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The development of evaluating a correlation is a critical element in the assessment of correlation between different objects. This study proposes a new method to measure the correlation between a possible factor with an object and give a interpretation from the perspective of principal components. To test this, some water quality data was used to analyze, the result demonstrates the effectiveness of theoretical description and formula we provided.

Digital forensics investigator faces the challenge of reliability of forensic conclusions. Formal automatic analysis method is helpful to deal with the challenge. The finite state machine analysis method tries to determine all possible sequences of events that could have happened in a digital system during an incident. Its basic idea is to model the target system using a finite state machine and then explore its all possible states on the condition of available evidence. Timed mealy finite state machine is introduced to model the target system, and the formalization of system running process and evidence is presented to match the system running with possible source evidence automatically. Based on Gladyshev's basic reasoning method, general reasoning algorithms with multi strategies are developed to find the possible real scenarios. Case study and experimental results show that our method is feasible and adaptable to possible cases and takes a further step to practical formal reasoning for digital forensics.

Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.

Intrusion Detection Systems (IDSs) need a mass of labeled data in the process of training, which hampers the application and popularity of traditional IDSs. Classical principal component analysis is highly sensitive to outliers in training data, and leads to poor classification accuracy. This paper proposes a novel scheme based on robust principal component classifier, which obtains principal components that are not influenced much by outliers. An anomaly detection model is constructed from the distances in the principal component space and the reconstruction error of training data. The experiments show that this proposed approach can detect unknown intrusions effectively, and has a good performance in detection rate and false positive rate especially.

Intrusion detection is an essential component of critical infrastructure protection mechanism. Since many current IDSs are constructed by manual encoding of expert knowledge, it is time-consuming to update their knowledge. In order to solve this problem, an effective method for misuse intrusion detection with low cost and high efficiency is presented. This paper gives an overview of our research in building a detection model for identifying known intrusions, their variations and novel attacks with unknown natures. The method is based on rough set theory and capable of extracting a set of detection rules from network packet features. After getting a decision table through preprocessing raw packet data, rough-set-based reduction and rule generation algorithms are applied, and useful rules for intrusion detection are obtained. In addition, a rough set and rule-tree-based incremental knowledge acquisition algorithm is presented in order to solve problems of updating rule set when new attacks appear. Compared with other methods, our method requires a smaller size of training data set and less effort to collect training data. Experimental results demonstrate that our system is effective and more suitable for online intrusion detection.

There are already some extensions of rough set theory for incomplete information systems, such as tolerance relation, limited tolerance relation, similarity relation, and etc. But there are no approaches and algorithms for these extensions. A direct approach for processing incomplete information systems is developed in this paper, including discretization, attribute reduction, value reduction, and rule matching. This approach can be used in all kinds of extensions of rough set theory for incomplete information systems. It is both effective in complete and incomplete information systems.

One main technical means of anti-Spam is to build filters in email transfer route. However, the design of many junk mail filters hasn't made use of the whole security information in an email, which exists mostly in mail header rather than in the text and accessory. In this paper, data mining based on rough sets is introduced to design a new anti-Spam filter. Firstly, by recording and analyzing the header of every collected email sample, we get all necessary original raw data. Next, by selecting and computing features from the original header data, we obtain our decision table including several condition attributes and one decision attribute. Then, a data mining technique based on rough sets, which mainly includes relative reduction and rule generation, is introduced to mine this decision table. And we obtain some useful anti-Spam knowledge from all the email headers. Finally, we have made tests by using our rules to judge different mails. Tests demonstrate that when mining on selected baleful email corpus with specific Spam rate, our anti-Spam filter has high efficiency and high identification rate. By mining email headers, we can find potential security problems of some email systems and cheating methods of Spam senders.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Rough set is a valid mathematical theory developed in recent years. It has the ability to deal with imprecise, uncertain, and vague information. It has been applied in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring successfully. In this paper, we will make a comparative study of the algebra view and information view of rough set theory. Some inequivalent relationships between these two views of rough set theory in inconsistent decision table systems are discovered. It corrects an error of many researchers, that is, the algebra view and information view of rough set theory are equivalent. It is helpful for developing heuristic knowledge reduction algorithms for inconsistent decision table systems.

