The current personalized recommendation platform has the characteristics of a wide range of data sources and many data types. With the data sparsity of the platform as an important reason for affecting the performance of the recommendation system, there are many challenges faced by the recommendation system:how to mine structured data and unstructured data of the platform to discover more features, improve the accuracy of recommendations in data-sparse scenarios, alleviate the cold start problem, and make recommendations interpretable. Therefore, for the personalized scenario of recommending Items for Users, the Heterogeneous Information Network (HIN) was used to build the association relationships between objects in the recommendation platform, and the Meta-Graph was used to describe the association paths between objects and calculate the User-Item similarity matrices under different paths; the FunkSVD matrix decomposition algorithm was adopted to calculate the implicit features of Users and Items, and for the unstructured data with text as an example, the Convolutional Neural Network (CNN) technology was used to mine the text features of the data; after splicing the features obtained by the two methods, a Factorization Machine (FM) incorporating historical average scores of Users and Items was used to predict Users' scores for Items. In the experiment, based on the public dataset Yelp, the proposed hybrid recommendation model, the single recommendation model based on Meta-Graph, the FM Recommendation model (FMR) and the FunkSVD based recommendation model were established and trained. Experimental results show that the proposed hybrid recommendation model has good validity and interpretability, and compared with the comparison models, the recommendation accuracy of this model has been greatly improved.

It is of great significance to apply the blockchain technology to the field of digital music copyright management in view of the difficulties in copyright confirmation, infringement monitoring, right protection and evidence collection, royalty settlement and other problems in the traditional music copyright industry. A digital music copyright management system was designed and constructed by using the VNT Chain blockchain platform. In the system, the blockchain technology was used to provide the proof of music copyright and realize the evidence solidification, the Shazam algorithm was used to provide the proof of originality for music copyright, and the smart contract was used to guarantee the security and reliability of transactions. This system included six function modules:user management, copyright registration, copyright trading, infringement monitoring, evidence solidification and music ecology, covering the main parts of copyright management. According to different needs of business data, blockchain, InterPlanetary File System(IPFS) and MySQL were adopted as storage engines respectively for the system. Experimental results show that the copyright registration time of each music increases by about 1.9 s, and the storage of music feature fingerprint data of one song on IPFS costs about 8 MB averagely, which meets the expected system performance requirements.

In this paper, an novel virtual EEPROM based on flash device was designed, which focused on the current urgent need for low-cost in embedded systems especially, The virtual EEPROM was used as NAND device part in embedded Operating System (OS), which used data backup mechanism to ensure data security. Considering that NAND only supports page programming, a method of log update based on memory buffer was employed to realize byte programming in Virtual EEPROM. Finally, to achieve unified management of NAND block, a wear leveling was designed, meanwhile, issues of bad block and Program/Erase cycle were greatly optimized. The result shows that the Virtual EEPROM has fast programming characteristics of NAND, as well as a great advantage of endurance over the EEPROM. The design has good compatibility and can be widely transplanted in various embedded platforms.

To solve the problems of the ringing, blocking artifacts and the excessive noise amplification in local image enhancement, an object-based multilevel contrast stretching method was proposed. First, segment the image into its constitent objects by using morphological watersheds and regional merging; then, separately stretch the image contrast at inter-object level and intra-object level in different ways. At inter-object level, an approach of stretching between adjacent extrema was adopted to adequately enlarge the local dynamic range of gray levels between objects; at intra-object level, the linear stretching approach was adopted to enhance the textural feature of the object and keep its appearance. Experimental results show, besides enhancing the image structure, the proposed method can effectively avoid ringing, blocking artifacts, restrict excessive noise amplification in smooth areas and preserve the overall brightness of the image, thus can provide the enhanced image with natural appearance.