ABSTRACT Recently, significant progress has been made in completing static knowledge graphs. However, knowledge tends to evolve with time, and static knowledge graph completion (KGC) methods struggle to capture the changes. Therefore, temporal knowledge graph (TKG) reasoning has become a focus of research. Most existing TKG methods incorporate temporal information into triplets and transform them into KGC tasks, ignoring the important influence of time information and implicit relationships between entities. In this paper, we propose a new method called TD‐RKG, which addresses the challenges of temporal variability and implicit entity correlations based on a dynamic fusion representation learning approach. The method consists of four modules: dynamic local recurrent encoding layer, dynamic implicit encoding layer, dynamic global information attention layer and decoding layer. Experimental results on three benchmark datasets demonstrate substantial improvements in TD‐RKG across multiple evaluation metrics.

In recent years, sequential recommendation has received widespread attention for its role in enhancing user experience and driving personalized content recommendations. However, it also encounters challenges, including the limitations of modeling information and the variability of user preferences. A novel time-aware Long-Short Term Transformer (TLSTSRec) for sequential recommendation is introduced in this paper to address these challenges. TLSTSRec has two major innovative features. (1) Accurate modeling of users is achieved by fully leveraging temporal information. Time information is modeled by creating a trainable timestamp matrix from both the perspectives of time duration and time spectrum. (2) A novel time-aware Transformer model is proposed. To address the inherent variability of user preferences over time, the model combines long-term and short-term temporal information and adjusts the personalized trade-offs between long-term and short-term sequences using adaptive fusion layers. Subsequently, newly designed encoders and decoders are employed to model timestamps and interaction items. Finally, extensive experiments substantiate the effectiveness of TLSTSRec relative to various state-of-the-art sequential recommendation models based on MC/RNN/GNN/SA across a spectrum of widely used metrics. Furthermore, experiments are conducted to validate the rationality of the TLSTSRec structure.

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

Credit fraud is a common financial crime that causes significant economic losses to financial institutions. To address this issue, researchers have proposed various fraud detection methods. Recently, research on deep forests has opened up a new path for exploring deep models beyond neural networks. It combines the features of neural networks and ensemble learning, and has achieved good results in various fields. This paper mainly studies the application of deep forests to the field of fraud detection and proposes a distributed dense rotation deep forest algorithm (DRDF-spark) based on the improved RotBoost. The model has three main characteristics: firstly, it solves the problem of multi-granularity scanning due to the lack of spatial correlation in the data by introducing RotBoost. Secondly, Spark is used for parallel construction to improve the processing speed and efficiency of data. Thirdly, a pre-aggregation mechanism is added to the distributed algorithm to locally aggregate the statistical results of sub-forests in the same node in advance to improve communication efficiency. The experiments show that DRDF-spark performs better than deep forests and some mainstream ensemble learning algorithms on the fraud dataset in this paper, and the training speed is up to 3.53 times faster. Furthermore, if the number of nodes is further increased, the speedup ratio will continue to increase.

In order to solve the multi-controller placement problem of Software Defined Networking (SDN), this paper proposes a collaborative approach based on a competitive game. Under the premise of constraining the local network load, the gain setting of a single competitive game collaborative selects the participants of the next round of the game, and after applying the collaborative approach several times, the total networks latency is effectively reduced and load balancing is obtained. The simulation results in Internet2 OS3E topology and Cernet topology show that the proposed approach is more stable in terms of networks latency index and effectively ensures the balanced distribution of network load compared with the multi-controller placement schemes derived from K-means clustering, K-means++ clustering, and K-medoids clustering.

Abstract With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image analysis for images with noise, among which the image of low-light is the top priority of our research. In this paper, a low-light image enhancement method based on the enhanced network module optimized Generative Adversarial Networks(GAN) is proposed. The proposed method first applied the enhancement network to input the image into the generator to generate a similar image in the new space, Then constructed a loss function and minimized it to train the discriminator, which is used to compare the image generated by the generator with the real image. We implemented the proposed method on two image datasets (DPED, LOL), and compared it with both the traditional image enhancement method and the deep learning approach. Experiments showed that our proposed network enhanced images have higher PNSR and SSIM, the overall perception of relatively good quality, demonstrating the effectiveness of the method in the aspect of low illumination image enhancement.

A novel Distributed Hash Table BChord is presented in this paper. The standard Chord routes only in clockwise direction. Through adding extra anti-clockwise fingers in Finger Table, BChord adopts bi-directional routing mechanism based on Chord. Though fingers in BChord almost double to Chord, results from theoretic analysis show that BChord evidently minimizes the Average Path Length and increases efficiency of broadcast message compared with Chord.

Fuzzy logic offers the ability to handle uncertainty and imprecision effectively, and is therefore ideally suited to reasoning about trust in P2P Environment. A new P2P fuzzy trust model is presented in this paper. Because Trust-based P2P Computing network is very similar to the interpersonal relationship network, this Fuzzy Trust Model includes two phases: Recommendation Trust Phase and Direct Trust Phase. Recommendation Trust Phase mainly focuses on Extraction of Trust Link and Calculation of Recommendation Trust Degree based on Fuzzy Decision-making method. Fuzzy trust evaluation result will be obtained according to various Factor Sets and Evaluation Sets based on Fuzzy Decision-making method. Direct Trust Phase mainly focuses on updating of Direct Trust Degree.