In order to meet the emerging requirements for high computational complexity, low delay and energy consumption of the 5th generation wireless systems (5G) network, ultra-dense networks (UDNs) combined with multi-access edge computing ( MEC) can further improve network capacity and computing capability. In addition, the integration of green energy can effectively reduce the on-grid energy consumption of system and realize green computation. This paper studies the joint optimization of user association (UA) and resource allocation (RA) in MEC enabled UDNs under the green energy supply pattern, users need to perceive the green energy status of base stations (BSs) and choose the one with abundant resources to associate. To minimize the computation cost for all users, the optimization problem is formulated as a mixed integer nonlinear programming (MINLP) which is NP-hard. In order to solve the problem, a deep reinforcement learning ( DRL)-based association and optimized allocation (DAOA) scheme is designed to solve it in two stages. The simulation results show that the proposed scheme has good performance in terms of  computationcost and time out ratio, as well achieve load balancing potentially.

In order to study the role of the new technological concept of shared experiences in the digital interactive experience of cultural heritage and apply it to the digital interactive experience of cultural heritage to solve the current problems in this field, starting from the mixed reality (MR) technology that the shared experiences rely on, proper software and hardware platforms were investigated and selected, a universal shared experiences solution was designed, and an experimental project based on the proposed solution was made to verify its feasibility. In the end, a proven and workable shared experiences solution was obtained. This solution included a proposed MR spatial alignment method, and it integrated the existing MR content production process and standard network synchronization functions. Furthermore, it is concluded that the introduction and reasonable use of new technologies can help the development of the digital interactive experience of cultural heritage. The shared experiences solution for the digital interactive experience of cultural heritage balances investment issues in the exhibition, display effect, and user experience. It can speed up the promotion of cultural heritage and bring the vitality of MR technology to relevant projects.

At present, there is an urgent need for blockchain interoperability technology to realize interconnection between various blockchains, data communication and value transfer between blockchains, so as to break the ‘ value silo’ phenomenon of each blockchain. Firstly, it lists what people understand about the concept of interoperability. Secondly, it gives the key technical issues of cross-chain, including cross-chain mechanism, interoperability, eventual consistency, and universality. Then, the implementation of each cross-chain key technology is analyzed, including Hash-locking, two-way peg, notary schemes, relay chain scheme, cross-chain protocol, and global identity system. Immediately after that, five typical cross-chain systems are introduced and comparative analysis is made. In addition, two examples of cross-chain programmability and their analysis are given. Finally, the current state of cross-chain technology is summarized from two aspects: key technology implementation and cross-chain application enforcement. The cross-chain technology as a whole has formed a centralized fixed mechanism, as well as a trend of modular design, and some of the solutions to mature applications were established in the relevant standards organizations, and the cross-chain technology architecture tends to be unified, which is expected to accelerate the evolution of the open cross-chain network that supports the real needs of the interconnection of all chains.

Aiming at the shortcomings of current gesture tracking methods in accuracy and speed, based on deep learning You Only Look Once version 4 (YOLOv4) model, a new YOLOv4 model combined with Kalman filter rea-time hand tracking method was proposed. The new algorithm can address some problems existing in hand tracking technology such as detection speed, accuracy and stability. The convolutional neural network (CNN) model YOLOv4 is used to detect the target of current frame tracking and Kalman filter is applied to predict the next position and bounding box size of the target according to its current position. The detected target is tracked by comparing the estimated result with the detected target in the next frame and, finally, the real-time hand movement track is displayed. The experimental results validate the proposed algorithm with the overall success rate of 99.43%

at speed of 41.822 frame/ s, achieving superior results than other algorithms. 

In the long history of more than 1 500 years, Dunhuang murals suffered from various deteriorations causing irreversible damage such as falling off, fading, and so on. However, the existing Dunhuang mural restoration methods are time-consuming and not feasible to facilitate cultural issemination and permanent inheritance. Inspired by cultural computing using artificial intelligence, gated-convolution-based dehaze net (GD-Net) was proposed for Dunhuang mural refurbishment and comprehensive protection. First, a neural network with gated convolution was applied to restore the falling off areas of the mural to ensure the integrity of the mural content. Second, a dehaze network was applied to enhance image quality to cope with the fading of the mural. Besides, a Dunhuang mural dataset was presented to meet the needs of deep learning approach, containing 1 180 images from the Cave 290 and Cave 112 of the Mogao Grottoes. The  experimental results demonstrate the effectiveness and superiority of GD-Net.

When the power of the mainlobe interference received by the receiver is at the same level as the power of the sidelobe interference power, the traditional eigen-projection interference suppression method has the problems of severe beam deformation and peak shift. Aiming at these problems, a beam pattern optimization method (BPOM) was proposed, which can suppress the interference well even when the mainlobe interference power is approximately equal to the sidelobe interference power. In the method, the mainlobe interference eigenvectors are firstly determined according to the correlation criterion. Then through the eigenvalue comparison, the sidelobe interference eigenvectors whose eigenvalues are approximately equal to the mainlobe interference eigenvalues are judged. After that, a projection matrix is constructed to filter out the mainlobe and sidelobe interference. Finally, the covariance matrix is reconstructed and the weight vector for beamforming is obtained. Simulation shows that BPOM has a better output performance than the existing algorithms in case that the power of the mainlobe interference is close to that of the sidelobe interference.

In the context of interdisciplinary research, using computer technology to further mine keywords in cultural texts and carry out semantic analysis can deepen the understanding of texts, and provide quantitative support and evidence for humanistic studies. Based on the novel A Dream of Red Mansions, the automatic extraction and classification of those sentiment terms in it were realized, and detailed analysis of large-scale sentiment terms was carried out. Bidirectional encoder representation from transformers (BERT) pretraining and fine-tuning model was used to construct the sentiment classifier of A Dream of Red Mansions. Sentiment terms of A Dream of Red Mansions are divided into eight sentimental categories, and the relevant people in sentences are extracted according to specific rules. It also tries to visually display the sentimental interactions between Twelve Girls of Jinling and Jia Baoyu along with the development of the episode. The overall F1 score of BERT-based sentiment classifier reached 84-89%. The best single sentiment score reached 91-15%. Experimental results show that the classifier can satisfactorily classify the text of A Dream of Red  Mansions, and the text classification and interactional analysis results can be mutually verified with the text interpretation of A dream of Red Mansions by literature experts.