Design of Legal Chinese English Simultaneous Interpretation System based on PSO Algorithm

Aiming at the phenomenon of “wrong words” and “missing words” in the process of Chinese English legal interpretation, a Chinese English legal simultaneous interpretation system based on PSO algorithm is designed. According to the construction requirements of fuzzy neural network, the optimization results of PSO inertia weight are determined, and then the system model optimization based on PSO algorithm is realized with the help of membership function. On this basis, this paper analyzes the key trigger factors of simultaneous interpretation, and distinguishes the specific differences between consecutive interpretation load and simultaneous interpretation by defining the way of legal Chinese English text transmission effect, so as to realize the smooth application of legal Chinese English simultaneous interpretation system based on PSO algorithm. The results shows that, compared with the consecutive interpretation system, the simultaneous interpretation system can effectively solve all the problems of “wrong words” and “missing words” in the process of legal Chinese English document translation, and effectively guarantee the authenticity of document samples.

Comparison of Gradient-Based Edge Detectors Applied on Mammograms

Breast cancer is one of the most common types of cancer amongst women, but it is also one of the most frequently cured cancers. Because of this, early detection is crucial, and this can be done through mammography screening. With the increasing need of an automated interpretation system, a lot of methods have been proposed so far and, regardless of the algorithms, they all share a step: pre-processing. That is, identifying the image orientation, detecting the breast and eliminating irrelevant parts. This paper aims to describe, analyze, compare and evaluate six of the most commonly used edge detection operators: Sobel, Roberts Cross, Prewitt, Farid and Simoncelli, Scharr and Canny. We detail the algorithms, their implementations and the metrics used for evaluation and continue by comparing the operators both visually and numerically, finally concluding that Canny best suit our needs.

Reputation Description and Interpretation

<p>Reputation is an opinion held by others about a particular person, group, organisation, or resource. As a tool, reputation can be used to forecast the reliability of others based on their previous actions, moreover, in some domains it can even be used to estimate trustworthiness. Due to the large scale of virtual communities it is impossible to maintain a meaningful relationship with every member. Reputation systems are designed explicitly to manufacture trust within a virtual community by recording and sharing information regarding past interactions. Reputation systems are becoming increasingly popular and widespread, with the information generated varying considerably between domains. Currently, no formal method to exchange reputation information exists. However, the OpenRep framework, currently under development, is designed to federate reputation information, enabling the transparent exchange of information between reputation systems. This thesis presents a reputation description and interpretation system, designed as a foundation for the OpenRep framework. The description and interpretation system focuses on enabling the consistent and reliable expression and interpretation of reputation information across heterogeneous reputation systems. The description and interpretation system includes a strongly typed language, a verification system to validate usage of the language, and a XML based exchange protocol. In addition to these contributions, three case studies are presented as a means of generating requirements for the description and interpretation system, and evaluating the use of the proposed system in a federated reputation environment. The case studies include an electronic auction, virtual community and social network based relationship management service.</p>

Reputation Description and Interpretation

<p>Reputation is an opinion held by others about a particular person, group, organisation, or resource. As a tool, reputation can be used to forecast the reliability of others based on their previous actions, moreover, in some domains it can even be used to estimate trustworthiness. Due to the large scale of virtual communities it is impossible to maintain a meaningful relationship with every member. Reputation systems are designed explicitly to manufacture trust within a virtual community by recording and sharing information regarding past interactions. Reputation systems are becoming increasingly popular and widespread, with the information generated varying considerably between domains. Currently, no formal method to exchange reputation information exists. However, the OpenRep framework, currently under development, is designed to federate reputation information, enabling the transparent exchange of information between reputation systems. This thesis presents a reputation description and interpretation system, designed as a foundation for the OpenRep framework. The description and interpretation system focuses on enabling the consistent and reliable expression and interpretation of reputation information across heterogeneous reputation systems. The description and interpretation system includes a strongly typed language, a verification system to validate usage of the language, and a XML based exchange protocol. In addition to these contributions, three case studies are presented as a means of generating requirements for the description and interpretation system, and evaluating the use of the proposed system in a federated reputation environment. The case studies include an electronic auction, virtual community and social network based relationship management service.</p>

