Intelligent Case Assignment Method Based on the Chain of Criminal Behavior Elements

The assignment of cases means the court assigns cases to specific judges. The traditional case assignment methods, based on the facts of a case, are weak in the analysis of semantic structure of the case not considering the judges\u27 expertise. By analyzing judges\u27 trial logic, we find that the order of criminal behaviors affects the final judgement. To solve these problems, we regard intelligent case assignment as a text-matching problem, and propose an intelligent case assignment method based on the chain of criminal behavior elements. This method introduces the chain of criminal behavior elements to enhance the structured semantic analysis of the case. We build a BCTA (Bert-Cnn-Transformer-Attention) model to achieve intelligent case assignment. This model integrates a judge\u27s expertise in the judge\u27s presentation, thus recommending the most compatible judge for the case. Comparing the traditional case assignment methods, our BCTA model obtains 84% absolutely considerable improvement under P@1. In addition, comparing other classic text matching models, our BCTA model achieves an absolute considerable improvement of 4% under P@1 and 9% under Macro F1. Experiments conducted on real-world data set demonstrate the superiority of our method

On-Line Load Balancing with Task Buffer

On-line load balancing is one of the most important problems for applications with resource allocation. It aims to assign tasks to suitable machines and balance the load among all of the machines, where the tasks need to be assigned to a machine upon arrival. In practice, tasks are not always required to be assigned to machines immediately. In this paper, we propose a novel on-line load balancing model with task buffer, where the buffer can temporarily store tasks as many as possible. Three algorithms, namely LPTCP1_α, LPTCP2_α, and LPTCP3_β, are proposed based on the Longest Processing Time (LPT) algorithm and a variety of planarization algorithms. The planarization algorithms are proposed for reducing the difference among each element in a set. Experimental results show that our proposed algorithms can effectively solve the on-line load balancing problem and have good performance in large scale experiments

A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning

Sentencing prediction is an important direction of artificial intelligence applied to the judicial field. The purpose is to predict the trial sentence for the case based on the description of the case in the adjudication documents. Traditional methods mainly use neural networks exclusively, which are trained on a large amount of data to encode textual information and then directly regress or classify out the sentence. This shows that machine learning methods are effective, but are extremely dependent on the amount of data. We found that there is still external knowledge such as laws and regulations that are not used. Moreover, the prediction of sentences in these methods does not fit well with the trial process. Thus, we propose a sentence prediction method that incorporates trial logic based on abductive learning, called SPITL. The logic of the trial is reflected in two aspects: one is that the process of sentence prediction is more in line with the logic of the trial, and the other is that external knowledge, such as legal texts, is utilized in the process of sentence prediction. Specifically, we establish a legal knowledge base for the characteristics of theft cases, translating relevant laws and legal interpretations into first-order logic. At the same time, we designed the process of sentence prediction according to the trial process by dividing it into key circumstance element identification and sentence calculation. We fused the legal knowledge base as weakly supervised information into a neural network through the combination of logical inference and machine learning. Furthermore, a sentencing calculation method that is more consistent with the sentencing rules is proposed with reference to the Sentencing Guidelines. Under the condition of the same training data, the effect of this model in the experiment of responding to the legal documents of theft cases was improved compared with state-of-the-art models without domain knowledge. The results are not only more accurate as a sentencing aid in the judicial trial process, but also more explanatory

A Sentence Prediction Approach Incorporating Trial Logic Based on Abductive Learning

Sentencing prediction is an important direction of artificial intelligence applied to the judicial field. The purpose is to predict the trial sentence for the case based on the description of the case in the adjudication documents. Traditional methods mainly use neural networks exclusively, which are trained on a large amount of data to encode textual information and then directly regress or classify out the sentence. This shows that machine learning methods are effective, but are extremely dependent on the amount of data. We found that there is still external knowledge such as laws and regulations that are not used. Moreover, the prediction of sentences in these methods does not fit well with the trial process. Thus, we propose a sentence prediction method that incorporates trial logic based on abductive learning, called SPITL. The logic of the trial is reflected in two aspects: one is that the process of sentence prediction is more in line with the logic of the trial, and the other is that external knowledge, such as legal texts, is utilized in the process of sentence prediction. Specifically, we establish a legal knowledge base for the characteristics of theft cases, translating relevant laws and legal interpretations into first-order logic. At the same time, we designed the process of sentence prediction according to the trial process by dividing it into key circumstance element identification and sentence calculation. We fused the legal knowledge base as weakly supervised information into a neural network through the combination of logical inference and machine learning. Furthermore, a sentencing calculation method that is more consistent with the sentencing rules is proposed with reference to the Sentencing Guidelines. Under the condition of the same training data, the effect of this model in the experiment of responding to the legal documents of theft cases was improved compared with state-of-the-art models without domain knowledge. The results are not only more accurate as a sentencing aid in the judicial trial process, but also more explanatory

Fast prediction of hydrodynamic load of floating horizontal axis tidal turbine with variable speed control under surging motion with free surface

