Consensus mechanism is the core of blockchain technology， and consensus algorithms are the specific technical means to achieve this mechanism. Consensus mechanism ensures consistency and correctness of blockchain database， and is crucial to system performance of the blockchain such as security， scalability and throughput. Therefore， firstly， from perspective of underlying storage of blockchain technology， consensus algorithms were divided into two categories： chain and graph， and working principles， optimization strategies and typical representative algorithms of different types of different categories of consensus algorithms were classified and reviewed. Then， in view of complex application background of blockchain， the mainstream improved algorithms of chain structure and graph structure consensus algorithms were sorted out respectively and comprehensively， and main line of consensus algorithm development was given， especially in terms of security， the algorithms were compared deeply， and advantages， disadvantages and possible security risks of them were pointed out. Finally， from multiple dimensions such as security， scalability， fairness and incentive strategy， challenges faced by the current blockchain consensus algorithms were discussed in depth， and their development trends were prospected， so as to provide theoretical reference for researchers.

Formal concept analysis is an important tool for knowledge representation and mining， and formal context is one of the basic concepts in formal concept analysis. A new attribute reduction — inner product reduction was proposed to solve the problem of whether the object set in the formal context has the same attribute in a given attribute set， and also to solve the problem of how to eliminate irrelevant attributes in the calculation. Firstly， the concept of inner product was given in formal context. Then， the reduction theory and method in relation system were used to define the inner product reduction， and the inner product reduction algorithm based on discernibility matrix was proposed to obtain all the reduction results in the formal context， and the reduction core was obtained through the intersection operation based on the results. In addition， when attributes increased， an incremental inner product reduction algorithm was designed. Finally， the application of inner product reduction was explored in infectious disease network. In the simulated case， 6 attributes were reduced to 2 attributes. Simulation outcomes demonstrate that the inner product reduction method is feasible， interpretable， and successful in achieving the knowledge reduction goal.

In Internet Protocol Television （IPTV） applications， a television terminal is usually shared by several family members. The exiting recommendation algorithms are difficult to analyze the different interests and preferences of family members from the historical data of terminal. In order to meet the video-on-demand requirements of multiple members under the same terminal， a capsule network-based IPTV video-on-demand recommendation model， namely CapIPTV， was proposed. Firstly， a user interest generation layer was designed on the basis of the capsule network routing mechanism， which took the historical behavior data of the terminal as the input， and the interest expressions of different family members were obtained through the clustering characteristic of the capsule network. Then， the attention mechanism was adopted to dynamically assign different attention weights to different interest expressions. Finally， the interest vector of different family members and the expression vector of video-on-demand were extracted， and the inner product of them was calculated to obtain the Top-N preference recommendation. Experimental results based on both the public dataset MovieLens and real radio and television dataset IPTV show that， the proposed CapIPTV outperforms the other 5 similar recommendation models in terms of Hit Rate （HR）， Recall and Normalized Discounted Cumulative Gain （NDCG）.

Concerning that the upper, lower bounds and stride of the non-normalized loop are uncertain, some issues were normalized based on a transform method such as that loop conditions were logical expression, increment-reduction statement and do-while. An unroll-jam method was proposed to deal with the loops that cannot be normalized, which mined the unroll-jam results by Superword Level Parellelism (SLP) vectorization. Compared with the existing Single Instruction Multiple Data (SIMD) vectorization method for non-normalized loops, the experimental results show that the transform method and unroll-jam method are better to explore the parallelism of the non-normalized loops, which can improve the performance by more than 6%.

A fuzzy multi-objective software reliability allocation model was established, and bacteria foraging optimization algorithm based on estimation of distribution was proposed to solve software reliability redundancy allocation problem. As the fuzzy target function, software reliability and cost were regarded as triangular fuzzy members, and bacterial foraging algorithm optimization based on Gauss distribution was applied. Different membership function parameters were set up, and different Pareto optimal solutions could be obtained. The experimental results show that the proposed swarm intelligence algorithm can solve multi-objective software reliability allocation effectively and correctly, Pareto optimal solution can help the decision between software reliability and cost.

Basic Input Output System (BIOS) trapdoor has huge impact on computer system, and it is difficult to detect the existence of BIOS trapdoor effectively with the existing tools. After researching BIOS structure and BIOS code obfuscation technique based on reverse analysis, BIOS trapdoors were divided into module-level BIOS trapdoor and instruction-level BIOS trapdoor according to implementation granularity, followed by analyzing the implementation principle and characteristics of these two BIOS trapdoors in detail. Finally the detection method of module-level trapdoor based on analyzing module structure and the detection method of instruction-level trapdoor based on integrity measurement were presented. The experimental results show that these two methods can detect the existence of their corresponding BIOS trapdoors effectively.

Depending on BLS signing algorithm, blind signing algorithm of BLS signatures, and aggregating algorithm of BLS signatures, a new efficient identity-based blind signature scheme was proposed. First, the round complexity of this scheme was optimal, i.e. it was enough for the user and the signer to respectively transmit only one message during each blind signing process. Second, its security was based on the so-called one-more Computational Diffie-Hellman Assumption (CDH) assumption, while the security of the other similar identity-based signature scheme was based on the stronger assumption-ROS assumption. Additionally, this scheme was computationally efficient and had very short signature length. Therefore, it was very suitable for the applications such as e-cash and e-voting.