Rough set theory is emerging as a new tool for dealing with fuzzy and uncertain data. In this paper, a theory is developed to express, measure and process uncertain information and uncertain knowledge based on our result about the uncertainty measure of decision tables and decision rule systems. Based on Skowron’s propositional default rule generation algorithm, we develop an initiative learning model with rough set based initiative rule generation algorithm. Simulation results illustrate its efficiency.

Rough Set is a valid mathematical theory developed in recent years, which has the ability to deal with imprecise, uncertain, and vague information. It has been applied in such fields as machine learning, data mining, intelligent data analyzing and control algorithm acquiring successfully. Many researchers have studied rough sets in different view. In this paper, the authors discuss the reduction of knowledge using information entropy in rough set theory. First, the changing tendency of the conditional entropy of decision attributes given condition attributes is studied from the viewpoint of information. Then, two new algorithms based on conditional entropy are developed. These two algorithms are analyzed and compared with MIBARK algorithm. Furthermore, our simulation results show that the algorithms can find the minimal reduction in most cases.

Aiming at the demand of adaptive wavelet transforms via lifting, a three-stage lifting scheme (predict-update-adapt) is proposed according to common two-stage lifting scheme (predict-update) in this paper. The second stage is updating stage. The third is adaptive predicting stage. Our scheme is an update-then-predict scheme that can detect jumps in image from the updated data and it needs not any more additional information. The first stage is the key in our scheme. It is the interim of updating. Its coefficient can be adjusted to adapt to data to achieve a better result. In the adaptive predicting stage, we use symmetric prediction filters in the smooth area of image, while asymmetric prediction filters at the edge of jumps to reduce predicting errors. We design these filters using spatial method directly. The inherent relationships between the coefficients of the first stage and the other stages are found and presented by equations. Thus, the design result is a class of filters with coefficient that are no longer invariant. Simulation result of image coding with our scheme is good.

In this paper, we discuss the design of an MPEG-4 compression technology based multimedia e-mail system. As our test in ISDN context indicates, the MPEG-4 gives high compression ratio up to 130:1 without discernable quality deterioration in multimedia e-mail application where video is featured by infrequent and slow motions. It is an exciting improvement over other compressors in this area. The performance comparison of our system with other similar systems is also given in this paper. IN our multimedia e- mail system, messaging subsystem is implemented with Messaging Application Programming Interface (MAPI). A special architecture of multimedia e-mail package is also presented.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

In this paper, we propose an approach that can generate logical rules from an information system. It is based on Pawlak's rough set theory. There are two steps in our rule generation approach. First, attribute reduction is done on an information table according to Skowron's discernibility matrix and logic function simplification, some important and valuable attributes are extracted. Then, value reduction is performed and corresponding logic rules are generated. All reducts including the minimal reduct of an information system can be obtained through these two reductions. Our approach can generate both the maximal generalized decision rules as well as potential interesting and useful rules according to requirements.

As the amount of information in the world is steadily increasing, there is a growing demand for tools for analyzing the information. Many scholars have been working hard to study machine learning in order to obtain knowledge from domain data sets. They hope to find patterns in terms of implicit dependencies in data. Artificial neural networks are efficient computing models which have shown their strengths in solving hard problems in artificial intelligence. They have also been shown to be universal approximators. Some scholars have done much work to interpret neural networks so that they will no longer be seen as black boxes and provided some plots and methods for knowledge acquisition using neural networks. These can be classified into three categories: fuzzy neural networks, CF (certainty factor) based neural networks, and logical neurons. We review some of these research works in this paper.