Signature verification is an important part of digital forensics. In order to solve the shortcomings of manual identification in technical accuracy and subjectivity, this paper proposed an off-line signature identific...
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Signature verification is an important part of digital forensics. In order to solve the shortcomings of manual identification in technical accuracy and subjectivity, this paper proposed an off-line signature identification method based on Support Vector Machine (SVM). A powerful feature set is collected by extracting grid features and global features of a signature picture. The method is applied for identifying different writing systems and the highest correct probability of identification arrives at 100%. The results indicated that the method is workable and can be an effectively technical support for digital forensics.
With the development of social media, online documents such as the comments of news articles, blogs and microblogs have received great attention, and the sentiment analysis via online documents has become one popular ...
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With the development of social media, online documents such as the comments of news articles, blogs and microblogs have received great attention, and the sentiment analysis via online documents has become one popular research area. This paper focuses on establishing user sentimental space obtained from online documents to analyze user's personalized sentiments, which aims to identify user's sentimental feature. Affection, sentiment and attributes of user are firstly employed to build user's personalized sentimental space. Then, the general constrains of user sentiments space are proposed to calculate user's personality. And finally we seek out sentimental leaders who paly pivotal role in the leading public opinions. Our works can give some suggestions for decision makers when urgent event happen.
People can usually give reasons for recognizing a particular object as a specific category, using various means such as body language (by pointing out) and natural language (by telling). This inspires us to develop a ...
People can usually give reasons for recognizing a particular object as a specific category, using various means such as body language (by pointing out) and natural language (by telling). This inspires us to develop a recognition model with such principles to explain the recognition process to enhance human trust. We propose Semantic Prototype Analysis Network (SPANet), an interpretable object recognition approach that enables models to explicate the decision process more lucidly and comprehensibly to humans by "pointing out where to focus" and "telling about why it is" simultaneously. With the proposed method, some part prototypes with semantic concepts will be provided to elaborate on the classification together with a group of visualized samples to achieve both part-wise and semantic interpretability. The results of extensive experiments demonstrate that SPANet is able to recognize objects almost as well as the non-interpretable models, at the same time generating intelligible explanations for its decision process.
In this article, a novel approach is proposed to predict RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS). In the RNATS algorithm, two search models, intensification searc...
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In this article, a novel approach is proposed to predict RNA secondary structure called RNA secondary structure prediction based on Tabu Search (RNATS). In the RNATS algorithm, two search models, intensification search and diversification search, are developed to exploit the local regions around the current solution and explore the unvisited solution space, respectively. Simulation experiments are conducted on eight RNA sequences to show that the proposed method is feasible and effective.
Common algorithmic problem is an optimization problem, which has the nice property that several other NPcomplete problems can be reduced to it in linear time. A tissue P system with cell division is a computing model ...
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G-chain transitive and G-chain mixing have an important significance in terms of theory and application. According to the definition of chain transitive and chain mixing, we give the concept of G-chain transitiveand G...
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The problem of base station (BS) planning for TD-LTE network is studied in this paper. This method takes various factors into consideration according to the public attitude. The public attitude plays an important role...
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ISBN:
(纸本)9781479970926
The problem of base station (BS) planning for TD-LTE network is studied in this paper. This method takes various factors into consideration according to the public attitude. The public attitude plays an important role in the process of BS planning. We choose the area where the network performance is the worst to build BS, according to public attitude, Monte Carlo method and least cost path analysis. The experience results show that after building a few BS, the network performance of planning area is generally good. The method has good application value.
Answering questions that require reading texts in an image is challenging for current models. One key difficulty of this task is that rare, polysemous, and ambiguous words frequently appear in images, e.g., names of p...
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ISBN:
(数字)9781728171685
ISBN:
(纸本)9781728171692
Answering questions that require reading texts in an image is challenging for current models. One key difficulty of this task is that rare, polysemous, and ambiguous words frequently appear in images, e.g., names of places, products, and sports teams. To overcome this difficulty, only resorting to pre-trained word embedding models is far from enough. A desired model should utilize the rich information in multiple modalities of the image to help understand the meaning of scene texts, e.g., the prominent text on a bottle is most likely to be the brand. Following this idea, we propose a novel VQA approach, Multi-Modal Graph Neural Network (MM-GNN). It first represents an image as a graph consisting of three sub-graphs, depicting visual, semantic, and numeric modalities respectively. Then, we introduce three aggregators which guide the message passing from one graph to another to utilize the contexts in various modalities, so as to refine the features of nodes. The updated nodes have better features for the downstream question answering module. Experimental evaluations show that our MM-GNN represents the scene texts better and obviously facilitates the performances on two VQA tasks that require reading scene texts.
Multi-label Out-Of-Distribution (OOD) detection aims to discriminate the OOD samples from the multi-label In-Distribution (ID) ones. Compared with its multiclass counterpart, it is crucial to model the joint informati...
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The main idea of SVM, i.e. Support Vector Machine, is mapping nonlinear separable data into higher dimension linear space where the data can be separated by hyper plane. Based on Jordan Curve Theorem, a general classi...
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The main idea of SVM, i.e. Support Vector Machine, is mapping nonlinear separable data into higher dimension linear space where the data can be separated by hyper plane. Based on Jordan Curve Theorem, a general classification method HSC, Classification based on Hyper Surface, is put forward in this paper. The separating hyper surface is directly made to classify large database. The data are classified according to whether the intersecting number is odd or even. It is a novel approach which has no need of either mapping from lower dimension space to higher dimension space or considering kernel function. It can directly solve the nonlinear classification problem. The experiments show that the new method can efficiently and accurately classify large data.
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