Search

With the rapid development of internet technology and the increasing popularity of e-commerce, data encryption technology plays a very important role in data security. Information security has two aspects: security protocol and cryptographic algorithm and the latter is the foundation and core technology of information security. Advanced Encryption Standard (AES) encryption algorithm is one of the most commonly used algorithms in symmetric encryption algorithms. Such algorithms face issues when used in the context of key management and security functions. This paper focuses on the systematic analysis of these issues and summarizes AES algorithm implementation, comprehensive application and algorithm comparison with other existing methods. To analyze the performance of the proposed algorithm and to make full use of the advantages of AES encryption algorithm, one needs to reduce round key and improve the key schedule, as well as organically integrate with RSA algorithm. Java language is used to implement the algorithm due to its large library, then to show the efficiency of the proposed method we compare different parameters, such as encryption/decryption speed, entropies and memory consumption...) with a classic algorithm. Based on the results of the comparison between AES and the hybrid AES algorithm, the proposed algorithm shows good performance and high security. It therefore can be used for key management and security functions, particularly for sharing sensitive files through insecure channel. This analysis provides a reference useful for selecting different encryption algorithms according to different business needs.

Plagiarism source retrieval is the core task of plagiarism detection. It has become the standard for plagiarism detection to use the queries extracted from suspicious documents to retrieve the plagiarism sources. Generating queries from a suspicious document is one of the most important steps in plagiarism source retrieval. Heuristic-based query generation methods are widely used in the current research. Each heuristic-based method has its own advantages, and no one statistically outperforms the others on all suspicious document segments when generating queries for source retrieval. Further improvements on heuristic methods for source retrieval rely mainly on the experience of experts. This leads to difficulties in putting forward new heuristic methods that can overcome the shortcomings of the existing ones. This paper paves the way for a new statistical machine learning approach to select the best queries from the candidates. The statistical machine learning approach to query generation for source retrieval is formulated as a ranking framework. Specifically, it aims to achieve the optimal source retrieval performance for each suspicious document segment. The proposed method exploits learning to rank to generate queries from the candidates. To our knowledge, our work is the first research to apply machine learning methods to resolve the problem of query generation for source retrieval. To solve the essential problem of an absence of training data for learning to rank, the building of training samples for source retrieval is also conducted. We rigorously evaluate various aspects of the proposed method on the publicly available PAN source retrieval corpus. With respect to the established baselines, the experimental results show that applying our proposed query generation method based on machine learning yields statistically significant improvements over baselines in source retrieval effectiveness.

The problem of text plagiarism has increased because of the digital resources available on the World Wide Web. Source Retrieval and Text Alignment are two core tasks of plagiarism detection. A plagiarism source retrieval and text alignment system based on relevance ranking model is described in this paper. Not only the source retrieval task but also the text alignment task is all regarded as a process of information retrieval, and the relevance ranking is used to search the plagiarism sources and obtain the candidate plagiarism seeds. For source retrieval, BM25 model is used, while for text alignment, Vector Space Model is exploited. Furthermore, a plagiarism detection system named HawkEyes is developed based on the proposed methods and some demonstrations of HawkEyes are given.

Providing effective methods of identification of high-obfuscation plagiarism seeds presents a significant research problem in the field of plagiarism detection. The conventional methods of plagiarism detection are based on single type of features to capture plagiarism seeds. But for high-obfuscation plagiarism detection, these single type features are not sufficient for identifying the plagiarism seeds effectively because of the varied plagiarism methods used in high-obfuscation plagiarism. This paper presents a multi-features fusion method for the highobfuscation plagiarism seeds identification. This method exploits Logical Regression model to integrate lexicon features, syntax features, semantics features and structure features which extracted from suspicious document and source document. A multi-feature fusion classifier based on Logical Regression model is proposed to decide whether a text fragment pair can be regarded as plagiarism seeds or not. Experimental results on the PAN@CLEF2013 summary-obfuscation corpus show that the fusion of different types of features produces more accurate results.

Clustering analysis is an unsupervised method to find hidden structures in datasets and has been widely used in various fields. However, it is always difficult for users to understand, evaluate, and explain the clustering results in the spaces with dimension greater than three. Although high-dimensional visualization of clustering technology can express clustering results well, it still has significant limitations. In this paper, a visualization cluster analysis method based on the minimum distance spectrum (MinDS) is proposed, aimed at reducing the problems of clustering multidimensional datasets. First, the concept of MinDS is defined based on the distance between high-dimensional data. MinDS can map any dataset from high-dimensional space to a lower dimension to determine whether the data set is separable. Next, a clustering method which can automatically determine the number of categories is designed based on MinDS. This method is not only able to cluster a dataset with clear boundaries, but can also cluster the dataset with fuzzy boundaries through the edge corrosion strategy based on the energy of each data point. In addition, strategies for removing noise and identifying outliers are designed to clean datasets according to the characteristics of MinDS. The experimental results presented validate the feasibility and effectiveness of the proposed schemes and show that the proposed approach is simple, stable, and efficient, and can achieve multidimensional visualization cluster analysis of complex datasets.

As the unique nonlinear component of block ciphers, Substitution box (S-box) directly affects the safety of a cryptographic system. It is important and difficult to design strong S-box that simultaneously meets multiple cryptographic criteria such as bijection, nonlinearity, strict avalanche criterion (SAC), bit independence criterion (BIC), differential probability (DP), and linear probability (LP). Though many chaotic S-boxes have been proposed, the cryptographic performance of most of them needs to be further improved. A new chaotic S-box based on the intertwining logistic map and bacterial foraging optimization is designed in this paper. It firstly iterates the intertwining logistic map to construct many S-boxes and then applies a bacterial foraging optimization algorithm to find the optimal S-box. Moreover, bacterial foraging optimization algorithm considers the nonlinearity and differential uniformity as the fitness functions in the optimization process. We experiment that the proposed S-box can effectively resist multiple types of cryptanalysis attacks.

