In view of the large amount of new data in the Industrial Internet Of Things（IIOT） and the imbalance of data at the factory sub-ends， a data sharing method of IIOT based on Federal Incremental Learning （FIL-IIOT） was proposed. Firstly， the industry federation model was distributed to the factory sub-end as the local initial model. Then， the federal sub-end optimization algorithm was proposed to dynamically adjust the participating subset. Finally， the incremental weight of the factory sub-end was calculated through the federal incremental learning algorithm， thereby integrating the new state data with the original industry federation model quickly. Experimental results the Case Western Reserve University （CWRU） bearing failure dataset show that the proposed FIL-IIOT makes the accuracy of bearing fault diagnosis reached 93.15%， which is 6.18 percentage points and 2.59 percentage points higher than those of Federated Averaging （FedAvg） algorithm and FIL-IIOT of Non Increment （FIL-IIOT-NI） method， respectively. The proposed method meets the needs of continuous optimization of industry federation model based on industrial incremental data.

With the rapid development of online education platforms represented by Massive Open Online Courses （MOOC）， how to evaluate the large-scale subjective question assignments submitted by platform learners is a big challenge. Peer grading is the mainstream scheme for the challenge， which has been widely concerned by both academia and industry in recent years. Therefore， peer grading technologies for online education were survyed and analyzed. Firstly， the general process of peer grading was summarized. Secondly， the main research results of important peer grading activities， such as grader allocation， comment analysis， abnormal peer grading information detection and processing， true grade estimation of subjective question assignments， were explained. Thirdly， the peer grading functions of representative online education platforms and published teaching systems were compared. Finally， the future development trends of peer grading was summed up and prospected， thereby providing reference for people who are engaged in or intend to engage in peer grading research.

It is difficult for the existing methods to get overall sentiment orientation of the comment text. To solve this problem, the method of multi-document sentiment summarization based on Latent Dirichlet Allocation (LDA) model was proposed. In this method, all the subjective sentences were extracted by sentiment analysis and described by LDA model, then a summary was generated based on the weight of sentences which combined the importance of words and the characteristics of sentences. The experimental results show that this method can effectively identify key sentiment sentences, and achieve good results in precision, recall and F-measure.

Concerning that conventional Particle Swarm Optimization (PSO) is easy trapped in local optima and with low search efficiency in later stage, an improved PSO based on mean information and elitist mutation, named MEPSO, was proposed. Average information of swarm was introduced into MEPSO to improve the global search ability, and Time-Varying Acceleration Coefficient (TVAC) strategy was adopted to balance the local search and global search ability. In the latter stage of the iteration, the Cauchy mutation operation was applied to the global best particle to improve the global search ability and to further reduce the risk of trapping into local optimum. Contrast experiments on six benchmark functions were given. Compared with Basic PSO (BPSO), PSO with TVAC (PSO-TVAC), PSO with Time-Varying Inertia Weight factor (PSO-TVIW) and Hybrid PSO with Wavelet Mutation (HPSOWM), MEPSO achieved better mean value and standard variance with shorter optimization time and better reliability. The results show that MEPSO can better balance the ability of local search and global search, and can converge faster with higher accuracy and efficiency.

Currently, most of proposed Radio Frequency Identification (RFID) authentication protocols cannot resist replay attack and altering attack. This article proposed a low-cost secure protocol, called Matrix-based Secure Protocol (MSP), which could resist these attacks. MSP utilized matrix-theory and Pseudo Random Number Generator (PRNG), and required only 1000 gate equivalents. Compared to previous proposed protocols using the same algorithm, MSP had less demand on the storage and the computing capability. Then, this article analyzed the security of MSP with Burrows-Abadi-Needham (BAN) logic. The conclusion is that MSP applies to RFID well.

To reduce the computation of Gauss mixture model effectively and improve the accuracy of shadow elimination in moving object detection, an algorithm which updated the model selectively and eliminated the shadow by the change of brightness was proposed. Firstly, the weight of the Gauss distribution and the rate of those that did not belong to the background were compared before updating the Gauss distribution, if the former was larger, then did not update it, otherwise, updated it; Secondly, the range of brightness change was chosen to be a threshold factor of shadow detection, so that the threshold could be adjusted adaptively according to the change of brightness. Finally, compared this algorithm with the traditional ones through experiments on indoor and outdoor videos, the experimental results show that the time consumption of the algorithm is about one-third of the traditional ones, the accuracy of shadow eliminating is improved and the efficiency of the algorithm is confirmed.

To overcome the deficiency in sharing and reusing design knowledge in manufacturing industry, a strategy based on functional ontology was proposed for design knowledge-sharing. The existing product structure can be mapped to its product functions by using functional ontology, and the design principles can be represented by functional decomposition tree. First, the basic framework of functional ontology was introduced. Then, the role of the functional decomposition tree to product design was analyzed. Finally, a knowledge-sharing platform for household appliances was designed and developed based on functional ontology. It was used to verify the proposed strategy. The experimental results show that the proposed strategy can effectively realize information retrieval, sharing and reuse of design knowledge in manufacturing industry. It can also shorten product development cycles.

According to the non-rigid characteristics of the moving target, an algorithm based on Mantle ratio, which can effectively separate multi-target using Support Vector Machine (SVM) and automatically select the largest characteristic region of non-rigid was proposed. It canceled the special requirements of selected regions beforehand.

An anonymous authentication protocol was proposed to improve the anonymity and to control anonymity abuse in wireless communications. The protocol implemented the anonymity of Home Location Register (HLR) using Visitor Location Registers (VLRs) which had been employed to forward the authentication message by Mobile Station (MS). Through checking the authentication token, this protocol could control anonymity abuse. The probability statistics were adopted to analyze the security and anonymity of this protocol. The analysis results show that this protocol not only improves the anonymity of HLR but also defends anonymity abuse effectively.