Cloud computing primarily focuses on providing secure and reliable access to geographically distributed resources. Cloud computing is a parallel and distributed system that simplifies the virtualization of distributed computing. Job scheduling is one of the most important services in cloud computing. Job scheduling is used to schedule users' jobs to allocate suitable resources in a cloud environment. Recently, several efficient job scheduling algorithms have been proposed for cloud computing. The purpose of designing these algorithms is to achieve time and cost optimization for job execution. On the other hand, it is necessary to provide a model to assess the reliability of the different job scheduling algorithms. Unfortunately, no accurate and complete model has been proposed to evaluate the reliability of these algorithms. Scheduling failures are inevitable in cloud computing due to the harsh deployment environment, resource migration, non-compliance time or cost, etc. Therefore, providing a reliability model is necessary for successful scheduling algorithms. In this paper, a reliability model based on a discrete-time Markov chain is incorporated for the famous scheduling algorithms MCT, MET, BCO, HCOC, and PPO. We propose an approach to evaluating the reliability of scheduling algorithms based on time and cost constraints. The proposed approach is modeled by a Discrete Time Markov Chain, and the results show the reliability of scheduling algorithms in different states.
To address issues such as data sparsity, overfitting, and the inability to fully extract latent information in traditional recommendation methods, a new insurance product recommendation algorithm is proposed that combines knowledge graphs improved by matrix factorization with deep neural networks (DNN). First, to tackle the issue of sparse data in existing insurance products, an improved knowledge graph recommendation algorithm based on matrix factorization (FunkSVD) is proposed. By training the data through matrix factorization the problem of data sparsity is mitigated. After refining the customer-product matrix, knowledge graph triples are constructed based on customer characteristics and insurance product features such as insured age and coverage. Feature extraction and customer preference prediction are carried out using an alternating learning approach within a multi-task knowledge graph framework. Then, to alleviate issues like local optima and vanishing gradients during recommendation, DNN is applied for further recommendation. A fully connected layer is constructed, and forward propagation and backpropagation algorithms are used to train customer features and product matrices, predicting customer purchasing behavior and generating recommendations. Finally, comparative experimental results show that, compared to other recommendation algorithms such as collaborative filtering and DNN, the proposed algorithm improves accuracy, recall, F1 score, and other metrics. This algorithm not only speeds up recommendations but also improves recommendation quality.
To address the issue of low recall rates in detecting small faces within crowded scenes, this paper conducts an analysis of the primary reasons behind this challenge and introduces a real-time face detection system named CF-YOLO (Crowded-Face-YOLO). The study identifies a crucial factor contributing to this problem, which is the insufficient provision of positive samples for small faces during the training phase by conventional face detectors. To tackle this limitation, a Sa-SimOTA strategy is proposed to enhance the availability of positive samples for small targets. Additionally, in the post-processing stage, the utilization of the non-maximum suppression (NMS) algorithm for assigning optimal bounding boxes to detected faces is discussed. The traditional fixed threshold employed in the NMS algorithm for decision-making often results in the loss of small face detection boxes in crowded scenarios. To alleviate this issue, a Soft-Face-NMS algorithm is introduced, which incorporates facial feature variables into the Soft-NMS algorithm for weighted processing, facilitating the selection of face boxes with higher confidence in overlapping regions. Furthermore, to augment the feature extraction capabilities of the YOLO backbone, an EMA+ attention module is proposed, and modifications are made to the network structure of YOLOv7 to enhance the extraction of more effective features conducive to small face detection. The proposed model demonstrates impressive accuracy rates of 97.3 %, 96.4 %, and 92.8 % on the easy, medium, and hard subsets of the Wider-Face dataset, respectively. Notably, the accuracy achieved on the hard subset approaches the state-of-the-art level, which further demonstrates the effectiveness of our proposed approach for face detection in crowded scenes.
Deep generative models are widely used in recommendation systems because of their ability to deal with uncertainty by learning inherent data distribution. Among deep generative models, Generative Adversarial Networks (GAN) perform well in recommendation tasks. However, existing Collaborative Filtering (CF) recommendation algorithms based on GAN generally have problems such as mode collapse and training instability, which are further aggravated by sparse and noisy recommendation data. To solve these problems, a Collaborative Diffusion Generative Adversarial Networks (CDGAN) framework for recommendation systems is proposed in this paper. Specifically, CDGAN framework is mainly composed of three parts: feature encoder, diffusion generator, and self-attention discriminator. The feature encoder extracts the rating information and side information to obtain potential feature vectors to alleviate the data sparsity problem. The diffusion generator simulates the complex nonlinear mode of the user-item interaction matrix through forward diffusion and reverse denoising to reconstruct the user-item interaction matrix to alleviate the problem of data noise. The self-attention discriminator obtains the user's specific behaviors and preferences through the self-attention mechanism to improve the discriminator's discriminating ability. Furthermore, a corresponding CDGAN recommendation algorithm is designed based on our proposed CDGAN framework. Comprehensive experiments are conducted on three real-world recommendation datasets. The experimental results indicate that, when compared with multiple representative recommendation models, the proposed CDGAN model achieves superior performance in the evaluation metrics Precision and Recall on all datasets, thereby proving its effectiveness.
