With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing predictio...With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.展开更多
The standardization of sentencing is an important issue in the Chinese reform of criminal justice and sentencing procedure. This study draws on the material obtained from a sentencing procedure pilot project and emplo...The standardization of sentencing is an important issue in the Chinese reform of criminal justice and sentencing procedure. This study draws on the material obtained from a sentencing procedure pilot project and employs typical experimental methodology to test the effect of implementing the independent sentencing procedure through overall and paired comparisons. The results show that independent sentencing is viable in that it can significantly improve the transparency of the sentencing process and increase the quantity and amount of sentencing information obtained and employed by judges. Although the degree of satisfaction of participants in the procedure is not as high as expected, the resultsin trials of the first instance appear to have greater legitimacy and justifiability. At the same time, the independent sentencing procedure also helps give substance to the function of the collegiate bench, and can thus be taken as an important entry point for deepening court reform. However, the findings of our study show that there was room for improvement in the role of lawyers in this experiment.展开更多
The anchoring effect is a powerful and widespread cognitive phenomenon in the decision-making field.Our quantitative analysis of a sample of 520 sentences indicates that the sentencing recommendation of the public pro...The anchoring effect is a powerful and widespread cognitive phenomenon in the decision-making field.Our quantitative analysis of a sample of 520 sentences indicates that the sentencing recommendation of the public procuratorate has a marked influence upon the court＇s sentencing judgment.That is,whether we are investigating the freedom penalty,the fine penalty or the term of probation imposed by the judge,we find that the anchoring effect does exist.In addition,a change in the court conviction or in the trial sentencing circumstances and the availability of a defense lawyer may weaken the anchoring effect of the procuratorate＇s sentencing recommendations.As a form of cognitive bias,the presence of the anchoring effect in the area of sentencing further highlights the necessity of applying analyses based on legal realism to the field of criminal justice in China;and at the institutional level,it demands that judges adopt corresponding arrangements to ensure the impartial exercise of discretion.展开更多
Although criminal law as a positive branch of law has been established at the early stage of founding the first states,scientific analysis of basic terms and institutes of criminal law began only in the 18th and the 1...Although criminal law as a positive branch of law has been established at the early stage of founding the first states,scientific analysis of basic terms and institutes of criminal law began only in the 18th and the 19th century.At the same time more natural and social sciences have been developed looking for the answers to the following questions:(1)what are the causes of crime?(2)what is the baseline of criminal responsibility?and(3)what are the measures that state should implement towards the criminal offences’perpetrators?In answering these questions series of scientific trends and understandings are developed in order to analyse forms of social reactions to crime in different ways,and this paper elaborates that.展开更多
In the published version of our article(Shaji et al.,2024),in the last paragraph of the article,Hong Kong should be corrected to Hong Kong(China)and the repetition of Spain and Ireland in the same sentence need to be ...In the published version of our article(Shaji et al.,2024),in the last paragraph of the article,Hong Kong should be corrected to Hong Kong(China)and the repetition of Spain and Ireland in the same sentence need to be deleted.The correct sentence is as below.展开更多
The maximum punishment for holding large amounts of property with unidentified sources, currently five years’ imprison-ment, is set to double if a draft amendment to the Criminal Law is adopted.