In recent years, robot technology has been rapidly developed and applied to every walk of life. With the promotion and popularization of the robot technology, requirement of using robot is becoming high and the demand of intelligent robots is particularly urgent. Machine vision is an important research direction in the field of intelligent robotics. In the robot vision system, the core problem is the targets extraction, and the image segmentation is the key technique of extracting targets accurately, rapidly and in real-time. Since the environment is complex and the targets are diverse, the amount of images data perceived by robots is large and the images are unpredictable. Thus, it is very important to extract and segment targets accurately. Aimed at the segmentation processing of high-resolution images, a novel clustering algorithm is proposed in this paper. According to the sum of the energy of data and their sizes, it recognizes the clustering representatives and members, and identifies the most probability members by the competition among data points. Then we apply the algorithm by combining the novel clustering algorithm into Mean Shift clustering algorithm in color image segmentation problem. The algorithm can quickly and efficiently achieve the targets segmentation of high-resolution images, and has good segmentation effect. The experiments show that the proposed approach has better clustering quality and is faster than the traditional clustering algorithm.

Clustering analysis is an unsupervised method to find out hidden structures in datasets. Most partitional clustering algorithms are sensitive to the selection of initial exemplars, the outliers and noise. In this paper, a novel technique called data competition algorithm is proposed to solve the problems. First the concept of aggregation field model is defined to describe the partitional clustering problem. Next, the exemplars are identified according to the data competition. Then, the members will be assigned to the suitable clusters. Data competition algorithm is able to avoid poor solutions caused by unlucky initializations, outliers and noise, and can be used to detect the coexpression gene, cluster the image, diagnose the disease, distinguish the variety, etc. The provided experimental results validate the feasibility and effectiveness of the proposed schemes and show that the proposed approach of data competition algorithm is simple, stable and efficient. The experimental results also show that the proposed approach of data competition clustering outperforms three of the most well known clustering algorithms K-means clustering, affinity propagation clustering, hierarchical clustering.

Psychological counseling Q&A system is enjoying a remarkable and increasing popularity in recent years. Knowledge base is the important component for such kind of systems, but it is difficult and time-consuming to construct the knowledge base manually. Fortunately, there emerges large number of Q&A pairs in many psychological counseling websites, which can provide good source enriching the knowledge base. This paper presents the method of knowledge extraction from psychological consulting Q&A pairs of on-line psychological counseling websites, which include keywords, semantic extension and word sequence. P-XML, which is the knowledge template based on XML, is also designed to store the knowledge. The extracted knowledge has been successfully used in our non-obstructive psychologycal counseling system, called P.A.L., and the experimental results also demonstrated the feasibility and effectiveness of our approach.

Word Sense Disambiguation (WSD) is to decide the sense of an ambiguous word on particular context. Most of current studies on WSD only use several ambiguous words as test samples, thus leads to some limitation in practical application. In this paper, we perform WSD study based on large scale real-world corpus using two unsupervised learning algorithms based on ±n-improved Bayesian model and Dependency Grammar (DG)-improved Bayesian model. ±n-improved classifiers reduce the window size of context of ambiguous words with close-distance feature extraction method, and decrease the jamming of useless features, thus obviously improve the accuracy, reaching 83.18% (in open test). DG-improved classifier can more effectively conquer the noise effect existing in Naive-Bayesian classifier. Experimental results show that this approach does better on Chinese WSD, and the open test achieved an accuracy of 86.27%.

The high-obfuscation plagiarism detection in big data environment, such as the paraphrasing and cross-language plagiarism, is often difficult for anti-plagiarism system because the plagiarism skills are becoming more and more complex. This paper proposes HawkEyes, a plagiarism detection system implemented based on the source retrieval and text alignment algorithms which developed for the international competition on plagiarism detection organized by CLEF. The text alignment algorism in HawkEyes gained the first place in PAN@CLEF2012. In the demonstration, we will present our system implemented on PAN@CLEF2014 training data corpus.

Being as an important nonlinear component of block ciphers, Substitution box (S-box) directly affect the security of the cryptographic systems. It is important and difficult to design cryptographically strong S-box that simultaneously meet with multiple cryptographic criteria such as bijection, non-linearity, strict avalanche criterion (SAC), bits independence criterion (BIC), differential probability (DP) and linear probability (LP). To address the issue, an S-box generation approach based on L-L cascade Chaotic Map and Line Map (LLCMLM) is proposed in this paper. L-L cascade chaotic map is used to generate an integer sequence ranging 0–255, and line map is applied to scramble the position of the integer sequence. A series of experiments have been conducted to compare multiple cryptographic criteria of LLCMLM with other algorithms. Simulation results indicate that LLCMLM meets well with the design criteria of the S-box.

Being a measurable criterion of clustering quality for the classical K-means algorithm, the objective function always exists many local minimum values. The objective function may converge at some minimum values, when the initial clustering centers are dropped neighbor to the local minimum values, or the two data objects in the same cluster are regarded as two initial clustering centers which represent two clusters. Then, the problem of local optimal solution will happen. To this, a K-means clustering algorithm based on the maximum triangle rule (KMTR) is proposed in this paper. KMTR, which uses the rule of maximum triangle, selects appropriate initial clustering centers for the classical K-means algorithm. Experimental results on some UCI data sets show the validity of applying maximum triangle rule to the K-means algorithm.