To address the issue of network active attacks such as tampering for industrial cloud storage system data， to achieve the goal of secure sharing of industrial data in cloud storage， and to ensure the confidentiality， integrity， and availability of industrial data transmission and storage processes， a data tamper-proof batch auditing scheme based on industrial cloud storage systems was proposed. In this scheme， a homomorphic digital signature algorithm based on bilinear pairing mapping was proposed， enabling a third-party auditor to achieve batch tamper-proof integrity detection of industrial cloud storage system data， and feedback the tamper-proof integrity auditing results to engineering service end users timely. Besides， the computational burden on engineering service end users was reduced by adding auditors， while ensuring the integrity of industrial encrypted data during transmission and storage processes. Security analysis and performance comparison results demonstrate that the proposed scheme reduces the third-party auditing computational cost significantly by reducing the third-party auditor’s computational cost from O（n） bilinear pairing operations to O（1） constant-level bilinear pairing operations through the design of tamper-proof detection vectors. It can be seen that the proposed scheme is suitable for lightweight batch auditing scenarios that require tamper-proof detection of a large number of core data files of industrial cloud storage systems.
Local Feature Selection （LFS） methods partition the sample space into multiple local regions and select the optimal feature subset for each region to reflect local heterogeneous information. However， the existing LFS methods partition local regions around each sample and find the optimal feature subset， resulting in low optimization efficiency and limited applicability. To address this issue， a new evolutionary Bi-level adaptive Local Feature Selection （BiLFS） algorithm was proposed. The LFS problem was formulated as a bi-level optimization problem， with feature subsets and locally optimized regions as the decision variables. At the upper level， Non-dominated Sorting Genetic Algorithm Ⅱ was employed to find the optimal feature subsets for the selected local regions， with region purity and selected feature ratio as the objective functions. At the lower level， based on the upper-level solution， local region clustering analysis was used to obtain center samples within each region， followed by local region fusion to eliminate unnecessary regions and update the population of necessary regions. Experimental results on 11 UCI datasets demonstrate that BiLFS achieves an average classification accuracy up to 98.48%， and an average computation time down to 9.51% compare to those of non-adaptive LFS methods based on evolutionary algorithms， significantly improving computational efficiency to the level of linear programming-based LFS methods. Visual analysis of the locally optimized regions selected by the BiLFS algorithm during the iteration process indicates the stability and reliability of selecting necessary local regions.
Aiming at the issues in the existing prediction methods， such as only predicting b and y backbone fragment ions， as well as single model's difficulty in capturing the complex relationships within peptide sequences， a theoretical tandem mass spectrometry prediction method for peptide sequences based on Transformer and Gated Recurrent Unit （GRU）， named DeepCollider， was proposed. Firstly， through self-attention mechanism and long-distance dependencies， the deep learning architecture combining Transformer and GRU was used to enhance the modeling ability of relationship between peptide sequences and fragment ion intensities. Secondly， unlike the existing methods encoding peptide sequences to predict all b and y backbone ions， fragmentation flags were utilized to mark fragmentation sites within peptide sequences， thereby enabling the encoding of fragment ions at specific fragmentation sites and prediction of the corresponding fragment ions. Finally， Pearson Correlation Coefficient （PCC） and Mean Absolute Error （MAE） were employed as evaluation metrics to measure the similarity between predicted spectrometry and experimental spectrometry. Experimental results demonstrate that DeepCollider shows advantages in both PCC and MAE metrics compared to the existing methods limited to predicting b and y backbone fragment ions， such as pDeep and Prosit methods， with an increase of 0.15 in PCC value and a decrease of 0.005 in MAE value. It can be seen that DeepCollider not only predicts b， y backbone ions and their corresponding dehydrated and deaminated neutral loss ions， but also further improves the peak coverage and similarity of theoretical spectrometry prediction.
Aiming at the hidden danger of fire caused by electric bicycles and gas tanks taken into elevators， an improved attention mechanism was proposed to detect dangerous goods in elevator scene， and a method based on the mechanism was proposed. With YOLOX-s as the baseline model， firstly， a depthwise separable convolution was introduced in the enhanced feature extraction network to replace the standard convolution， which improved the reasoning speed of the model. Secondly， an Efficient Convolutional Block Attention Module （ECBAM） based on mixed-domain was proposed and embedded into the backbone feature extraction network. In the channel attention part of ECBAM， two fully connected layers were replaced by a one-dimensional convolution， which not only reduced the complexity of Convolutional Block Attention Module （CBAM） but also improved the detection precision. Finally， a multi-frame collaboration algorithm was proposed to reduce the false alarms of dangerous goods’ intrusion into the elevator by combining the dangerous goods detection results of multiple images. Experimental results show that compared with YOLOX-s， the improved model can increase the mean Average Precision （mAP） by 1.05 percentage points， reduce the floating point computational cost by 34.1% and reduce the model size by 42.8%. The improved model reduces false alarms in practical applications and meets the precision and speed requirements of dangerous goods detection in elevator scene.
With the rapid development of Internet of Things （IoT）， a large amount of data generated in edge scenarios such as sensors often needs to be transmitted to cloud nodes for processing， which brings huge transmission cost and processing delay. Cloud-edge collaboration provides a solution for these problems. Firstly， on the basis of comprehensive investigation and analysis of the development process of cloud-edge collaboration， combined with the current research ideas and progress of intelligent cloud-edge collaboration， the data acquisition and analysis， computation offloading technology and model-based intelligent optimization technology in cloud edge architecture were analyzed and discussed emphatically. Secondly， the functions and applications of various technologies in intelligent cloud-edge collaboration were analyzed deeply from the edge and the cloud respectively， and the application scenarios of intelligent cloud-edge collaboration technology in reality were discussed. Finally， the current challenges and future development directions of intelligent cloud-edge collaboration were pointed out.
Aiming at the problems of insufficient fraud samples, expensive data labeling and low accuracy of traditional Euclidean space model, a new One-Class medical insurance fraud detection model based on Graph convolution and Variational Auto-Encoder (OCGVAE) was proposed. Firstly, a social network was established through patient visit records, the weight relationships between the patients and the doctors were calculated, and a 2-layer Graph Convolutional neural Network (GCN) was designed as the input of the social network data to reduce the data dimension of the social network. Secondly, a Variational Auto-Encoder (VAE) was designed to implement the model training under only one-class fraud sample label. Finally, a Logistic Regression (LR) model was designed to discriminate the data category. The experimental results show that the detection accuracy of the OCGVAE model reaches 87.26%, which is 16.1%,70.2%,31.7%,36.5%,and 27.6% higher than that of One-Class Adversarial Net (OCAN), One-Class Gaussian Process (OCGP), One-Class Nearest Neighbor (OCNN), One-Class Support Vector Machine (OCSVM) and Semi-supervised GCN (Semi-GCN) algorithm, demonstrating that the proposed model effectively improves the accuracy of medical insurance fraud screening.
