Report on a Research Experiment on the Independent Sentencing Procedure:A Sample from the Wuhu Model

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.

Sentence及其结构研究——以古典主义时期作品为例

文章以sentence为研究对象,一方面对国内外曲式学教材及相关文献展开概念比较、辨析与理论梳理,阐明其基本概念与内部结构;另一方面则以古典主义时期作品为分析实例,讨论这种特定主题(句法)的“结构范型”、并进一步对其“结构变形”的情况进行归类。

Sentencing Recommendations,Anchoring Effect and Fairness in Criminal Justice——An Empirical Study Based on a Sample of 520 Sentences in K City

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.

Correction to:Green Vertical-Cavity Surface-Emitting Lasers Based on InGaN Quantum Dots and Short

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.

Trends in Event Understanding and Caption Generation/Reconstruction in Dense Video:A Review

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.

Classification of Conversational Sentences Using an Ensemble Pre-Trained Language Model with the Fine-Tuned Parameter

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.

Review of Research on English Translation of Chinese Running Sentences

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.

Will Tougher Sentencing Close Corruption Loopholes?

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.

A Sentence Retrieval Generation Network Guided Video Captioning

Currently,the video captioning models based on an encoder-decoder mainly rely on a single video input source.The contents of video captioning are limited since few studies employed external corpus information to guide the generation of video captioning,which is not conducive to the accurate descrip-tion and understanding of video content.To address this issue,a novel video captioning method guided by a sentence retrieval generation network(ED-SRG)is proposed in this paper.First,a ResNeXt network model,an efficient convolutional network for online video understanding(ECO)model,and a long short-term memory(LSTM)network model are integrated to construct an encoder-decoder,which is utilized to extract the 2D features,3D features,and object features of video data respectively.These features are decoded to generate textual sentences that conform to video content for sentence retrieval.Then,a sentence-transformer network model is employed to retrieve different sentences in an external corpus that are semantically similar to the above textual sentences.The candidate sentences are screened out through similarity measurement.Finally,a novel GPT-2 network model is constructed based on GPT-2 network structure.The model introduces a designed random selector to randomly select predicted words with a high probability in the corpus,which is used to guide and generate textual sentences that are more in line with human natural language expressions.The proposed method in this paper is compared with several existing works by experiments.The results show that the indicators BLEU-4,CIDEr,ROUGE_L,and METEOR are improved by 3.1%,1.3%,0.3%,and 1.5%on a public dataset MSVD and 1.3%,0.5%,0.2%,1.9%on a public dataset MSR-VTT respectively.It can be seen that the proposed method in this paper can generate video captioning with richer semantics than several state-of-the-art approaches.

Next Words Prediction and Sentence Completion in Bangla Language Using GRU-Based RNN on N-Gram Language Model

We use a lot of devices in our daily life to communicate with others. In this modern world, people use email, Facebook, Twitter, and many other social network sites for exchanging information. People lose their valuable time misspelling and retyping, and some people are not happy to type large sentences because they face unnecessary words or grammatical issues. So, for this reason, word predictive systems help to exchange textual information more quickly, easier, and comfortably for all people. These systems predict the next most probable words and give users to choose of the needed word from these suggested words. Word prediction can help the writer by predicting the next word and helping complete the sentence correctly. This research aims to forecast the most suitable next word to complete a sentence for any given context. In this research, we have worked on the Bangla language. We have presented a process that can expect the next maximum probable and proper words and suggest a complete sentence using predicted words. In this research, GRU-based RNN has been used on the N-gram dataset to develop the proposed model. We collected a large dataset using multiple sources in the Bangla language and also compared it to the other approaches that have been used such as LSTM, and Naive Bayes. But this suggested approach provides excellent exactness than others. Here, the Unigram model provides 88.22%, Bi-gram model is 99.24%, Tri-gram model is 97.69%, and 4-gram and 5-gram models provide 99.43% and 99.78% on average accurateness. We think that our proposed method profound impression on Bangla search engines.

A Study of Nominal Predicate Sentences Under the Framework of Cognitive Grammar

Cognitive grammar,as a linguistic theory that attaches importance to the relationship between language and thinking,provides us with a more comprehensive way to understand the structure,semantics and cognitive processing of noun predicate sentences.Therefore,under the framework of cognitive grammar,this paper tries to analyze the semantic connection and cognitive process in noun predicate sentences from the semantic perspective and the method of example theory,and discusses the motivation of the formation of this construction,so as to provide references for in-depth analysis of the cognitive laws behind noun predicate sentences.

The Inheritance and Deviation of the Subjective Construction“You Bei X Dao(有被X到)”

Sentence“bei”(“被”字句)and“bei xx”(被xx)provide syntactic and semantic basis for the generation of the construction“You Bei X Dao(有被x到)”.They are in a continuum and have family similarity.The sentence“bei”is a typical member,while“bei xx”is an atypical member.Because the construction“You Bei X Dao”has a single function and less attention,it is at the edge of the continuum and is a marginal member.However,it has certain research value in the inheritance and innovation of“bei”and“bei xx”.

Annoyance-type speech emotion detection in working environment

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.

Product image sentence annotation based on kernel descriptors and tag-rank

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.

英语词汇记忆方法:语篇语境记忆法

在记忆单词的过程中,很多同学都遇到过这样的情况,即一个自己曾经学过或者背过的单词,放在阅读的文章中却仍然很陌生,即使套用自己背过的词义似乎也解释不通。例如下面的三个例子,没有一个复杂的单词,可是它们的意思你都能看得懂吗?

如何设计一节课

教师需要智慧。一节成功的英语课,应该是学生积极参与、主动思考的课;教师要尽量让学生在"做中学",无论是"做"什么,都应该是快乐地做;在"做中学"的前提是教师要了解学生的性格特点、学习风格,才能够有效把握课堂教学的节奏。2012年9月,我到西城教育研修学院做研修员,做的主要工作之一就是听课。从2012年9月到2013年的12月,我总计听课超过150节。在听课过程中,我做了一些笔记,后来又把这些笔记整理归纳下来。我很想有机会结合自己的教学实践、

《学前英语》中高职衔接的教学与实践

为培养适应新时期发展需求的学前英语教育专业人才,在幼师院校的英语教学中应选用和学前英语相关的职业英语教材。本文介绍了《学前英语》教材出版的背景和教材的特点,分析了《学前英语》在中高职阶段中的使用策略,以加强中高职衔接,促进学前英语教育专业学生综合素质的提高。

“艺术是践行自由的场所”——汪建伟访谈

作为艺术家,汪建伟名声在外。但当完成这次采访,我们更愿意用"活跃的思考者"来描述他。他以个人的自由意志,重构着我们"习以为常"的世界,并一次次地挑战自我。这篇访谈或许可以展现他思想世界的一角。

浅谈概念检查性问题(CCQs)的用法

本文简要介绍了概念检查性问题(CCQs)存在的目的,CCQs的定义,CCQs的设计方法,CCQs的优点和缺点,如何有效地运用CCQs。文中引用了大量实例,通俗易懂,对于英语教师正确使用CCQs具有一定的借鉴意义。

教学语法在教材编写中的功能

台湾即将拥有第二套在官方主导下出版的系列中文教材,取名《当代中文课程》。这套教材的编写正由台湾师范大学国语教学中心紧锣密鼓地进行,是由本人担任总主编、率领国语中心十八位资深教师展开的。第一册是为零起点的初学者编写的,目前正在排印中,其他几册则或已经完成编写。