Application of Deep Neural Network Algorithm in the Analysis of Legal Precedent Citation Basis

In order to improve the retrieval efficiency of civil litigation cases, the research introduces the fuzzy neural network algorithm and constructs a targeted retrieval algorithm system. In the simulation verification, it is found that, in the artificial subjective evaluation results of the expert group, the comprehensive score of reference cases given by the retrieval scheme exceeds the level of reference cases in the cases promoted and studied by the Supreme Court. The use of this scheme can effectively save the preparation time of prelitigation documents and help to improve the fairness and justice of the court trial process. It is proved that the retrieval scheme has certain popularization value.

Image Retrieval Model Analysis of Digital Library Based on Texture Characteristics

A new document image retrieval algorithm is proposed in view of the inefficient retrieval of information resources in a digital library. First of all, in order to accurately characterize the texture and enhance the ability of image differentiation, this paper proposes the statistical feature method of the double-tree complex wavelet. Secondly, according to the statistical characteristic method, combined with the visual characteristics of the human eye, the edge information in the document image is extracted. On this basis, we construct the meaningful texture features and use texture features to define the characteristic descriptors of document images. Taking the descriptor as the clue, the content characteristics of the document image are combined organically, and appropriate similarity measurement criteria are used for efficient retrieval. Experimental results show that the algorithm not only has high retrieval efficiency but also reduces the complexity of the traditional document image retrieval algorithm.

Geo-L: Topological Link Discovery for Geospatial Linked Data Made Easy

Geospatial linked data are an emerging domain, with growing interest in research and the industry. There is an increasing number of publicly available geospatial linked data resources, which can also be interlinked and easily integrated with private and industrial linked data on the web. The present paper introduces Geo-L, a system for the discovery of RDF spatial links based on topological relations. Experiments show that the proposed system improves state-of-the-art spatial linking processes in terms of mapping time and accuracy, as well as concerning resources retrieval efficiency and robustness.

Discrete Two-Step Cross-Modal Hashing through the Exploitation of Pairwise Relations

The cross-modal hashing method can map heterogeneous multimodal data into a compact binary code that preserves semantic similarity, which can significantly enhance the convenience of cross-modal retrieval. However, the currently available supervised cross-modal hashing methods generally only factorize the label matrix and do not fully exploit the supervised information. Furthermore, these methods often only use one-directional mapping, which results in an unstable hash learning process. To address these problems, we propose a new supervised cross-modal hash learning method called Discrete Two-step Cross-modal Hashing (DTCH) through the exploitation of pairwise relations. Specifically, this method fully exploits the pairwise similarity relations contained in the supervision information: for the label matrix, the hash learning process is stabilized by combining matrix factorization and label regression; for the pairwise similarity matrix, a semirelaxed and semidiscrete strategy is adopted to potentially reduce the cumulative quantization errors while improving the retrieval efficiency and accuracy. The approach further combines an exploration of fine-grained features in the objective function with a novel out-of-sample extension strategy to enable the implicit preservation of consistency between the different modal distributions of samples and the pairwise similarity relations. The superiority of our method was verified through extensive experiments using two widely used datasets.

A Novel Ensemble Architecture of Residual Attention-Based Deep Metric Learning for Remote Sensing Image Retrieval

Recently, deep metric learning (DML) has received widespread attention in the field of remote sensing image retrieval (RSIR), owing to its ability to extract discriminative features to represent images and then to measure the similarity between images via learning a distance function among feature vectors. However, the distinguishability of features extracted by the most current DML-based methods for RSIR is still not sufficient, and the retrieval efficiency needs to be further improved. To this end, we propose a novel ensemble architecture of residual attention-based deep metric learning (EARA) for RSIR. In our proposed architecture, residual attention is introduced and ameliorated to increase feature discriminability, maintain global features, and concatenate feature vectors of different weights. Then, descriptor ensemble rather than embedding ensemble is chosen to further boost the performance of RSIR with reduced time cost and memory consumption. Furthermore, our proposed architecture can be flexibly extended with different types of deep neural networks, loss functions, and feature descriptors. To evaluate the performance and efficiency of our architecture, we conduct exhaustive experiments on three benchmark remote sensing datasets, including UCMD, SIRI-WHU, and AID. The experimental results demonstrate that the proposed architecture outperforms the four state-of-the-art methods, including BIER, A-BIER, DCES, and ABE, by 15.45%, 13.04%, 10.31%, and 6.62% in the mean Average Precision (mAP), respectively. As for the retrieval execution complexity, the retrieval time and floating point of operations (FLOPs), needed by the proposed architecture on AID, reduce by 92% and 80% compared to those needed by ABE, albeit with the same Recall@1 between the two methods.

