Smart contract technology， as a milestone of blockchain 2.0， has received widespread attention from both academic and industry circles. It runs on an underlying infrastructure without trusted computing environment and has characteristics that distinguish it from traditional programs， and there are many vulnerabilities with huge influence in its own security， so that the research on security auditing for it has become a popular and urgent key scientific problem in the field of blockchain security. Aiming at the detection and automatic repair of smart contract vulnerabilities， firstly， main types and classifications of smart contract vulnerabilities were introduced. Secondly， three most important methods of smart contract vulnerability detection in the past five years were reviewed， and representative and innovative research techniques of each method were introduced. Thirdly， smart contract upgrade schemes and cutting-edge automatic repair technologies were introduced in detail. Finally， challenges and future work of smart contract vulnerability detection and automatic repair technologies for online， real-time， multi-platform， automatic， and intelligent requirements were analyzed and prospected as a framework of technical solutions.

Splicing is the most universal image tampering operation, detection of which is effective for identifying image tamper. A blind splicing detection method was proposed. The method firstly analyzed the effects of different sub-bands on image splicing detection according to features of wavelet transform. High frequency sub-band was verified to be more appropriate for splicing detection both from theory analysis and experiment results. Secondly, the method conducted difference operation, rounded and made threshold to the coefficients as discrete Markov states, and calculated the state transition probabilities as splicing features. Finally, Support Vector Machine (SVM) was used as classifier, and the features were tested on Columbia image splicing detection evaluation datasets. The experimental results show that the proposed method performs better compared with other features and achieves a detection accuracy rate of 94.6% on the color dataset specially.

To solve the false detection and detection delay of concept drift for data stream, a new online concept drift detection method based on the distance measurement of overlapped data windows was proposed in this paper. By dividing the data stream into overlapped data windows and computing the heterogeneous Euclidean distance of neighboring windows, and measuring the inconsistency of the data windows through the nearest neighbor principle, the authors could achieve the evaluation of distribution diversity and the detection of concept drift. To evaluate the effectiveness of the proposed method, experiments were made on some public data sets with different drift severity and drift speed. The experimental results show that the proposed method can detect different types of concept drift quickly and accurately and can figure out the locations where concept drift appeared. Key words: concept drift; data stream; heterogeneous Euclidean distance; overlap data windows

Considering the problems caused by the traditional association rules visualization approaches, including being unable to display the frequent pattern and relationships of items, unitary express, especially being not conducive to represent multi-schema association rules, a new visualizing algorithm for multi-valued association rules mining was proposed. It introduced the redefinition and classification of multi-valued attribute data by using conceptual lattice and presented the multi-valued attribute items of frequent itemset and association rules with concept lattice structure. This methodology was able to achieve frequent itemset visualization and multi-schema visualization of association rules, including the type of one to one, one to many, many to one, many to many and concept hierarchy. At last, the advantages of these new methods were illustrated with the help of experimental data obtained from demographic data of a province, and the source data visualization, frequent pattern and association relation visual representation of the demographic data were also achieved. The practical application analysis and experimental results prove that the schema has more excellent visual effects for frequent itemset display and authentical multi-schema association rules visualization.

To solve the problem that the existing IP covert timing channel need to make agreed the encoding scheme between the information and IP packet timing interval, can’t dynamic adjustment of double-blind according to the network transmission quality in the process of sending and receiving, a double-blind dynamic adjustment strategy what is used to negotiate IP covert timing channel encoding scheme is proposed. By segmenting the network environment and depending on the dynamic network environment, the strategy selected the default encoding scheme, achieved double-blind dynamic adjustment of encoding scheme between sender and receiver. In order to verify the reliability of strategy, the experimental environment of covert timing channel is constructed. The results show that the proposed method can achieve double-blind dynamic adjustment between sender and receiver.

Many applications of computer-aided language learning system lack of evaluation & feedback of learning speech. Auto speech recognition could provide a score based on likelihood. To describe an oral English training system. Besides normal speech evaluation, error detection and prosody modification based on the result of phoneme alignment and phoneme recognition are provided by this system. Recommendations for improvement queried from correction knowledge base offer learners a timely help. Current experiment shows most mistakes made unconsciously by high-level 2nd language speaker could be corrected by it.