Rong, Qian School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Software Engineering Huazhong University of Science and Technology Wuhan China School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9783031306365

Recommender systems provide users personalized services by collecting and analyzing interaction data, undermining user privacy to a certain extent. In federated recommender systems, users can train models on local devices without uploading raw data. Nevertheless, model updates transmitted between the user and the server are still vulnerable to privacy inference attacks. Several studies adopt differential privacy to obfuscate transmitted updates, but they ignore the privacy sensitivity of recommender model components. The problem is that components closer to the original data are more susceptible to privacy leakage. To address this point, we propose a novel adaptive privacy-preserving method combining autoencoder for federated collaborative filtering, which guarantees privacy meanwhile maintaining high model performance. First, we extend the variational autoencoder (VAE) to federated settings for privacy-preserving recommendations. Additionally, we analyze the privacy risks of the variational autoencoder model in federated collaborative filtering. Subsequently, we propose an adaptive differential privacy method to enhance user privacy further. The key is to allocate less privacy budget for sensitive layers. We apply a metric based on model weights to determine the privacy sensitivity of each layer in the autoencoder. Then we adaptively allocate the privacy budget to the corresponding model layer. Extensive experiments and analysis demonstrate that our method can achieve competitive performance to non-private recommender models meanwhile providing fine-grained privacy protection. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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