Defining the Learning Curve Is Necessary to Improve Oocyte Retrieval Skills: Lessons From a Single High-volume Reproductive Center

Background: A lack of formal and standard training program of assisted reproductive techniques, including oocyte retrieval procedure, is one common problem in China. It is obscure that how a novice trainee was trained to be qualified to perform oocyte retrieval procedure. The objective of this study was to determine the novice trainee’s learning curve for oocytes retrieval procedure through assessment of oocytes retrieval efficiency, operative time, and other operative characteristics. Methods: This retrospective cohort study included 200 consecutive patients undergoing transvaginal ultrasound guided oocytes retrieval procedure. Those patients underwent oocyte retrieval procedure by a single operator and one experienced supervisor. Their clinical data, including demographic data, ovarian stimulation cycle information, surgical procedure, and laboratory data were collected over 3 months. CUSUM analyses based on the operative time were performed to determine the learning curve. Results: The mean operative time was 10.10 min. Based on the CUSUM plot of operative time, the learning curve can be divided into three separated phases, phase 1 (case 1 to case 49) was learning phase, phase 2 (case 50 to case 130) was acquisition phase, and phase 3 (case 131 to case 200) was proficiency phase. The operative time was significantly shortened from phase 1 to phase 3 (phase 1, 13.37 ± 4.83min; phase 2, 10.21 ± 3.30 min; phase 3, 7.67 ± 3.24 min, P < 0.001). The oocyte retrieval efficiency was also notably improved from phase 1 to phase 3 (78.2% to 100% based on method 1 to determine oocyte retrieval efficiency, and 104.2% to 121.1% based on method 2 to determine oocyte retrieval efficiency). The retrieved oocytes number, the fertilization rate, clinical pregnancy rate among the three phases showed no significant differences. No patients had severe adverse events. As determined by multiple linear regression, learning phase is the only independent predictor of oocyte retrieval efficiency. Conclusion: Trainees practice transvaginal ultrasound guided oocytes retrieval are expected to achieve a stabilized procedure over consecutive training cases, with acquisition of the skills at 49 cases, and proficiency at 130 cases. Cumulative operative experience can improve the operative time and oocytes retrieval efficiency, but showed minimal influence on retrieved oocytes number and reproductive outcomes.

Applying Similarity Measures to Improve Query Expansion

The huge evolving in the information technologies, especially in the few last decades, has produced an increase in the volume of data on the World Wide Web, which is still growing significantly. Retrieving the relevant information on the Internet or any data source with a query created by a few words has become a big challenge. To override this, query expansion (QE) has an important function in improving the information retrieval (IR), where the original query of user is recreated to a new query by appending new related terms with the same importance. One of the problems of query expansion is the choosing of suitable terms. This problem leads to another challenge of how to retrieve the important documents with high precision, high recall, and high F measure. In this paper, we solve this problem through applying different similarity measures with the use of English WordNet. The obtained results proved that, with a suitable selection method, we are able to take advantage of English WordNet to improve the retrieval efficiency. The work proposed in this paper is extracting the terms from all the documents and query, then applying the following steps: preprocessing, expanding the query based on English WordNet, selecting the best terms, weighting of term, and finally using the cosine similarity and Jaccard similarity to obtain the relevant documents. Our practical results were applied on the DUC2002 dataset that contains 559 documents distributed over several categories. The average precision of cosine (for random queries) = 100% whereas the average precision of Jaccard = 84.4 %, and the average recall of cosine = 86.8% whereas the average recall of Jaccard = 73.4%. The average f-measure of cosine = 92%, whereas the average f-measure of Jaccard = 76%.

An Attribute-Based Access Control Policy Retrieval Method Based on Binary Sequence

With the widespread application of new technologies, fine-grained authorization requires a large number of access control policies. However, the existing policy retrieval method applied to a large-scale policy environment has the problem of low retrieval efficiency. Therefore, this paper proposes an attribute access control policy retrieval method based on the binary sequence. This method uses binary identification and binary code to express access control requests and policies. When the policy is retrieved, the appropriate group is selected through the logical operation of the access control request and the policy binary identification. Within the group, the binary code of the access control request is matched with the binary code of all rules to find suitable rules, thereby reducing the number of matching attribute-value pairs in the rule and improving the efficiency of policy retrieval. Experimental results show that the policy retrieval method proposed in this paper has higher retrieval efficiency.