Correction to:Nano-Micro Lett.(2023)15:223 https://doi.org/10.1007/s40820-023-01189-0 In this article the author’s name“Hao-Chung Kuo”was incorrectly written as“Hao-Chung Guo”.And in the last sentence of the firs...Correction to:Nano-Micro Lett.(2023)15:223 https://doi.org/10.1007/s40820-023-01189-0 In this article the author’s name“Hao-Chung Kuo”was incorrectly written as“Hao-Chung Guo”.And in the last sentence of the first paragraph of Introduction,the text‘(20-20)’should have read‘(20-21)’.The original article has been corrected.展开更多
Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It...Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.展开更多
Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requir...Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.展开更多
In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one c...In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.展开更多
Phishing emails have experienced a rapid surge in cyber threats globally,especially following the emergence of the COVID-19 pandemic.This form of attack has led to substantial financial losses for numerous organizatio...Phishing emails have experienced a rapid surge in cyber threats globally,especially following the emergence of the COVID-19 pandemic.This form of attack has led to substantial financial losses for numerous organizations.Although variousmodels have been constructed to differentiate legitimate emails fromphishing attempts,attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes.This form of attack has led to substantial financial losses for numerous organizations.While efforts are ongoing to create phishing detection models,their current level of accuracy and speed in identifying phishing emails is less than satisfactory.Additionally,there has been a concerning rise in the frequency of phished emails recently.Consequently,there is a pressing need for more efficient and high-performing phishing detection models to mitigate the adverse impact of such fraudulent messages.In the context of this research,a comprehensive analysis is conducted on both components of an email message—namely,the email header and body.Sentence-level characteristics are extracted and leveraged in the construction of a new phishing detection model.This model utilizes K Nearest Neighbor(KNN)introducing the novel dimension of sentence-level analysis.Established datasets fromKagglewere employed to train and validate the model.The evaluation of this model’s effectiveness relies on key performance metrics including accuracy of 0.97,precision,recall,and F1-measure.展开更多
In order to recognize people＇s annoyance emotions in the working environment and evaluate emotional well- being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a...In order to recognize people＇s annoyance emotions in the working environment and evaluate emotional well- being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a Mandarin database with two thousands samples is built. In searching for annoyance-type emotion features, the prosodic feature and the voice quality feature parameters of the emotional statements are extracted first. Then an improved back propagation （BP） neural network based on the shuffled frog leaping algorithm （SFLA） is proposed to recognize the emotion. The recognition capability of the BP, radical basis function （RBF） and the SFLA neural networks are compared experimentally. The results show that the recognition ratio of the SFLA neural network is 4. 7% better than that of the BP neural network and 4. 3% better than that of the RBF neural network. The experimental results demonstrate that the random initial data trained by the SFLA can optimize the connection weights and thresholds of the neural network, speed up the convergence and improve the recognition rate.展开更多
Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summariza...Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.T2293773,72371145,and 12288201the Special Funds for Taishan Scholars Project of Shandong Province,China under Grant No.tsqn202211004National Key Research and Development Program under Grant No.2022YFC3303000.
文摘With the development and applications of the Smart Court System(SCS)in China,the reliability and accuracy of legal artificial intelligence have become focal points in recent years.Notably,criminal sentencing prediction,a significant component of the SCS,has also garnered widespread attention.According to the Chinese criminal law,actual sentencing data exhibits a saturated property due to statutory penalty ranges,but this mechanism has been ignored by most existing studies.Given this,the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks.Building on the saturated structure of our model,a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the L1 loss together with the corresponding gradient update strategy,a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge,the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure.An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.
文摘The standardization of sentencing is an important issue in the Chinese reform of criminal justice and sentencing procedure. This study draws on the material obtained from a sentencing procedure pilot project and employs typical experimental methodology to test the effect of implementing the independent sentencing procedure through overall and paired comparisons. The results show that independent sentencing is viable in that it can significantly improve the transparency of the sentencing process and increase the quantity and amount of sentencing information obtained and employed by judges. Although the degree of satisfaction of participants in the procedure is not as high as expected, the resultsin trials of the first instance appear to have greater legitimacy and justifiability. At the same time, the independent sentencing procedure also helps give substance to the function of the collegiate bench, and can thus be taken as an important entry point for deepening court reform. However, the findings of our study show that there was room for improvement in the role of lawyers in this experiment.
基金Supported by the Philosophy and Social Sciences Foundation of Shanghai(No.:2017BFX001)Shanghai’s Outstanding Young Talents Projects
文摘The anchoring effect is a powerful and widespread cognitive phenomenon in the decision-making field.Our quantitative analysis of a sample of 520 sentences indicates that the sentencing recommendation of the public procuratorate has a marked influence upon the court＇s sentencing judgment.That is,whether we are investigating the freedom penalty,the fine penalty or the term of probation imposed by the judge,we find that the anchoring effect does exist.In addition,a change in the court conviction or in the trial sentencing circumstances and the availability of a defense lawyer may weaken the anchoring effect of the procuratorate＇s sentencing recommendations.As a form of cognitive bias,the presence of the anchoring effect in the area of sentencing further highlights the necessity of applying analyses based on legal realism to the field of criminal justice in China;and at the institutional level,it demands that judges adopt corresponding arrangements to ensure the impartial exercise of discretion.