AutoCNV: a semiautomatic CNV interpretation system based on the 2019 ACMG/ClinGen Technical Standards for CNVs

Background The American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen) presented technical standards for interpretation and reporting of constitutional copy-number variants in 2019 (the standards). Although ClinGen developed a web-based CNV classification calculator based on scoring metrics, it can only track and tally points that have been assigned based on observed evidence. Here, we developed AutoCNV (a semiautomatic automated CNV interpretation system) based on the standards, which can automatically generate predictions on 18 and 16 criteria for copy number loss and gain, respectively. Results We assessed the performance of AutoCNV using 72 CNVs evaluated by external independent reviewers and 20 illustrative case examples. Using AutoCNV, it showed that 100 % (72/72) and 95 % (19/20) of CNVs were consistent with the reviewers’ and ClinGen-verified classifications, respectively. AutoCNV only required an average of less than 5 milliseconds to obtain the result for one CNV with automated scoring. We also applied AutoCNV for the interpretation of CNVs from the ClinVar database and the dbVar database. We also developed a web-based version of AutoCNV (wAutoCNV). Conclusions AutoCNV may serve to assist users in conducting in-depth CNV interpretation, to accelerate and facilitate the interpretation process of CNVs and to improve the consistency and reliability of CNV interpretation.

Design of Chinese-English Wireless Simultaneous Interpretation System Based on Speech Recognition Technology

A Chinese-English wireless simultaneous interpretation system based on speech recognition technology is suggested to solve the problems of low translation accuracy and a high number of ambiguous terms in current Chinese-English simultaneous interpretation systems. The system’s general structure and hardware architecture are summarized. The chairman unit, representative unit, transliteration unit, and auditing unit are the four basic components of the simultaneous interpretation system. The CPU is the nRF24E1 hardware wireless radio frequency transceiver chip, while the chairman machine, representative machine, translator, and auditorium are all created separately. Speech recognition technology is used by the system software to create a speech recognition process that properly produces speech-related semantics. The input text is used as the search criteria, a manual interactive synchronous translation program is created, and the results for the optimum translation impact are trimmed. The experimental findings reveal that this system’s sentence translation accuracy rate is 0.9–1.0, and the number of ambiguous terms is minimal, which is an improvement on previous systems’ low translation accuracy.

Development of A Computer Aided Real-Time Interpretation System for Indigenous Sign Language in Nigeria Using Convolutional Neural Network

Sign language is the primary method of communication adopted by deaf and hearing-impaired individuals. The indigenous sign language in Nigeria is one area receiving growing interest, with the major challenge faced is communication between signers and non-signers. Recent advancements in computer vision and deep learning neural networks (DLNN) have led to the exploration of necessary technological concepts towards tackling existing challenges. One area with extensive impact from the use of DLNN is the interpretation of hand signs. This study presents an interpretation system for the indigenous sign language in Nigeria. The methodology comprises three key phases: dataset creation, computer vision techniques, and deep learning model development. A multi-class Convolutional Neural Network (CNN) is designed to train and interpret the indigenous signs in Nigeria. The model is evaluated using a custom-built dataset of some selected indigenous words comprising of 15000 image samples. The experimental outcome shows excellent performance from the interpretation system, with accuracy attaining 95.67%.

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis

Motivation Single-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of condition-specific functional gene modules (FGM) can help to understand interactive gene networks and complex biological processes in different cell clusters. QUBIC2 is recognized as one of the most efficient and effective biclustering tools for condition-specific FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analysis functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify condition-specific FGMs, predict cell types/clusters, uncover differentially expressed genes, and perform pathway enrichment analysis. It is noteworthy that IRIS-FGM can also take Seurat objects as input, facilitating easy integration with the existing analysis pipeline. Availability and Implementation IRIS-FGM is implemented in the R environment (as of version 3.6) with the source code freely available at https://github.com/BMEngineeR/IRISFGM. Supplementary information Supplementary data are available at Bioinformatics online.