High-density NAND flash memory, such as quadruple-level cell (QLC) flash, has has been widely adopted in emerging storage systems. However, its limited endurance and performance challenges necessitate novel solutions. Voltage-based write-once memory (WOM-v) codes have demonstrated their effectiveness in extending flash memory lifespan by reducing the erase count of flash blocks. Concurrently, secure deletion is essential to ensure data privacy in flash-based storage systems. Existing secure deletion approaches—encryption-based, erasure-based, and scrubbing-based—often face limitations such as susceptibility to deciphering or significant performance overheads. Additionally, the inherent “big block problem” in high-density flash memory complicates garbage collection (GC), further degrading system performance. To address these challenges, this paper proposes WOM-FTL, a flash translation layer (FTL) that integrates secure deletion and garbage collection (GC) with WOM-v codes to enhance both security and performance. WOM-FTL classifies request data into four categories based on access frequency and privacy requirements: hot-secure (HS), cold-secure (CS), hot-unsecure (HU) and cold-unsecure (CU). Additionally, WOM-FTL divides each block into several equal-sized sub-blocks and further classifies them into top sub-blocks and bottom sub-blocks according to their data storage characteristics. When a secure data deletion command is issued to the storage device, WOM-FTL leverages unsecure data (HU and CU) to overwrite the secure data (HS and CS). Furthermore, WOM-FTL allocates different types of request data to the corresponding sub-blocks, creating a data allocation pattern that is both scrubbing-friendly and GC-friendly. Experimental results demonstrate that WOM-FTL improves the I/O performance of storage systems by 60.91% compared to state-of-the-art solutions, providing a significant advancement in secure and efficient management of high-density flash memory.

The advancement of deep clustering in recent years has been propelled by advances in deep learning technology, breaking through bottlenecks and being widely applied in multiple fields. However, current deep clustering algorithms focus only on single information or cannot fully balance the interrelationships between the original architecture and underlying institutions. Therefore, we propose a new method called structural embedding contrastive graph clustering (SECGC) that can effectively address the aforementioned issues. Firstly, We capture deeper representational information through higher-order neighborhood information models. Secondly, we designed an attribute structure fusion module that adaptively integrates the obtained high-order neighborhood information with the attribute information of the autoencoder. Simultaneously, the multi-scale feature fusion module fuses information from different dimensions to explor varying levels of semantic hierarchy. In addition, a dual self-supervised mechanism and high-confidence pseudo-label technique were designed to guide representation learning and clustering allocation. A new loss function has been designed to explore potential data structures and improve clustering performance by introducing contrastive loss. Extensive experiments on multiple benchmark datasets have shown that our method is comparable to state-of-the-art methods.

Nowadays some progress has been made in learning the representation of nodes in unlabeled multi-view for clustering. However, current multi-view graph clustering methods ignore important clustering information during training leading to limited performance. In this paper, we introduce a new framework for multi-view graph clustering, known as IGMC. It utilizes both node attributes and graph topology and uses network topology and the pseudo-labels obtained during the training process as supervisory signals to construct pairs of samples with high discriminative ability and high reliability so that each node corresponds to multiple positive samples. In addition, the clustering quality of the node representations is improved by maximizing the mutual information between the node and the corresponding clusters to emphasize the classes of the nodes. IGMC produces better results than advanced methods on several challenging datasets. The source code is available at https://github.com/tczgithub/IGMC.

As an important part of cyber–physical–social intelligence, artificial intelligence (AI)-driven smart healthcare is committed to promoting the application of human–machine hybrid augmented intelligence in the medical field, including AI-assisted medical image analysis and lesion recognition. Among them, deep learning models represented by fully convolutional networks (FCNs) have achieved excellent performance in medical image segmentation. However, limited by the complex structure of segmentation networks and the inherently redundant characteristics of convolutional operation, the scale of these models is extremely large. To further promote the application of machine intelligence in the field of medical image analysis, we propose an attention U-Net based on Bi-ConvLSTM (AUBC-Net) for accurate segmentation of medical images in this article. Different from classical U-Net, the proposed model deals with the potential association between decoding features and encoding features by bidirectional convolution LSTM. Furthermore, for the inherent redundancy characteristics of FCNs, we propose a lightweight feature generation strategy and optimize the calculation process of Bi-ConvLSTM based on tensor multilinear algebra, which can greatly reduce the number of network parameters. In addition, we have conducted the image segmentation experiments on two benchmark medical datasets, and the experimental results demonstrate that the proposed model can not only achieve better performance than existing methods, but also effectively compress network parameters while ensuring performance, which greatly facilitates AI-driven smart medical applications.