文摘Although criminal law as a positive branch of law has been established at the early stage of founding the first states,scientific analysis of basic terms and institutes of criminal law began only in the 18th and the 19th century.At the same time more natural and social sciences have been developed looking for the answers to the following questions:(1)what are the causes of crime?(2)what is the baseline of criminal responsibility?and(3)what are the measures that state should implement towards the criminal offences’perpetrators?In answering these questions series of scientific trends and understandings are developed in order to analyse forms of social reactions to crime in different ways,and this paper elaborates that.
文摘In the published version of our article(Shaji et al.,2024),in the last paragraph of the article,Hong Kong should be corrected to Hong Kong(China)and the repetition of Spain and Ireland in the same sentence need to be deleted.The correct sentence is as below.
文摘The maximum punishment for holding large amounts of property with unidentified sources, currently five years’ imprison-ment, is set to double if a draft amendment to the Criminal Law is adopted.
文摘Correction to:Nano-Micro Lett.(2023)15:223 https://doi.org/10.1007/s40820-023-01189-0 In this article the author’s name“Hao-Chung Kuo”was incorrectly written as“Hao-Chung Guo”.And in the last sentence of the first paragraph of Introduction,the text‘(20-20)’should have read‘(20-21)’.The original article has been corrected.
文摘Video description generates natural language sentences that describe the subject,verb,and objects of the targeted Video.The video description has been used to help visually impaired people to understand the content.It is also playing an essential role in devolving human-robot interaction.The dense video description is more difficult when compared with simple Video captioning because of the object’s interactions and event overlapping.Deep learning is changing the shape of computer vision(CV)technologies and natural language processing(NLP).There are hundreds of deep learning models,datasets,and evaluations that can improve the gaps in current research.This article filled this gap by evaluating some state-of-the-art approaches,especially focusing on deep learning and machine learning for video caption in a dense environment.In this article,some classic techniques concerning the existing machine learning were reviewed.And provides deep learning models,a detail of benchmark datasets with their respective domains.This paper reviews various evaluation metrics,including Bilingual EvaluationUnderstudy(BLEU),Metric for Evaluation of Translation with Explicit Ordering(METEOR),WordMover’s Distance(WMD),and Recall-Oriented Understudy for Gisting Evaluation(ROUGE)with their pros and cons.Finally,this article listed some future directions and proposed work for context enhancement using key scene extraction with object detection in a particular frame.Especially,how to improve the context of video description by analyzing key frames detection through morphological image analysis.Additionally,the paper discusses a novel approach involving sentence reconstruction and context improvement through key frame object detection,which incorporates the fusion of large languagemodels for refining results.The ultimate results arise fromenhancing the generated text of the proposedmodel by improving the predicted text and isolating objects using various keyframes.These keyframes identify dense events occurring in the video sequence.
文摘Sentence classification is the process of categorizing a sentence based on the context of the sentence.Sentence categorization requires more semantic highlights than other tasks,such as dependence parsing,which requires more syntactic elements.Most existing strategies focus on the general semantics of a conversation without involving the context of the sentence,recognizing the progress and comparing impacts.An ensemble pre-trained language model was taken up here to classify the conversation sentences from the conversation corpus.The conversational sentences are classified into four categories:information,question,directive,and commission.These classification label sequences are for analyzing the conversation progress and predicting the pecking order of the conversation.Ensemble of Bidirectional Encoder for Representation of Transformer(BERT),Robustly Optimized BERT pretraining Approach(RoBERTa),Generative Pre-Trained Transformer(GPT),DistilBERT and Generalized Autoregressive Pretraining for Language Understanding(XLNet)models are trained on conversation corpus with hyperparameters.Hyperparameter tuning approach is carried out for better performance on sentence classification.This Ensemble of Pre-trained Language Models with a Hyperparameter Tuning(EPLM-HT)system is trained on an annotated conversation dataset.The proposed approach outperformed compared to the base BERT,GPT,DistilBERT and XLNet transformer models.The proposed ensemble model with the fine-tuned parameters achieved an F1_score of 0.88.
