Legal Judgment Elements Extraction Approach with Law Article-aware Mechanism

Social media platform-oriented topic mining and information security analysis by big data and deep convolutional neural network

Legal judgments are generally very long, and relevant information is often scattered throughout the text. To complete a legal judgment summarization, capturing important, relevant information comprehensively from a lengthy text is crucial. The existing abstractive-summarization models based on pre-trained language have restrictions on the length of an input text. Another concern is that the generated summaries have not been well integrated with the legal judgment’s technical terms and specific topics. In this paper, we used raw legal judgments as information of different granularities and proposed a two-stage text-summarization model to handle different granularities of information. Specifically, we treated the legal judgments as a sequence of sentences and selected key sentence sets from the full texts as an input corpus for summary generation. In addition, we extracted keywords related to technical terms and specific topics in the legal texts and introduced them into the summary-generation model as an attention mechanism. The experimental results on the CAIL2020 and the LCRD datasets showed that our model achieved an overall 0.19–0.41 improvement in its ROUGE score, as compared to the baseline models. Further analysis also showed that our method could comprehensively capture essential and relevant information from lengthy legal texts and generate better legal judgment summaries.

Basis is also explanation: Interpretable Legal Judgment Reasoning prompted by multi-source knowledge

Legal judgment prediction (LJP) and decision support aim to enable machines to predict the verdict of legal cases after reading the description of facts, which is an application of artificial intelligence in the legal field. This paper proposes a legal judgment prediction model based on process supervision for the sequential dependence of each subtask in the legal judgment prediction task. Experimental results verify the effectiveness of the model framework and process monitoring mechanism adopted in this model. First, the convolutional neural network (CNN) algorithm was used to extract text features, and the principal component analysis (PCA) algorithm was used to reduce the dimension of data features. Next, the prediction model based on process supervision is proposed for the first time. When modeling the dependency relationship between sequential sub-data sets, process supervision is introduced to ensure the accuracy of the obtained dependency information, and genetic algorithm (GA) is introduced to optimize the parameters so as to improve the final prediction performance. Compared to our benchmark method, our algorithm achieved the best results on four different legal open data sets (CAIL2018_Small, CAIL2018_Large, CAIL2019_Small, and CAIL2019_Large). The realization of automatic prediction of legal judgment can not only assist judges, lawyers, and other professionals to make more efficient legal judgment but also provide legal aid for people who lack legal expertise.

Legal Judgment Prediction (LJP) is a key technique for social fair. It aims to predict the judicial decisions automatically given the fact description and has great prospects in judicial assistance and management. This article focuses on the prediction of criminal judgment and proposes a legal domain-oriented method for the LJP task, by exploiting the dependencies of labels across tasks of LJP. The proposed method captures the dependencies by a prediction forward-propagate mechanism over a directed heterogeneous graph, and a novel prediction task, attribute prediction. The experiments prove the efficiency of the method and show the superior of our model on real-world datasets.

In order to integrate the concept of intangible cultural heritage (ICH) protection into the construction of smart cities, realize the organic integration of smart cities and cultural heritage, and improve the cultural experience of urban residents and tourists, this study explores an interactive design scheme of smart cities application interface applied to ICH protection to meet the needs of protection and inheritance. Firstly, the ICH of Chongqing is sorted out and classified. The ICH-related APP interfaces in the market are analyzed through investigation. Secondly, an image recognition algorithm of ICH based on deep learning (DL) technology is proposed and applied in APP to realize automatic recognition and introduction of ICH. Finally, a set of APP interface interaction design schemes is designed based on user habits and visual feelings to enhance user experience. The experimental results reveal: (1) The model for recognizing ICH images using the convolutional neural network (CNN) has higher recognition accuracy, recall, and F1 value than the model without CNNs; (2) After incorporating transfer learning (TL) into the model, the recognition accuracy, recall, and F1 value of the model have further improved; (3) The survey results show that the Chongqing ICH APP interface system based on DL technology, user habits, and visual perception performs better in terms of user experience, usability, and other aspects. This study aims to design an APP interface system for the Chongqing ICH based on DL technology, user habits, and visual perception, to improve user experience and usability. Future research directions can further optimize image recognition algorithms to improve ICH's recognition accuracy and efficiency. Meanwhile, new technologies, such as virtual reality, are combined to enhance users' interactive experience and immersion.

