Vol. 43 · No. 3

The number of cyber incidents in which the Internet of Things (IoT) device or system is present is increasing every day, requiring the opening of forensic investigations that can shed light on what has occurred. In order to be able to provide investigators with proper solutions for performing complete and efficient examinations in this new environment, IoT systems and devices are being studied from a forensic perspective so that tools and procedures can be designed accordingly. In this article, besides reviewing the proposals from the community on this matter, the multi-purpose IoT operating system Ubuntu Core is studied to determine in what way a forensic investigation of this system should be performed, detailing how to approach the acquisition and analysis phases. In addition, both the volatile and non-volatile artifacts that might hold useful information are listed and described, and a forensic tool is presented for their recovery as well as for the acquisition of the non-volatile memory.

In this paper, a PCA-SSA-DBIGRU-Attention multi-factor short-term power load forecasting model is proposed. Taking a complete account of the influence of meteorological factors, principal components analysis (PCA) is used to analyze the meteorological factors of daily minimum, maximum, daily average temperature, relative humidity, daily precipitation and power load data at the same time. The realization of original load data is dimensioned down. The complexity of power load forecasting models is reduced. Then, the Attention Double Bidirectional Gating Recurrent Unit (DBIGRU) model is constructed to calculate the different weights of the hidden layer states of the two-layer BIGRU. The hidden layer states are assigned different weights. The Sparrow Search Algorithm (SSA) is incorporated into the DBIGRU-Attention. The SSA-DBIGRU-Attention network model is constructed to optimize the learning rate, the number of iterations and the four hyperparameters of the first and second hidden layer neurons. The extracted principal components are input into SSA-DBIGRU-Attention to realize multi-factor short-term power load forecasting. Experimental results show that the prediction accuracy of the proposed model is improved, and the prediction time is reduced. Compared to the VMD-BILSTM, PCA-DBILSTM, CNN-GRU-Attention and CNN-BIGRU-Attention model, the four aspects of MAPE, MAE, RMSE and time are reduced by 29.55

In today's data-rich world, one of the most significant challenges is efficiently identifying near-duplicate data, especially when integrating data from various sources. Identifying near-duplicate documents applies to any content and has been widely used to enhance the efficiency of search engines, identify plagiarism or spam, and so on. Even smaller or specialized search engines can benefit from knowledge about near-duplicate documents. Shingling and MinHash are two state-of-the-art approaches to detecting near-duplicate documents. However, there are not many attempts to improve the performance of this locality-sensitive hashing technique. In this research paper, we propose a parallel implementation of the MinHash algorithm for near-duplicate document detection utilizing the immense parallelism offered by general-purpose GPUs. Experimental results show that the GPU-based parallel solution is far more cost-effective than the CPU-based sequential and parallel solutions.

Corporate fraud risk detection is a branch of fraud. It may exist in various industries and cause economic problems. Effective identification of corporate fraud can protect the safety of funds for investors in some sense. This paper proposes a classifier model of a fractional-order immune BP neural network based on the self-attention mechanism to improve efficiency. The improved artificial immune algorithm with dynamic region contraction strategy is used to optimize the initialization process of the BP neural network. Furthermore, it combines the self-attention mechanism to design the input layer. Finally, Caputo fractional non-causal calculus is used to optimize the parameter updating process in BP neural network. The experiment results indicate that our model has fast convergence rate and powerful capacity of detection, and performs efficiently in detecting fraud behaviors.

The present smart city concept drives academics and urban planners to give residents modern, secure, and sustainable infrastructure as well as a fair level of living. CCTV footage has been employed to improve public safety and wellbeing to fill this demand recognising unusual occurrences. Despite scientific advances, it is challenging and time-consuming to monitor video systems inside of buildings. While cloud computing is unable to support a variety of dispersed IoT setups like wireless sensor networks, local services fall far short of meeting storage requirements. New strategies based on AI and deep learning are gradually replacing conventional computer vision techniques. Modern hardware and software are installed in large data centres, enabling them to process enormous amounts of data. To give cloudlets, fog computing, and multi-access edge computing with intelligent privacy protection for video data while it is being kept, we combine three separate edge computing techniques into a hierarchical edge computing architecture. Then, we offer a straightforward but secure solution. In this study, we look at the creation of a CCTV encryption method that combines RBFN with the Triple Data Encryption Standard (DES) to identify anomalies in intelligent video surveillance. The purpose of this study was to encourage the usage of an algorithm based on RBFN-TDES that provides authenticated encryption rather than the original Data Encryption Standard (DES) method. Because the goal of this investigation was to figure out how the original DES algorithm was cracked, it was unavoidable that hackers would succeed. The suggested technique the security requirements for different encryption properties are also examined and analysed by RBFN-TDES. Finally, the comparison findings between the proposed system and existing systems are presented, along with an examination of each's security characteristics and performance metrics. Our proposed RBFN-TDES model is efficient enough to be used for securing CCTV pictures in a distributed computing environment. To evaluate the effectiveness of our approach, we run large experiments on benchmarks constructed on top of the EPFL dataset. Comparing our strategy to state-of-the-art techniques tested over the datasets, we find a 97.21