With the deepening of industry 4.0 paradigm in logistics systems, artificial intelligent has been widely used to improve the quality of logistics services. Considering that data collected in comprehensive logistics service system usually integrate multistage complex information such as traffic flow records and spatiotemporal trajectory, it is inevitable that the data are incomplete and partially missing due to equipment failures, communication interruptions, etc. As an effective spatiotemporal completion tool in logistics systems, low-rank tensor completion has aroused extensive research interest thanks to its excellent performance on data recovery. Although existing tensor completion methods effectively capture the complex associations/dependencies of multidimensional inputs, they fail to exploit the potential characteristics of spatiotemporal data, such as the periodicity. In this article, we propose a jointly low-rank tensor completion method for logistics data completion, which constructs multiple periodic subtensors by setting an appropriate time window, then performs jointly low-rank completion and imputation. In addition, we also provide an optimization algorithm based on Alternating Direction Method of Multiplier framework for the proposed problem. Experimental results on four logistics-related datasets have further demonstrated the promising performance of the proposed method compared with other state-of-the-art competitors. We believe that the proposed approach not only effectively maintains the advantages of classical completion methods, but also fully excavates the multidimensional correlation and hidden patterns behind records, and further provides a novel and effective strategy for data completion and imputation in logistics systems.

Data in modern industrial applications and data science present multidimensional progressively, the dimension and the structural complexity of these data are becoming extremely high, which renders existing data analysis methods and machine learning algorithms inadequate to the extent. In addition, high-dimensional data in actual scenarios often share some common latent components and patterns, it is necessary and significant to analyze such data in an associative manner, rather than treating them independently. Considering the problem of data islands and data privacy that is prevalent in the industry. In this article, we propose the first joint high-order orthogonal iterative (J-HOOI) algorithm for simultaneous tensor decomposition and federated tensor decomposition (FTD) model for feature extraction and dimension reduction of high-dimensional industrial data under the federated learning framework. Moreover, we also develop a secure federated computation process based on the J-HOOI method. Using this method, multiple participants iteratively calculate the local factor matrices and transfer the local information to the parameter server, which aggregates the local information to generate the globally updated factor matrices. Finally, each client generates globally compressed features by projecting local data onto these common potential spaces. We have demonstrated with real-world industrial datasets that our approach is similar to a centralized training model in decomposition accuracy and classification accuracy while respecting privacy.

Aiming at discovering hidden different data structures in big data from different perspectives, a tensor-based multiple clustering method has been developed recently, which can be widely used in Industrial Internet of Things (IoT) to improve production and service quality. However, due to the high computational cost and huge volume of data, outsourcing computing to relatively inexpensive cloud servers can greatly save local costs, but there is a high risk of revealing user privacy. To address the above problem, a privacy-preserving tensor-based multiple clustering method on the secure hybrid cloud is proposed. The proposed scheme utilizes a homomorphic cryptosystem to encrypt object tensors and, then, employs cloud servers to completely implement multiple clustering calculation over encrypted object tensors. Furthermore, a series of related security subprotocols are proposed to support privacy-preserving tensor-based multiple clusterings. In the proposed scheme, only encryption and removing perturbation are performed on the client, which is very lightweight for users. Experimental results show that the proposed scheme is accurate and efficient when clustering objects to different groups, while no private or additional information is leaked. Moreover, when employing more cloud nodes, the scheme has high scalability; thus, it is very suitable for clustering Industrial IoT big data.

To uncover latent data structures in big data, a high-order clustering algorithm by fast search and find of density peaks has emerged recently, and will bring great application values in industrial Internet-of-Things data management and analysis. With the popularity of cloud computing, it provides users with the convenience of outsourcing calculations while bringing the risk of privacy disclosure. Aiming at the problem above, from the characteristics of the secure cloud service system, this paper proposes a secure high-order clustering algorithm by fast search and find of density peaks on hybrid cloud. In the proposed scheme, the client first builds the encrypted object tensors with user data using homomorphic encryption, then uploads them to the cloud to completely implement the proposed protocols. In the end, clustering results perturbed with random numbers are returned to client for remove perturbation. The performance of the proposed method is evaluated on a smart grid dataset in terms of clustering accuracy, efficiency, and speedup ratio. Experimental results reveal that the proposed approach can accurately and effectively cluster big data without disclosing user privacy while ensuring that the client is very lightweight. Therefore, the proposed scheme with high security and scalability is suitable for clustering industrial Internet-of-Things big data.

Multiple clustering analysis has the clear advantages to discover latent data pattern in big data from different views, so it has tremendous practical values in automation industries. However, most of current algorithms are difficult to group heterogeneous data to multiple clusterings according to the requirements of different applications. This paper presents a flexible multiple clustering analytic and service framework, and a novel tensor-based multiple clusterings (TMC) approach. Heterogeneous data objects in cyber-physical-social systems are first represented as low-order tensors and a weight tensor construction approach is proposed to measure the importance of attributes combinations in heterogeneous feature spaces. Then, a selective weighted tensor distance is explored to cluster tensorized data objects for different applications. This paper, through a real-world smart bike maintenance system, illustrates TMC and evaluates its clustering performance. Experimental results reveal TMC can obtain higher quality clustering results but with lower redundancies to meet different requirements of applications in automation systems.