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With the development of Industry 4.0 and big data technology, the Industrial Internet of Things (IIoT) is hampered by inherent issues such as privacy, security, and fault tolerance, which pose certain challenges to the rapid development of IIoT. Blockchain technology has immutability, decentralization, and autonomy, which can greatly improve the inherent defects of the IIoT. In the traditional blockchain, data is stored in a Merkle tree. As data continues to grow, the scale of proofs used to validate it grows, threatening the efficiency, security, and reliability of blockchain-based IIoT. Accordingly, this paper first analyzes the inefficiency of the traditional blockchain structure in verifying the integrity and correctness of data. To solve this problem, a new Vector Commitment (VC) structure, Partition Vector Commitment (PVC), is proposed by improving the traditional VC structure. Secondly, this paper uses PVC instead of the Merkle tree to store big data generated by IIoT. PVC can improve the efficiency of traditional VC in the process of commitment and opening. Finally, this paper uses PVC to build a blockchain-based IIoT data security storage mechanism and carries out a comparative analysis of experiments. This mechanism can greatly reduce communication loss and maximize the rational use of storage space, which is of great significance for maintaining the security and stability of blockchain-based IIoT. [ABSTRACT FROM AUTHOR]

This paper describes a virtual system that emulates an ARM-based processor machine, created to replace a traditional hardware-based system for teaching assembly language. The virtual system proposed here integrates, in a single environment, all the development tools necessary to deliver introductory or advanced courses on modern assembly language programming. The virtual system runs a Linux operating system in either a graphical or console mode on a Windows or Linux host machine. Student feedback indicates that teaching with the virtual system has become progressively easier, clearer and more interesting while keeping staff support to a minimum. Since no software licenses or extra hardware are required to use the virtual system, students are able to carry their own ARM emulator with them on a USB memory stick. Institutions adopting this, or a similar, virtual system can also benefit by reducing the capital investment in hardware-based development kits, and by enabling distance-learning courses.

Explainable AI (XAI) improved by a deep neural network (DNN) of a residual neural network (ResNet) and long short-term memory networks (LSTMs), termed XAIRL, is proposed for segmenting foot infrared imaging datasets. First, an infrared sensor imaging dataset is acquired by a foot infrared sensor imaging device and preprocessed. The infrared sensor image features are then defined and extracted with XAIRL being applied to segment the dataset. This paper compares and discusses our results with XAIRL. Evaluation indices are applied to perform various measurements for foot infrared image segmentation including accuracy, precision, recall, F1 score, intersection over union (IoU), Dice similarity coefficient, mean intersection of union, boundary displacement error (BDE), Hausdorff distance, and receiver operating characteristic (ROC). Compared to results from the literature, XAIRL shows the highest overall performance, achieving accuracy of 0.93, precision of 0.91, recall of 0.95, and F1 score of 0.93. XAIRL also displays the highest IoU, Dice similarity coefficient, and ROC curve and the lowest BDE and Hausdorff distance. Although U-Net performs well for most metrics, Mask R-CNN shows slightly worse performance but still outperforms the random forest and support vector machine algorithms. By building a high-quality foot infrared imaging dataset, machine learning-based algorithms can accurately analyze foot temperature and pressure distribution. These models can then be used to customize shoes for individual wearers, improving their comfort and reducing the risk of foot injuries, particularly for those with high blood pressure. [ABSTRACT FROM AUTHOR]

Log anomaly detection is an important paradigm for system troubleshooting. Existing log anomaly detection based on Long Short-Term Memory (LSTM) networks is time-consuming to handle long sequences. Transformer model is introduced to promote efficiency. However, most existing Transformer-based log anomaly detection methods convert unstructured log messages into structured templates by log parsing, which introduces parsing errors. They only extract simple semantic feature, which ignores other features, and are generally supervised, relying on the amount of labeled data. To overcome the limitations of existing methods, this paper proposes a novel unsupervised log anomaly detection method based on multi-feature (UMFLog). UMFLog includes two sub-models to consider two kinds of features: semantic feature and statistical feature, respectively. UMFLog applies the log original content with detailed parameters instead of templates or template IDs to avoid log parsing errors. In the first sub-model, UMFLog uses Bidirectional Encoder Representations from Transformers (BERT) instead of random initialization to extract effective semantic feature, and an unsupervised hypersphere-based Transformer model to learn compact log sequence representations and obtain anomaly candidates. In the second sub-model, UMFLog exploits a statistical feature-based Variational Autoencoder (VAE) about word occurrence times to identify the final anomaly from anomaly candidates. Extensive experiments and evaluations are conducted on three real public log datasets. The results show that UMFLog significantly improves F1- scores compared to the state-of-the-art (SOTA) methods because of the multifeature. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the development of blockchain technology has also been deeply impacted. When performing block verification in the blockchain network, if all transactions are verified on the chain, this will cause the accumulation of data on the chain, resulting in data storage problems. At the same time, the security of data is also challenged, which will put enormous pressure on the block, resulting in extremely low communication efficiency of the block. The traditional blockchain system uses the Merkle Tree method to store data. While verifying the integrity and correctness of the data, the amount of proof is large, and it is impossible to verify the data in batches. A large amount of data proof will greatly impact the verification efficiency, which will cause end-to-end communication delays and seriously affect the blockchain system’s stability, efficiency, and security. In order to solve this problem, this paper proposes to replace the Merkle tree with polynomial commitments, which take advantage of the properties of polynomials to reduce the proof size and communication consumption. By realizing the ingenious use of aggregated proof and smart contracts, the verification efficiency of blocks is improved, and the pressure of node communication is reduced. [ABSTRACT FROM AUTHOR]