For floating HATT (horizontal axis tidal turbine) with variable speed control, a numerical model based on CFD (Computational fluid dynamics) method is proposed to predict the surge force. Compared with the hydrodynamic load of the HATT under the same condition but with fixed speed control, the variable speed control can effectively improve the power coefficient of the HATT. When the surge period is 1.5 s and the surge amplitude is D/8, the average power coefficient increases by 36.36%. But the load fluctuation in terms of the axial load and power coefficients are significantly larger than those with fixed speed control. Based on the hydrodynamic load decomposition model of the HATT with fixed speed rotation and surging motion and the variation law of damping coefficient, the hydrodynamic load prediction method of the HATT with variable speed rotation and surging motion is established. By comparing with the CFD results, it is evidenced that this prediction method can effectively predict the axial load and power coefficients of the HATT. The research findings can provide a reference for the rapid prediction of the hydrodynamic load of the HATT during the actual operation of the floating tidal power station

Intention-guided deep semi-supervised document clustering via metric learning

The intention expresses the user’s preference for document structure division. Intention-guided document structure division is an important task in the field of text mining. To achieve this goal, deep semi-supervised document clustering provides a promising solution to personalized document clustering. However, traditional deep semi-supervised clustering models suffer from the problem of the limited number of constraints which is insufficient for intention-guided document clustering. Moreover, documents normally have various emphases on their representations to reflect different structural opinions. In this paper, we proposed an intention-guided deep semi-supervised document clustering model, namely IGSC, to divide document structure based on a small amount of user-provided supervised information. IGSC designs a deep metric learning network to solve the above problems. The deep metric learner explores the user’s global intention and outputs an intention matrix. The intention is explored from the small amount user provided pairwise constraints and is used to guide the representation learning. Moreover, IGSC uses the intention matrix to guide the clustering process, to get the clustering results that best meet the user’s intention. This paper compares IGSC with a number of document clustering models on four real-world text datasets, namely Reu-10k, BBC, ACM, and Abstract. The results show that IGSC evidently improves the clustering performance and outperforms the best result of benchmark models with 7% on average. The comparison with other models and the visualization results can demonstrate that IGSC is effective

Enhancement of protective immunity in European eel (Anguilla anguilla) against Aeromonas hydrophila and Aeromonas sobria by a recombinant Aeromonas outer membrane protein

To develop a vaccine, which can simultaneously prevent the diseases caused by various pathogenic bacteria in fish, we try to find a conserved outer membrane protein (OMP) antigen from different bacterial pathogens. In this study, an OMP fragment of 747 bp (named as Omp-G), which was highly conserved in seven Aeromonas OMP sequences from the NCBI database, was amplified by PCR from one Aeromonas sobria strain (B10) and two Aeromonas hydrophila strains (B27 and B33) with the designed specific primers. The sequence was cloned into pGEX-2T (6 x His-tag) vector, expressed in Escherichia coli system, and then the recombinant protein (named as rOmp-G) was purified with nickel chelating affinity chromatography. The purified rOmp-G showed a good immunogenicity in rabbits and well-conserved characteristics in these three pathogens by enzyme-linked immunosorbed assay. Furthermore, the rOmp-G also showed good immunogenicity in eels (Anguilla anguilla) for eliciting significantly increased specific antibodies (P < 0.01), and providing higher protection efficiencies (P < 0.05) after the pathogens challenge. The values of the relative percent survival in eels were 70% and 50% for two A. hydrophila strain challenge, and 75% for A. sobria strain challenge. This is the first report of a potential vaccination in eels that simultaneously provide protectiveness against different Aeromonas pathogens with a conserved partial OMP

Isolation and Identification of a Novel Inducible Antibacterial Peptide from the Skin Mucus of Japanese Eel, Anguilla japonica

In this study, acetone extracts and acidic extracts were prepared from skin mucus, gill, kidney, liver and spleen of the Japanese eel, Anguilla japonica, and they exhibited different levels of antibacterial activities against three strains of Gram-negative bacteria, Edwardsiella tarda, Aeromonas hydrophila, Aeromonas sp. and one Gram-positive bacterium Micrococcus leteus. The mucus was chosen as the source of antibacterial peptide for further purification of antibacterial peptides. Following the intraperitoneal injection of A. hydrophila, one of the main pathogenic bacteria of Japanese eel and many other fish, a peptide was purified from acetic acid extraction of the skin mucus, by using cationic exchange liquid chromatography and reverse-phase high-performance liquid chromatography (RP-HPLC). The isolated antibacterial peptide, named as AJN-10, exhibited antibacterial activity against A. hydrophila. The AJN-10 is a heat-tolerant and hydrophilic peptide. The molecular weight of this peptide is 6,044.28 Da, as determined by matrix-assisted laser desorption ionisation time of flight mass spectrometry. The 20 N-terminal amino acid sequences were clarified by Edman degradation, and based on results of homology search by BLAST analysis of the 20 N-terminal sequences, the AJN-10 showed little similarity to other proteins in databases