The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads.For this purpose,driving data were gathered through a driving simulator ex...The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads.For this purpose,driving data were gathered through a driving simulator experiment with 37 young drivers.Additionally,a survey was conducted to collect their demographic characteristics and driving behavior preferences.During the experiment,the drivers were distracted using contemporary smartphone internet applications i.e.,Facebook Messenger,Facebook and Google Maps.Regression analysis models were developed in order to identify and investigate the effect of distraction on accident probability,speed deviation,headway distance,as well as lateral distance deviation.Additionally,random forest(RF),a machine learning classification algorithm,was deployed for real-time distraction prediction.It was revealed that distraction due to web surfing and texting leads to a statistically significant increase in accident probability,headway distance and lateral distance deviation by 32%,27%and 6%,respectively.Moreover,the driving speed deviation was reduced by 47%during distraction.Apart from the real-time prediction,the RF revealed that headway distance,lateral distance,and traffic volume were important features.The RF outcomes revealed consistency with regression analysis and drivers during the distractive task are more defensive by driving at the edge of the road near the hard shoulder and maintaining longer headways.Overall,driving behavior and safety among young drivers were both significantly affected by the investigated internet applications.展开更多
Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information ...Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need.展开更多
Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive te...Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).展开更多
A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can ...A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.展开更多
Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to signi...Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.展开更多
We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered tex...We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.展开更多
Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from l...Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.展开更多
The application of legal texts in the context of digital television is a process that relies on several normative instruments,ranging from international treaties,such as those of the ITU(International Telecommunicatio...The application of legal texts in the context of digital television is a process that relies on several normative instruments,ranging from international treaties,such as those of the ITU(International Telecommunications Union),to national regulations defining the obligations of audiovisual operators and the modalities of consumer support.Many countries have introduced specific laws and regulations to organize the gradual switch-off of analog broadcasting and encourage the adoption of new digital standards.Consequently,the digitization of Guinea’s broadcasting network cannot be carried out without taking into account the legal framework:allocation of resources and broadcasting players.Analog and digital broadcasting,according to regulatory texts,shows the relationships between the different communication management structures.As for digital broadcasting,we note the appearance of a new service,multiplex.展开更多
Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper propo...Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper proposes a new GSPT-CVAE model(Graph Structured Processing,Single Vector,and Potential Attention Com-puting Transformer-Based Conditioned Variational Autoencoder model).The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text,and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation.In the process of potential representation guiding text generation,the model adopts a combination of traditional embedding and potential attention calculation to give full play to the guiding role of potential representation for generating text,to improve the controllability and effectiveness of text generation.The experimental results show that the model has excellent representation learning ability and can learn rich and useful textual relationship representations.The model also achieves satisfactory results in the effectiveness and controllability of text generation and can generate long texts that match the given constraints.The ROUGE-1 F1 score of this model is 0.243,the ROUGE-2 F1 score is 0.041,the ROUGE-L F1 score is 0.22,and the PPL-Word score is 34.303,which gives the GSPT-CVAE model a certain advantage over the baseline model.Meanwhile,this paper compares this model with the state-of-the-art generative models T5,GPT-4,Llama2,and so on,and the experimental results show that the GSPT-CVAE model has a certain competitiveness.展开更多
We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of t...We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.展开更多
This paper will explore the common origins and developments of Judaism,Christianity,and Islam,known as the Abrahamic faiths.Drawing on the references of F.E.Peters and Huston Smith,this paper examines how these tradit...This paper will explore the common origins and developments of Judaism,Christianity,and Islam,known as the Abrahamic faiths.Drawing on the references of F.E.Peters and Huston Smith,this paper examines how these traditions are unified by monotheism,reverence for sacred scripture,and ethical principles,yet dives in their historical narratives,interpretations of covenant,and worship practices.Spiritual figures such as Abraham,Moses,Jesus,and Muhammad are analyzed for their roles in shaping theology and guiding communities of faith.The study highlights the Torah,Bible,and Qur’an as sources of authority and identity,while comparing moral teachings and ritual expressions across the traditions.An emphasis is placed on the shared values and theological differences that have shaped both dialogue and conflict.Ultimately,the paper demonstrates how understanding these faiths together deepens insight into their enduring influence on culture,spirituality,and human history.展开更多
