With the rapid development of embeddedtechnology, mobile devices have been widely used than before. Face recognition has also been taken as a key application with PCA as the basic algorithm. Though PCA can provide ba...
详细信息
In this paper, a discriminant manifold learning method based on Locally Linear Embedding (LLE), which is named Locally Linear Representation Fisher Criterion (LLRFC), is proposed for the classification of tumor gene e...
详细信息
Medication guide ontology has great significance for semantic study of rational use of drugs. Nowadays, medical ontology is mainly constructed manually, which spends a lot in time and manpower, and is also difficult t...
详细信息
Emotional agents are useful to variety of computer application. This paper focuses on the emotion surprise. Surprise is the automatic reaction to a mismatch, which plays an important role in the behaviors of intellige...
详细信息
In the protein-protein interactions (PPI), hot regions are the key factor to maintain metabolism and the cause of disease. The conformation and prediction of the hot regions effectively is a worth researching topics. ...
详细信息
ISBN:
(纸本)9781479913114
In the protein-protein interactions (PPI), hot regions are the key factor to maintain metabolism and the cause of disease. The conformation and prediction of the hot regions effectively is a worth researching topics. This paper proposes a prediction method of hot regions based on complex network and community detection. In the prediction process, a hotspots retrieving strategy is used to exploit false positive (FP) and false negative (FN) residues, and these FP and FN residues achieved from the previous hotspots prediction. The method has advantage to identify hot regions in PPI, and experimental results show that the method not only improves the prediction accuracy of hot regions, but also has more reliability.
This paper presents a novel despeckling algorithm that can be used to enhance image quality in medical ultrasound images. Firstly, the log-transformed images are transformed by dual-tree complex wavelet transform (DTC...
详细信息
ISBN:
(纸本)9781479923427
This paper presents a novel despeckling algorithm that can be used to enhance image quality in medical ultrasound images. Firstly, the log-transformed images are transformed by dual-tree complex wavelet transform (DTCWT). And then, we use a non-Gaussian statistical model with an adaptive smoothing parameter for ideal image signal in the transformed domain. According to Bayesian theory, the MAP estimator is obtained with a proposed adaptive threshold which has better despeckling performance by exploiting the interscale properties of wavelet coefficients. The proposed approach results in significant speckle reduction and preserve details of ultrasound images at the same time while the introduced distortions are not noticeable.
This paper is written to extend on my previous paper, Nayyar (2004). This paper categorizes G-SQL that was presented in the previous paper as a Graph Manipulation Language (GML) for generation of graphs. This paper in...
详细信息
This paper is written to extend on my previous paper, Nayyar (2004). This paper categorizes G-SQL that was presented in the previous paper as a Graph Manipulation Language (GML) for generation of graphs. This paper introduces a new `Graph Definition Language for G-SQL' (GDL) and also updates GML to integrate with the new GDL. The purpose of the new Graph Definition Language (GDL) is to create a customized look and feel of graphs. It shall provide the user with the ability to define the UI components of a graph such as graph size, color of plotted values, graph title attributes etc.
The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our...
详细信息
The Cross-lingual Dependency Parsing (XDP) task poses a significant challenge due to the differences in dependency structures between training and testing languages, known as the out-of-distribution (OOD) problem. Our research delved into this issue in the XDP dataset by selecting 43 languages from 22 language families. We found that the primary factor of the OOD problem is the unbalanced length distribution among languages. To address the impact of the OOD problem, we propose deep stable learning for Cross-lingual Dependency Parsing (SL-XDP), which utilizes deep stable learning with a feature fusion module. In detail, we implemented five feature fusion operations for generating comprehensive representations with dependency relations and the deep stable learning algorithm to decorrelate dependency structures with sequence length. Our experiments on Universal Dependencies have demonstrated that SL-XDP can lessen the impact of the OOD problem and improve the model generalization among 21 languages, with a maximum improvement of 18%.
暂无评论