Aiming at the problems of long sharing time, low accuracy, recall, and F1 value in the traditional data sharing method of college dance teaching resource database, a data sharing method of college dance teaching resource database based on PSO algorithm is proposed. Multiple regression KNN method is used to eliminate the data noise of college dance teaching resource database, so as to obtain the missing value and complete the filling of incomplete data of college dance teaching resource database. Taking the preprocessed data as the basic element of transmission object statistics and analysis, establish the data transmission self-service channel of college dance teaching resource database, calculate the similarity of the data according to the unequal length sequence, and use the partial least square method to complete the feature extraction of the resource database data. According to the feature extraction results, particle swarm optimization algorithm is adopted to share the data of college dance teaching resource database. The simulation results show that the accuracy, recall, and F1 value of the data sharing method of college dance teaching resource database based on PSO algorithm are high, and the sharing time is short.

BackgroundClinical practices have demonstrated that disease treatment can be very complex. Patients with chronic diseases often suffer from more than one disease. Complex diseases are often treated with a variety of drugs, including both primary and auxiliary treatments. This complexity and multidimensionality increase the difficulty of extracting knowledge from clinical data.MethodsIn this study, we proposed a subgroup identification algorithm for complex prescriptions (SIAP). We applied the SIAP algorithm to identify the importance level of each drug in complex prescriptions. The algorithm quickly classified and determined valid prescription combinations for patients. The algorithm was validated through classification matching of classical prescriptions in traditional Chinese medicine. We collected 376 formulas and their compositions from a formulary to construct a database of standard prescriptions. We also collected 1438 herbal prescriptions from clinical data for automated prescription identification. The prescriptions were divided into training and test sets. Finally, the parameters of the two sub-algorithms of SIAP and SIAP-All, as well as those of the combination algorithm SIAP + All, were optimized on the training set. A comparison analysis was performed against the baseline intersection set rate (ISR) algorithm. The algorithm for this study was implemented with Python 3.6.ResultsThe SIAP-All and SIAP + All algorithms outperformed the benchmark ISR algorithm in terms of accuracy, recall, and F1 value. The F1 values were 0.7568 for SIAP-All and 0.7799 for SIAP + All, showing improvements of 8.73% and 11.04% over the existing ISR algorithm, respectively.ConclusionWe developed an algorithm, SIAP, to automatically match sub-prescriptions of complex drugs with corresponding standard or classic prescriptions. The matching algorithm weights the drugs in the prescription according to their importance level. The results of this study can help to classify and analyse the drug compositions of complex prescriptions.

Data on the gastrointestinal absorption of 12 elements have been reviewed. In each case, absorption is expressed as the fraction of the ingested element absorbed to blood, referred to as the f1 value, applying to intakes of unspecified chemical form by average population groups. The level of confidence in individual absorption values has been estimated in terms of lower and upper bounds, A and B, such that there is judged to be roughly a 90% probability that the true central value is no less than A and no greater than B. Ranges are proposed for intakes by adults, 10-year-old children and 3-month-old infants. Uncertainty in f1 values (B/A) ranged from 10% to factors of 100-400. The lowest uncertainties were for the well absorbed elements, H, I and Cs, for which there are good data, and the greatest uncertainties were for less well absorbed elements for which few data are available, particularly Zr and Sb. Ranges were generally wider for children and infants than for adults because of the need to allow for the likelihood of increased absorption with only limited data in support of the proposed values. The largest ranges were for 3-month-old infants, reflective lack of knowledge on the time-course and magnitude of possible increased absorption in the first few months of life. For each age group, ICRP values of absorption tend towards the upper bound of the ranges, indicating a degree of conservatism in th calculation of ingestion dose coefficients. Examination of the effect of the proposed confidence intervals for f1 values on uncertainties in dose coefficients for ingested radionuclides showed that there was no direct relationship. For some radionuclides, uncertainties in effective dose were small despite large uncertainties in f1 values while for others the uncertainties in effective doses approached the corresponding values for uncertainty in f1 values. These differences reflect the relative contributions to effective dose from cumulative activity in the contents of the alimentary tract, which in many cases is insensitive to uncertainties in f1, and cumulative activity of the absorbed radionuclide in systemic tissues, which is proportional to f1. In general, uncertainties in effective close for children and infants exceeded those in adults as a result of greater uncertainties in f1 values for the younger age groups. However, this effect was reduced in some cases by shorter retention times of absorbed nuclides in body tissues and organs.