文摘In order to convey complete meanings,there is a phenomenon in Chinese of using multiple running sentences.Xu Jingning(2023,p.66)states,“In communication,a complete expression of meaning often requires more than one clause,which is common in human languages.”Domestic research on running sentences includes discussions on defining the concept and structural features of running sentences,sentence properties,sentence pattern classifications and their criteria,as well as issues related to translating running sentences into English.This article primarily focuses on scholarly research into the English translation of running sentences in China,highlighting recent achievements and identifying existing issues in the study of running sentence translation.However,by reviewing literature on the translation of running sentences,it is found that current research in the academic community on non-core running sentences is limited.Therefore,this paper proposes relevant strategies to address this issue.
文摘Phishing emails have experienced a rapid surge in cyber threats globally,especially following the emergence of the COVID-19 pandemic.This form of attack has led to substantial financial losses for numerous organizations.Although variousmodels have been constructed to differentiate legitimate emails fromphishing attempts,attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes.This form of attack has led to substantial financial losses for numerous organizations.While efforts are ongoing to create phishing detection models,their current level of accuracy and speed in identifying phishing emails is less than satisfactory.Additionally,there has been a concerning rise in the frequency of phished emails recently.Consequently,there is a pressing need for more efficient and high-performing phishing detection models to mitigate the adverse impact of such fraudulent messages.In the context of this research,a comprehensive analysis is conducted on both components of an email message—namely,the email header and body.Sentence-level characteristics are extracted and leveraged in the construction of a new phishing detection model.This model utilizes K Nearest Neighbor(KNN)introducing the novel dimension of sentence-level analysis.Established datasets fromKagglewere employed to train and validate the model.The evaluation of this model’s effectiveness relies on key performance metrics including accuracy of 0.97,precision,recall,and F1-measure.
基金The National Natural Science Foundation of China(No.61375028,61301219)China Postdoctoral Science Foundation(No.2012M520973)the Scientific Research Funds of Nanjing Institute of Technology(No.ZKJ201202)
文摘In order to recognize people＇s annoyance emotions in the working environment and evaluate emotional well- being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a Mandarin database with two thousands samples is built. In searching for annoyance-type emotion features, the prosodic feature and the voice quality feature parameters of the emotional statements are extracted first. Then an improved back propagation （BP） neural network based on the shuffled frog leaping algorithm （SFLA） is proposed to recognize the emotion. The recognition capability of the BP, radical basis function （RBF） and the SFLA neural networks are compared experimentally. The results show that the recognition ratio of the SFLA neural network is 4. 7% better than that of the BP neural network and 4. 3% better than that of the RBF neural network. The experimental results demonstrate that the random initial data trained by the SFLA can optimize the connection weights and thresholds of the neural network, speed up the convergence and improve the recognition rate.
基金The National Natural Science Foundation of China(No.61133012)the Humanity and Social Science Foundation of the Ministry of Education(No.12YJCZH274)+1 种基金the Humanity and Social Science Foundation of Jiangxi Province(No.XW1502,TQ1503)the Science and Technology Project of Jiangxi Science and Technology Department(No.20121BBG70050,20142BBG70011)
文摘Dealing with issues such as too simple image features and word noise inference in product image sentence anmotation, a product image sentence annotation model focusing on image feature learning and key words summarization is described. Three kernel descriptors such as gradient, shape, and color are extracted, respectively. Feature late-fusion is executed in turn by the multiple kernel learning model to obtain more discriminant image features. Absolute rank and relative rank of the tag-rank model are used to boost the key words＇ weights. A new word integration algorithm named word sequence blocks building （WSBB） is designed to create N-gram word sequences. Sentences are generated according to the N-gram word sequences and predefined templates. Experimental results show that both the BLEU-1 scores and BLEU-2 scores of the sentences are superior to those of the state-of-art baselines.