The Han-shan and Shi-de story,which spread to Japan around the 11th century,has given rise to many literary works in later times.The Japanese picture book Kanzan and Jittoku by Nagamatsu Yōko and Komai Keiko is a goo...The Han-shan and Shi-de story,which spread to Japan around the 11th century,has given rise to many literary works in later times.The Japanese picture book Kanzan and Jittoku by Nagamatsu Yōko and Komai Keiko is a good example.However,the picture book,which serves as a window on the cultural resonance of the Han-shan and Shi-de story,has not received enough attention from researchers,compared with other forms of rewriting such as poetry,drama,or short stories.This article investigates the representation of Han-shan and Shi-de in the picture book,and examines how the authors have incorporated the preceding texts as raw material for their own.It is found that the transformation of Han-shan and Shi-de in the picture book stems from the authors’selective use of the preceding texts and their unique interpretation.LüQiuyin’s preface,Hakuin’s comments on Han-shan,as well as the authors’knowledge and experience are vital in shaping the characters.展开更多
Manuscript Text and figures combined into a single file with page and line numbers up to3 MB in size in txt,doc,docx or tex files.Prepares your manuscript in the following order:Title page,Abstract,Introduction,Materi...Manuscript Text and figures combined into a single file with page and line numbers up to3 MB in size in txt,doc,docx or tex files.Prepares your manuscript in the following order:Title page,Abstract,Introduction,Materials and Methods,Results,Discussion,Acknowledgments,References,Tables,Figure Legends and Figures.Provide Cover letter and Supplementary Material(if necessary)at the same time.展开更多
Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured thro...Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.展开更多
As blockchain technology advances,non-fungible tokens(NFTs)are emerging as unconventional assets in the commercial market.However,it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailin...As blockchain technology advances,non-fungible tokens(NFTs)are emerging as unconventional assets in the commercial market.However,it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns.This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter,Weibo,and Reddit.Employing text clustering and topic modeling techniques,such as Latent Dirichlet Allocation,we constructed an analytical framework to delve into the intricacies of the NFT ecosystem.Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data.Weibo users predominantly engaged in reviews and critiques,whereas Twitter and Reddit users emphasized personal experiences and perceptions.The NFT ecosystem encompasses several crucial elements,including transactions,customers,infrastructure,products,environments,and perceptions.By identifying the prevailing trends and common issues,this study offers valuable guidance for the development of NFT ecosystems.展开更多
This study systematically investigates adverb translation in political texts through quantitative and qualitative analysis under the theoretical framework of“political equivalence,”utilizing the Government Work Repo...This study systematically investigates adverb translation in political texts through quantitative and qualitative analysis under the theoretical framework of“political equivalence,”utilizing the Government Work Report(2001-2024)as its database.The research aims to reveal the evolutionary trajectory of translation strategies across different historical periods and their underlying socio-cultural motivations.Findings demonstrate a notable shift in adverb translation strategies within political texts:transitioning from faithful reproduction to adaptive transformation.Throughout this progression,translations maintain discursive coherence while increasingly conforming to English idiomatic conventions,ultimately achieving dynamic equilibrium between accurate conveyance of political connotations and compliance with target-language norms.展开更多
文摘The present study aims to investigate the impact of texting and web surfing on the driving behavior and safety of young drivers on rural roads.For this purpose,driving data were gathered through a driving simulator experiment with 37 young drivers.Additionally,a survey was conducted to collect their demographic characteristics and driving behavior preferences.During the experiment,the drivers were distracted using contemporary smartphone internet applications i.e.,Facebook Messenger,Facebook and Google Maps.Regression analysis models were developed in order to identify and investigate the effect of distraction on accident probability,speed deviation,headway distance,as well as lateral distance deviation.Additionally,random forest(RF),a machine learning classification algorithm,was deployed for real-time distraction prediction.It was revealed that distraction due to web surfing and texting leads to a statistically significant increase in accident probability,headway distance and lateral distance deviation by 32%,27%and 6%,respectively.Moreover,the driving speed deviation was reduced by 47%during distraction.Apart from the real-time prediction,the RF revealed that headway distance,lateral distance,and traffic volume were important features.The RF outcomes revealed consistency with regression analysis and drivers during the distractive task are more defensive by driving at the edge of the road near the hard shoulder and maintaining longer headways.Overall,driving behavior and safety among young drivers were both significantly affected by the investigated internet applications.