Government agencies are required to mobilize every aspect of publication which is carried out every year which must be accounted for and also carried out for each device that receives it such as assisted villages by utilizing available apbd funds in maximizing work programs designed so that they can be implemented optimally and effectively. by getting the best from all aspects of the work program implementation, of course there are important points in designing an annual work program without exception. data mining itself can help the department of population, family planning, women's empowerment and child protection in analyzing each work program design from before it is implemented onwards to look at various aspects of past data whose grouping is in the form of classification. The purpose of this study is to build a classification model with the addition of a sigmoid activation function that uses svm and perceptron to obtain a comparison value for the accuracy of the algorithm used to obtain the best working program design. The classification results are used to get the best value for classifying the best P2KBP3A work program dataset where it can be seen that the average accuracy value is 87.5%, the f1 value is 82.2%, the precision value is 80.2%, and the recall value is 87.5% so that the final result of the research results obtained a good accuracy value.

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the facts of a case and becomes a promising application of artificial intelligence techniques in the legal field. In real-world scenarios, legal judgment usually consists of multiple subtasks, such as the decisions of applicable law articles, charges, fines, and the term of penalty. Moreover, there exist topological dependencies among these subtasks. While most existing works only focus on a specific subtask of judgment prediction and ignore the dependencies among subtasks, we formalize the dependencies among subtasks as a Directed Acyclic Graph (DAG) and propose a topological multi-task learning framework, TopJudge, which incorporates multiple subtasks and DAG dependencies into judgment prediction. We conduct experiments on several real-world large-scale datasets of criminal cases in the civil law system. Experimental results show that our model achieves consistent and significant improvements over baselines on all judgment prediction tasks. The source code can be obtained from https://github.com/thunlp/TopJudge.

Legal Judgment Prediction (LJP) study is experiencing a growing need for automating legal judgment process to predict court decisions. In this context, the present paper provides a systematic literature review of previous LJP study, implementing machine learning (ML) as decision-making and natural language processing (NLP) to extract information from legal judgment documents. Relevant articles were found in reputable indexing databases through the search strategy, with the outcomes filtered by applying inclusion and exclusion criteria. Furthermore, six research questions were constructed to observe the datasets, topics/trends, NLP and ML methods, evaluation methods, and challenges. The LJP topic included three topics which were charge, law article, and term-of-penalty prediction. There were 21 NLP methods applied, emphasizing the highest implementation of Term Frequency-Inverse Document Frequency (TF-IDF) while the most implemented ML method was Support Vector Machine (SVM). Accuracy was the most used metric as an evaluation method. Additionally, this work emphasizes the importance of LJP and the potential use of NLP and ML. This study urges further investigation into NLP and ML, as well as practical uses of LJP. Low classification performance, low quantity of data, imbalanced dataset, data accessibility, data labeling, extraction of semantic information from natural language, expert involvement, generalizability issue, and multilingual datasets represent a few of the major problems that LJP faces, and the study is significant because it clarifies some of the major issues that LJP faces. Among those problems, low amounts of dataset and low classification performance were regarded as the most challenging tasks to deal with.

