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How to protect the copyright of digital media over the Internet is a problem for the creator/owner. A novel support vector regression (SVR) based digital audio watermarking scheme in the wavelet domain which using subsampling is proposed in this paper. The audio signal is subsampled and all the sub-audios are decomposed into the wavelet domain respectively. Then the watermark information is embedded into the low-frequency region of random one sub-audio. With the high correlation among the sub-audios, accordingly, the distributing rule of different sub-audios in the wavelet domain is similar to each other, SVR can be used to learn the characteristics of them. Using the information of unmodified template positions in the low-frequency region of the wavelet domain, the SVR can be trained well. Thanks to the good learning ability of SVR, the watermark can be correctly extracted under several different attacks. The proposed watermarking method which doesn't require the use of the original audio signal for watermark extraction can provide a good copyright protection scheme. The experimental results show the algorithm is robust to signal processing, such as lossy compression (MP3), filtering, resampling and requantizing, etc. [ABSTRACT FROM AUTHOR]

Keyphrase extraction is a task with many applications in information retrieval, text mining, and natural language processing. In this paper, a keyphrase extraction approach based on neural network is proposed. To determine whether a phrase is a keyphrase, the following features of a phrase in a given document are adopted: its term frequency and inverted document frequency, whether to appear in the title or headings (subheadings) of the given document, and its frequency appearing in the paragraphs of the given document. The algorithm is evaluated by the standard information retrieval metrics of precision and recall, and human assessment. [ABSTRACT FROM AUTHOR]

This research article demonstrates the use of a constraints network for modeling the knowledge which is necessary for concurrent product design. A Knowledge-based Constraints Network System (KCNS) has been developed to maintain design consistency and to support the selection of appropriate design parameter intervals. A data-mining algorithm named fuzzy-rough algorithm is developed to acquire the knowledge level constraints from the numerical simulation. The method integrated Case Based Reasoning (CBR) and Rule Based Reasoning (RBR) with interval consistency algorithm is adopted to predict the potential conflicts and to specify the interval of design parameters. The design example of a crank connecting link in a V6 engine shows the validity of the system. [ABSTRACT FROM AUTHOR]

As a kind of data-driven decision support systems, business intelligence tools focus too much on data. In order to provide the business intelligence system with the ability of process-driven decision making, we introduce the concept of business process management to the current business intelligence system. With the implementation of case-base reasoning and rule-base reasoning technology, the process models can be built and managed efficiently. In this paper we also provide a strategy for data mining experience reuse. Process models in our system are all defined based on XML. [ABSTRACT FROM AUTHOR]

How to filter emails is a problem for Internet users. Support vector machine (SVM) is a valid filtering emails method. As it is well known, there exists uncertainty in deciding the legitimate email by Internet users. To formalize the uncertainty, the legitimate email is understood as fuzzy concept on a set of email samples in this paper, its membership function is obtained by aggregating opinions of Internet users, and aggregation operator is ordered weighted averaging (OWA) operator. Due to email training samples with membership degrees of the legitimate email, fuzzy support vector machine (FSVM) is adopted to classify emails, and penalty factor of FSVM is decided by content-specific misclassification costs. The advantages of our method are: 1) uncertainty of the legitimate email, i.e., membership degree, is considered in classifying emails, and a method to obtain membership degree is given; 2) content-specific misclassification costs is used to decide penalty factor of FSVM. Simulative experiments are shown to the effectiveness and human consistent of our method. [ABSTRACT FROM AUTHOR]

Benchmarking is a technique that engages and executes a series of measures to change indexes of the Decision Making Unit (DMU) to excellent by using the gap analysis information between the DMU and benchmark. In this paper, a DEA-Benchmarking model based on the theory of maximum entropy is proposed and the conception of Entropy-DEA efficiency is defined. According to the optimization model based on the theory of maximum entropy, the Entropy-DEA efficient DMUs is regarded as benchmarks, which have more advantages and direction than DEA efficient DMUs. The measure method and existence property of Entropy-DEA efficiency are all analyzed in this paper. [ABSTRACT FROM AUTHOR]

To establish a practical CAD (Computer-Aided Diagnosis) system to facilitate the diagnosis of microcalcifications, we propose a filter-based technique to detect microcalcifications. Via examination of an existing optimal filter-based technique, it is found that its performance on highlighting the energy of mammograms is seriously affected by artefacts and the background of breast. As a result, four methods in pre and post-processing are described in this paper to improve the optimal filtering, leading to an adaptive selection of thresholds for input mammograms. These methods have been tested by using 30 mammograms (with 25 microcalcifications) from the MIAS database and 23 mammograms from DDSM database. Comparing with the original optimal filter-based technique, our technique reduces the false detections (FD), eliminates the influence of the background in mammograms and is able to adaptively select the threshold for the detection of microcalcifications. [ABSTRACT FROM AUTHOR]