A Contrastive Hypergraph Transformer for session-based recommendation (CHT) model was proposed to address the problems of noise interference and sample sparsity in the session-based recommendation itself. Firstly, the session sequence was modeled as a hypergraph. Secondly, the global context information and local context information of items were constructed by the hypergraph transformer. Finally, the Item-Level (I-L) encoder and Session-Level (S-L) encoder were used on global relationship learning to capture different levels of item embeddings, the information fusion module was used to fuse item embedding and reverse position embedding, and the global session representation was obtained by the soft attention module while the local session representation was generated with the help of the weight line graph convolutional network on local relationship learning. In addition, a contrastive learning paradigm was introduced to maximize the mutual information between the global and local session representations to improve the recommendation performance. Experimental results on several real datasets show that the recommendation performance of CHT model is better than that of the current mainstream models. Compared with the suboptimal model S2-DHCN (Self-Supervised Hypergraph Convolutional Networks), the proposed model has the P@20 of 35.61% and MRR@20 of 17.11% on Tmall dataset, which are improved by 13.34% and 13.69% respectively; the P@20 reached 54.07% and MRR@20 reached 18.59% on Diginetica dataset, which are improved by 0.76% and 0.43% respectively; verifying the effectiveness of the proposed model.
The use of Unmanned Aerial Vehicle (UAV) to continuously monitor designated areas can play a role in deterring invasion and damage as well as discovering abnormalities in time, but the fixed monitoring rules are easy to be discovered by the invaders. Therefore, it is necessary to design a random algorithm for UAV flight path. In view of the above problem, a UAV persistent monitoring path planning algorithm based on Value Function Iteration (VFI) was proposed. Firstly, the state of the monitoring target point was selected reasonably, and the remaining time of each monitoring node was analyzed. Secondly, the value function of the corresponding state of this monitoring target point was constructed by combining the reward/penalty benefit and the path security constraint. In the process of the VFI algorithm, the next node was selected randomly based on ε principle and roulette selection. Finally, with the goal that the growth of the value function of all states tends to be saturated, the UAV persistent monitoring path was solved. Simulation results show that the proposed algorithm has the obtained information entropy of 0.905 0, and the VFI running time of 0.363 7 s. Compared with the traditional Ant Colony Optimization (ACO), the proposed algorithm has the information entropy increased by 216%, and the running time decreased by 59%,both randomness and rapidity have been improved. It is verified that random UAV flight path is of great significance to improve the efficiency of persistent monitoring.
The accurate flight delay prediction results can provide a great reference value for the prevention of large-scale flight delays. The flight delays prediction is a time-series prediction in a specific space, however most of the existing prediction methods are the combination of two or more algorithms, and there is a problem of fusion between algorithms. In order to solve the problem above, a Convolutional Long Short-Term Memory (Conv-LSTM) network flight delay prediction model was proposed that considers the temporal and spatial sequences comprehensively. In this model, on the basis that the temporal features were extracted by Long Short-Term Memory (LSTM) network, the input of the network and the weight matrix were convolved to extract spatial features, thereby making full use of the temporal and spatial information contained in the dataset. Experimental results show that the accuracy of the Conv-LSTM model is improved by 0.65 percentage points compared with LSTM, and it is 2.36 percentage points higher than that of the Convolutional Neural Network (CNN) model that only considers spatial information. It can be seen that with considering the temporal and spatial characteristics at the same time, more accurate prediction results can be obtained in the flight delay problem. In addition, based on the proposed model, a flight delay analysis system based on Browser/Server (B/S) architecture was designed and implemented, which can be applied to the air traffic administration flow control center.