The approach of visitor sentiment analysis to Borobudur Temple tourist destinations in Indonesia can be classified using various algorithms to get optimal results. Good algorithm performance can be seen from the confusion matrix (accuracy, precision, recall) value, Area Under Curve (AUC) value, and Receiver Operating Characteristic (ROC). This study used the Naïve Bayes Classifier (NBC), Decision Tree (DT), and Support Vector Machine (SVM) algorithms against 3850 text data obtained from the Tripadvisor website, especially reviews of Borobudur Temple visitors. The method refers to the Cross-Industry Standard Process for Data Mining (CRISP-DM) for optimizing tourist destination products and services by paying attention to six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The results of this study show that the results of NBC's algorithm performance evaluation can be seen to have a change in the confusion matrix value at the accuracy value from 98.73% to 95.6%, the precision value changed from 98.72% to 98.97%, the recall value also changed from 100% to 96.54%. In addition, the Area Under Curve (AUC) of NBC also changed from 0.500 (50%) to 0.693 (69.35%). In addition, the results of the DT algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 97.55% to 94.40%, the precision value increased from 97.63% to 91.86%, the recall value also changed from 99.90% to 99.47%. The Area Under Curve (AUC) of DT value also changed from 0.591 (59.1%) to 0.932 (93.2%). The results of the SVM algorithm performance evaluation showed a change in the confusion matrix value at the accuracy value from 98.73% to 99.41%; the precision value changed from 98.72% to 100%, and the recall value also changed from 100% to 99.01%. The Area Under Curve (AUC) of the SVM value also changed from 0.961 (96.1%) to 1.00 (100%). In addition, the T-test results show that the SVM algorithm is more dominant compared to other algorithms, where the SVM algorithm T-test value is 0.994 compared to the DT algorithm T-test value of 0.944 and the NBC algorithm T-test value of 0.98. Based on the Receiver Operating Characteristic (ROC) value, it can be seen that the DT algorithm also shows good performance in addition to SVM. It indicates that in analyzing the sentiment of visitors to Borobudur Temple, the best-recommended algorithm is the Support Vector Machine

To solve the limitations of the current entrepreneurial ecosystem, the research on the digital entrepreneurial ecosystem is more meaningful. This article aims to study the dynamic evolution mechanism of the digital entrepreneurship ecosystem based on text sentiment computing analysis. It proposes an improved Bi-directional long short-term memory (Bi-LSTM) model, which uses a multilayer neural network to deal with classification problems. It has a higher accuracy rate, recall rate, and F1 value than the traditional LSTM model and can better perform sentiment analysis on text. The algorithm uses the optimized Naive Bayes algorithm, which is based on Euclidean distance weighting and can assign different weights to the final classification results according to different attributes. Compared with the general Bayes algorithm, it improves the calculation efficiency and can better match the digital entrepreneurial ecosystem, which is evolving dynamically, predicting and analyzing its future development. The experimental results in this article show that the improved Bi-LSTM is better than the traditional Bi-LSTM model in terms of accuracy and F1 value. The accuracy rate is increased by 1.1%, the F1 value is increased by 0.6%, and the recall rate is only <0.2%. Running on the Spark platform, although 3% accuracy is sacrificed, the running time is increased by 320%. Compared with the traditional cellular neural network (CNN) algorithm, the accuracy rate is increased by 4%, the recall rate is increased by 14%, and the F1 value is increased by 9%, which proves that it has a strong non-linear fitting ability. The performance improvement brought by the huge data set is very huge, which fully proves the feasibility of the digital entrepreneurship ecosystem.

The rock mass is one of the key parameters in engineering design. Accurate rock mass classification is also essential to ensure operational safety. Over the past decades, various models have been proposed to evaluate and predict rock mass. Among these models, artificial intelligence (AI) based models are becoming more popular due to their outstanding prediction results and generalization ability for multiinfluential factors. In order to develop an easy-to-use rock mass classification model, support vector machine (SVM) techniques are adopted as the basic prediction tools, and three types of optimization algorithms, i.e., particle swarm optimization (PSO), genetic algorithm (GA) and grey wolf optimization (GWO), are implemented to improve the prediction classification and optimize the hyper-parameters. A database was assembled, consisting of 80 sets of real engineering data, involving four influencing factors. The three combined models are compared in accuracy, precision, recall, F1 value and computational time. The results reveal that among three models, the GWO-SVC-based model shows the best classification performance by training. The accuracy of training and testing sets of GWO-SVC are 90.6250% (58/64) and 93.7500% (15/16), respectively. For Grades I, II, III, IV and V, the precision value is 1, 0.93, 0.90, 0.92, 0.83, the recall value is 1, 1, 0.93, 0.73, 0.83, and the F1 value is 1, 0.96, 0.92, 0.81, 0.83, respectively. Sensitivity analysis is performed to understand the influence of input parameters on rock mass classification. It shows that the sensitive factor in rock mass quality is the RQD. Finally, the GWO-SVC is employed to assess the quality of rocks from the southeastern ore body of the Chambishi copper mine. Overall, the current study demonstrates the potential of using artificial intelligence methods in rock mass assessment, rendering far better results than the previous reports.