基金supported in part by the National Key Research and Development Program of China under Grant No.2024YFE0200600the Zhejiang Provincial Natural Science Foundation of China under Grant No.LR23F010005the Huawei Cooperation Project under Grant No.TC20240829036。
文摘Along with the proliferating research interest in semantic communication(Sem Com),joint source channel coding(JSCC)has dominated the attention due to the widely assumed existence in efficiently delivering information semantics.Nevertheless,this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding(SSCC)to enjoy a more underlying degree of freedom for optimization.We demonstrate that SSCC,after leveraging the strengths of the Large Language Model(LLM)for source coding and Error Correction Code Transformer(ECCT)complemented for channel coding,offers superior performance over JSCC.Our proposed framework also effectively highlights the compatibility challenges between Sem Com approaches and digital communication systems,particularly concerning the resource costs associated with the transmission of high-precision floating point numbers.Through comprehensive evaluations,we establish that assisted by LLM-based compression and ECCT-enhanced error correction,SSCC remains a viable and effective solution for modern communication systems.In other words,separate source channel coding is still what we need.
文摘Large language models(LLMs),such as ChatGPT developed by OpenAI,represent a significant advancement in artificial intelligence(AI),designed to understand,generate,and interpret human language by analyzing extensive text data.Their potential integration into clinical settings offers a promising avenue that could transform clinical diagnosis and decision-making processes in the future(Thirunavukarasu et al.,2023).This article aims to provide an in-depth analysis of LLMs’current and potential impact on clinical practices.Their ability to generate differential diagnosis lists underscores their potential as invaluable tools in medical practice and education(Hirosawa et al.,2023;Koga et al.,2023).
文摘A two-stage algorithm based on deep learning for the detection and recognition of can bottom spray codes and numbers is proposed to address the problems of small character areas and fast production line speeds in can bottom spray code number recognition.In the coding number detection stage,Differentiable Binarization Network is used as the backbone network,combined with the Attention and Dilation Convolutions Path Aggregation Network feature fusion structure to enhance the model detection effect.In terms of text recognition,using the Scene Visual Text Recognition coding number recognition network for end-to-end training can alleviate the problem of coding recognition errors caused by image color distortion due to variations in lighting and background noise.In addition,model pruning and quantization are used to reduce the number ofmodel parameters to meet deployment requirements in resource-constrained environments.A comparative experiment was conducted using the dataset of tank bottom spray code numbers collected on-site,and a transfer experiment was conducted using the dataset of packaging box production date.The experimental results show that the algorithm proposed in this study can effectively locate the coding of cans at different positions on the roller conveyor,and can accurately identify the coding numbers at high production line speeds.The Hmean value of the coding number detection is 97.32%,and the accuracy of the coding number recognition is 98.21%.This verifies that the algorithm proposed in this paper has high accuracy in coding number detection and recognition.
基金financed by the European Union-NextGenerationEU,through the National Recowery and Resilience Plan of the Republic of Bulgaria,Project No.BG-RRP-2.013-0001-C01.
文摘Social media has emerged as one of the most transformative developments on the internet,revolu-tionizing the way people communicate and interact.However,alongside its benefits,social media has also given rise to significant challenges,one of the most pressing being cyberbullying.This issue has become a major concern in modern society,particularly due to its profound negative impacts on the mental health and well-being of its victims.In the Arab world,where social media usage is exceptionblly high,cyberbullying has become increasingly prevalent,necessitating urgent attention.Early detection of harmful online behavior is critical to fostering safer digital environments and mitigating the adverse efcts of cyberbullying.This underscores the importance of developing advanced tools and systems to identify and address such behavior efectively.This paper investigates the development of a robust cyberbullying detection and classifcation system tailored for Arabic comments on YouTube.The study explores the efectiveness of various deep learning models,including Bi-LSTM(Bidirectional Long Short Term Memory),LSTM(Long Short-Term Memory),CNN(Convolutional Neural Networks),and a hybrid CNN-LSTM,in classifying Arabic comments into binary classes(bullying or not)and multiclass categories.A comprehensive dataset of 20,000 Arabic YouTube comments was collected,preprocessed,and labeled to support these tasks.The results revealed that the CNN and hybrid CNN-LSTM models achieved the highest accuracy in binary classification,reaching an impressive 91.9%.For multiclass dlassification,the LSTM and Bi-LSTM models outperformed others,achieving an accuracy of 89.5%.These findings highlight the efctiveness of deep learning approaches in the mitigation of cyberbullying within Arabic online communities.