The purpose of disentangled representation learning is to model the key factors that affect the form of data, so that the change of a key factor only causes the change of data on a certain feature, while the other features are not affected. It is conducive to face the challenge of machine learning in model interpretability, object generation and operation, zero-shot learning and other issues. Therefore, disentangled representation learning always be a research hotspot in the field of machine learning. Starting from the history and motives of disentangled representation learning, the research status and applications of disentangled representation learning were summarized, the invariance, reusability and other characteristics of disentangled representation learning were analyzed, and the research on the factors of variation via generative entangling, the research on the factors of variation with manifold interaction, and the research on the factors of variation using adversarial training were introduced, as well as the latest research trends such as a Variational Auto-Encoder (VAE) named β-VAE were introduced. At the same time, the typical applications of disentangled representation learning were shown, and the future research directions were prospected.
Attribute reduction (feature selection) is an important part of data preprocessing. Most of attribute reduction methods use attribute dependence as the criterion for filtering attribute subsets. A Fast Dependence Calculation (FDC) method was designed to calculate the dependence by directly searching for the objects based on relative positive domains. It is not necessary to find the relative positive domain in advance, so that the method has a significant performance improvement in speed compared with the traditional methods. In addition, the Whale Optimization Algorithm (WOA) was improved to make the calculation method effective for rough set attribute reduction. Combining the above two methods, a distributed rough set attribute reduction algorithm based on Spark named SP-WOFRST was proposed, which was compared with a Spark-based rough set attribute reduction algorithm named SP-RST on two synthetical large data sets. Experimental results show that the proposed SP-WOFRST algorithm is superior to SP-RST in accuracy and speed.
The k-step reachability query is used to answer whether there exists a path between two nodes with length no longer than k in a Directed Acyclic Graph (DAG). Concerning the problems of large index size and low query processing efficiency of existing approaches, a bi-directional shortest path index based on partial nodes was proposed to improve the coverage of reachable queries, and a set of optimization rules was proposed to reduce the index size. Then, a bi-directional reversed topological index was proposed to accelerate the unreachable queries answering based on the simplified graph. Finally, the farthest-node-first-visiting bi-traversal strategy was proposed to improve the efficiency of query processing. Experimental results on 21 real datasets, such as citation networks and social networks, show that compared with existing efficient approaches including PLL (Pruned Landmark Labeling) and BFSI-B (Breadth First Search Index-Bilateral), the proposed algorithm has smaller index size and higher query response speed.
According to partially known probability distribution of demand information on the production-distribution network of perishable products, WCVaR (Worst-Case Conditional Value-at-Risk) was introduced to measure the risk. On the basis of considering the effect of factors, such as production, logistics distribution, transportation path etc, on production cost, transportation cost, storage cost and loss of stockout, an optimization model with minimum WCVaR at certain service level was proposed. And then the best optimization strategy was realized by minimizing tail risk loss of production-distribution network. The numerical simulation results show that the WCVaR method can handle the uncertainty with more volatility and has more excellent stability, compared with the robust optimization method. When the demand obeys mixed distribution, the optimization problem of production-distribution network with uncertainty can be well solved with WCVaR optimization model.
Now the integer Discrete Cosine Transform (DCT) algorithm of H.264 can not apply to Distributed Video Coding (DVC) framework directly because of its high complexity. In view of this, the authors presented a integer DCT algorithm and transform radix generating method based on fixed long step quantization which length was 2x (x was a plus integer). The transform radix in H.264 could be stretched. The authors took full advantage of this feature to find transform radix which best suits for working principle of hardware, and it moved the contracted-quantized stage from coder to decoder to reduced complexity of coder under the premise of "small" transform radix. In the process of "moving", this algorithm guaranteed image quality by saturated amplification for DCT coefficient, guaranteed reliability by overflow upper limit, and improved compression performance by reducing radix error. The experimental results show that, compared with corresponding module in H.264, the quantization method of this algorithm is convenient for bit-plane extraction. And it reduces calculating work of contracted-quantized stage of coder to 16 times of integer constant addition under the premise of quasi-lossless compression, raises the ratio of image quality and compression by 0.239. This algorithm conforms to DVC framework.