With the continuous improvement of communication and computing technology, the technical threshold of the Metaverse (MVS) has been lowered, gradually expanding the scope of technology penetration, and attracting attention to the construction of characters in the MVS environment. In virtual reality (VR), character design and scene realizations are the foundation and guarantee of technology application. VR also provides an effective tool for the transformation of traditional media and the exploration of news communication innovation. On this basis, firstly, the introduction of VR is analyzed in the MVS environment. Secondly, based on the application status of VR, the modeling design and structure optimization of three-dimensional (3D) characters in the MVS environment are studied. Furthermore, by improving the traditional 3D modeling process, an adaptive hierarchical detail model is proposed to realize the scene modeling mechanism of the virtual environment combining image modeling and geometric modeling, so as to quickly complete the construction of the character image model of a news anchor in the scene. Finally, the model of the experimental design is simulated and constructed to test the effect of the model to construct the image of news anchors. The results reveal that the frame rate of news anchor images designed by the model can be kept at about 40 frames per second. The actual value of modeling details is always greater than the ideal value of modeling design. By comparing the actual modeling time of the control group and the experimental group, it can be found that the modeling time of the experimental group is shorter. Therefore, it can be concluded that the VR-based hierarchical detail model in the MVS environment designed in this research can better complete the image construction of news anchors. The research results can provide a reference and theoretical basis for the subsequent modeling work in the MVS environment and the construction of VR characters.

With the development of deep learning, deep neural methods have been introduced to boost the performance of Collaborative Filtering (CF) models. However, most of the models rely solely on the user-item heterogeneous graph and only implicitly capture homogenous information, which limits their performance improvement. Although some state-of-the-art methods try to utilize additional graphs to make up, they either merely aggregate the information of multiple graphs in the step of initial embedding or only merge different multi-graph information in the step of final embedding. Such one-time multi-graph integration leads to the loss of interactive and topological information in the intermediate process of propagation. This paper proposes a novel Multi-Graph iterative fusion Recommendation Framework (MGRF) for CF recommendation. The core components are dual information crossing interaction and multi-graph fusing propagation. The former enables repeated feature crossing between heterogeneous nodes throughout the whole embedding process. The latter repeatedly integrates homogeneous nodes as well as their topological relationships based on the constructed user-user and item-item graphs. Thus, MGRF can improve the embedding quality by iteratively fusing user-item heterogeneous graph, user-user and item-item homogeneous graphs. Extensive experiments on three public benchmarks demonstrate the effectiveness of MGRF, which outperforms state-of-the-art baselines in terms of Recall and NDCG.

Higher order mutation testing is a type of white-box testing in which the source code is changed repeatedly using two or more mutation operators to generate mutated programs. The objective of this procedure is to improve the design and execution phases of testing by allowing testers to automatically evaluate their test cases. However, generating higher order mutants is challenging due to the large number of mutants needed and the complexity of the mutation search space. To address this challenge, the problem is modeled as a search problem. The purpose of this study is to propose a genetic algorithm-based search technique for mutation testing. The expected outcome is a reduction in the number of equivalent high order mutants produced, leading to a minimum number of mutant sets that produce an adequate mutation score. The experiments were carried out and the results were compared with a random search algorithm and four different versions of the proposed genetic algorithm which use different selection methods: roulette wheel, tournament, rank, and truncation selection. The results indicate that the number of equivalent mutants and the execution cost can be reduced using the proposed genetic algorithm with respect to the selection method.

A brain-computer interface (BCI) based on motor imagery (MI) enables users to communicate with the computer directly using brain signals. However, due to the low signal-to-noise ratio and significant inter-subject variations, its long calibration time hinders the development of brain-computer interfaces. Meta-learning, as the current popular framework in few-shot learning, enables the model to quickly adapt to new few-shot tasks by learning a series of similar tasks. In this paper, we propose a novel algorithm that combines pre-training and meta-learning. It can reduce the number of training samples required for the target subject and meanwhile ensure stable and reliable BCI performance. This is achieved by first learning general feature representations from a large number of other subjects' data through pre-training, and then further optimizing the model by task scale using meta-learning based on the pre-training stage. Applications to a dataset confirm the effectiveness of the combination of pre-training and meta-learning. Results indicate that the proposed algorithm outperforms the considered comparable baseline algorithm, early meta-learning frameworks without pre-training, and fine-tuning in transfer learning. In addition, experiments show that the selection of source subjects can further improve the overall performance of the algorithm, and the pre-training stage is crucial for the model to achieve good performance. This study has significant instruction for the application of BCI in the field of medical rehabilitation. It can improve the classification performance of the disabled with poor motor imagination characteristics by using the data of healthy typical subjects and greatly reducing their training time.

Radiomics is a technology that extracts a large number of quantitative features from high-throughput medical images and has become a focus of research. It can help in disease diagnosis, therapy planning, and prognosis evaluation through Big Data analysis algorithms. Radiomics technology can extract hundreds or even tens of thousands of quantifiable data features from medical images, which can no longer fit into the memory of one machine. Therefore, we propose a distributed correlation analysis algorithm (DFCA) based on a MapReduce distributed computing framework for breast ultrasound radiomics feature datasets. Each compute node will produce massive intermediate data while the DFCA calculates the Pearson correlation coefficient of radiomics features. With the increase of feature data and dimensions, the data transmission cost will be in a square growth. To reduce the cost, we propose a distributed correlation estimation algorithm (DFCEA) for radiomics features based on DFCA. The DFCEA algorithm estimates the Pearson correlation coefficient using an iterative method, which can further reduce the I/O cost. The experiment proved that our algorithms are more effective compared to the algorithms in the literature.