基金work conducted under COST Action CA21125-a European forum for revitalisation of marginalised moun-tain areas(MARGISTAR)supported by COST(European Cooperation in Science and Technology)gratefully acknowledges the support received for the research from the University of Ljubljana’s research program Forest,forestry and renewable forest resources(P4-0059).
文摘We demonstrate a multi-method approach towards discovering and structuring sustainability transition knowl edge in marginalized mountain regions.By employing reflective thinking,artificial intelligence(AI)-powered text summarization and text mining,we synthesize experts’narratives on sustainable development challenges and solutions in Kardüz Upland,Türkiye.We then analyze their alignment with the UN Sustainable Development Goals(SDGs)using document embedding.Investment in infrastructure,education,and resilient socio-ecological systems emerged as priority sectors to combat poor infrastructure,geographic isolation,climate change,poverty,depopulation,unemployment,low education levels,and inadequate social services.The narratives were closest in substance to SDG 1,3,and 11.Social dimensions of sustainability were more pronounced than environmental dimensions.The presented approach supports policymakers in organizing loosely structured sustainability tran sition knowledge and fragmented data corpora,while also advancing AI applications for designing and planning sustainable development policies at the regional level.
基金the Special Project of the Shanghai Municipal Commission of Economy and Information Technology for Promoting High-Quality Industrial Development(No.2024-GZL-RGZN-02011)the Shanghai City Digital Transformation Project(No.202301002)the Project of Shanghai Shenkang Hospital Development Center(No.SHDC22023214)。
文摘Surgical site infections(SSIs)are the most common healthcare-related infections in patients with lung cancer.Constructing a lung cancer SSI risk prediction model requires the extraction of relevant risk factors from lung cancer case texts,which involves two types of text structuring tasks:attribute discrimination and attribute extraction.This article proposes a joint model,Multi-BGLC,around these two types of tasks,using bidirectional encoder representations from transformers(BERT)as the encoder and fine-tuning the decoder composed of graph convolutional neural network(GCNN)+long short-term memory(LSTM)+conditional random field(CRF)based on cancer case data.The GCNN is used for attribute discrimination,whereas the LSTM and CRF are used for attribute extraction.The experiment verified the effectiveness and accuracy of the model compared with other baseline models.
文摘The application of legal texts in the context of digital television is a process that relies on several normative instruments,ranging from international treaties,such as those of the ITU(International Telecommunications Union),to national regulations defining the obligations of audiovisual operators and the modalities of consumer support.Many countries have introduced specific laws and regulations to organize the gradual switch-off of analog broadcasting and encourage the adoption of new digital standards.Consequently,the digitization of Guinea’s broadcasting network cannot be carried out without taking into account the legal framework:allocation of resources and broadcasting players.Analog and digital broadcasting,according to regulatory texts,shows the relationships between the different communication management structures.As for digital broadcasting,we note the appearance of a new service,multiplex.
文摘Aiming at the problems of incomplete characterization of text relations,poor guidance of potential representations,and low quality of model generation in the field of controllable long text generation,this paper proposes a new GSPT-CVAE model(Graph Structured Processing,Single Vector,and Potential Attention Com-puting Transformer-Based Conditioned Variational Autoencoder model).The model obtains a more comprehensive representation of textual relations by graph-structured processing of the input text,and at the same time obtains a single vector representation by weighted merging of the vector sequences after graph-structured processing to get an effective potential representation.In the process of potential representation guiding text generation,the model adopts a combination of traditional embedding and potential attention calculation to give full play to the guiding role of potential representation for generating text,to improve the controllability and effectiveness of text generation.The experimental results show that the model has excellent representation learning ability and can learn rich and useful textual relationship representations.The model also achieves satisfactory results in the effectiveness and controllability of text generation and can generate long texts that match the given constraints.The ROUGE-1 F1 score of this model is 0.243,the ROUGE-2 F1 score is 0.041,the ROUGE-L F1 score is 0.22,and the PPL-Word score is 34.303,which gives the GSPT-CVAE model a certain advantage over the baseline model.Meanwhile,this paper compares this model with the state-of-the-art generative models T5,GPT-4,Llama2,and so on,and the experimental results show that the GSPT-CVAE model has a certain competitiveness.
文摘We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains.
文摘This paper will explore the common origins and developments of Judaism,Christianity,and Islam,known as the Abrahamic faiths.Drawing on the references of F.E.Peters and Huston Smith,this paper examines how these traditions are unified by monotheism,reverence for sacred scripture,and ethical principles,yet dives in their historical narratives,interpretations of covenant,and worship practices.Spiritual figures such as Abraham,Moses,Jesus,and Muhammad are analyzed for their roles in shaping theology and guiding communities of faith.The study highlights the Torah,Bible,and Qur’an as sources of authority and identity,while comparing moral teachings and ritual expressions across the traditions.An emphasis is placed on the shared values and theological differences that have shaped both dialogue and conflict.Ultimately,the paper demonstrates how understanding these faiths together deepens insight into their enduring influence on culture,spirituality,and human history.
文摘The Han-shan and Shi-de story,which spread to Japan around the 11th century,has given rise to many literary works in later times.The Japanese picture book Kanzan and Jittoku by Nagamatsu Yōko and Komai Keiko is a good example.However,the picture book,which serves as a window on the cultural resonance of the Han-shan and Shi-de story,has not received enough attention from researchers,compared with other forms of rewriting such as poetry,drama,or short stories.This article investigates the representation of Han-shan and Shi-de in the picture book,and examines how the authors have incorporated the preceding texts as raw material for their own.It is found that the transformation of Han-shan and Shi-de in the picture book stems from the authors’selective use of the preceding texts and their unique interpretation.LüQiuyin’s preface,Hakuin’s comments on Han-shan,as well as the authors’knowledge and experience are vital in shaping the characters.
文摘Manuscript Text and figures combined into a single file with page and line numbers up to3 MB in size in txt,doc,docx or tex files.Prepares your manuscript in the following order:Title page,Abstract,Introduction,Materials and Methods,Results,Discussion,Acknowledgments,References,Tables,Figure Legends and Figures.Provide Cover letter and Supplementary Material(if necessary)at the same time.
基金supported by the National Nature Foundation of China under Grants(No.72104108)the College Students’Innovation and Entrepreneurship Training Program(No.202410298155Y).
文摘Green consumption(GC)are crucial for achieving the SustainableDevelopmentGoals(SDGs).However,few studies have explored public attitudes toward GC using social media data,missing potential public concerns captured through big data.To address this gap,this study collects and analyzes public attention toward GC using web crawler technology.Based on the data from Sina Weibo,we applied RoBERTa,an advanced NLP model based on transformer architecture,to conduct fine-grained sentiment analysis of the public’s attention,attitudes and hot topics on GC,demonstrating the potential of deep learning methods in capturing dynamic and contextual emotional shifts across time and regions.Among the sample(N=188,509),53.91% expressed a positive attitude,with variation across different times and regions.Temporally,public interest in GC has shown an annual growth rate of 30.23%,gradually shifting fromfulfilling basic needs to prioritizing entertainment consumption.Spatially,GC is most prevalent in the southeast coastal regions of China,with Beijing ranking first across five evaluated domains.Individuals and government-affiliated accounts play a key role in public discussions on social networks,accounting for 45.89% and 30.01% of user reviews,respectively.A significant positive correlation exists between economic development and public attention to GC,as indicated by a Pearson correlation coefficient of 0.55.Companies,in particular,exhibit cautious behavior in the early stages of green product adoption,prioritizing profitability before making substantial investments.These findings provide valuable insights into the evolving public perception of GC,contributing to the development of more effective environmental policies in China.
基金funded by the National Social Science Fund of China(22CTQ019).
文摘As blockchain technology advances,non-fungible tokens(NFTs)are emerging as unconventional assets in the commercial market.However,it is necessary to establish a comprehensive NFT ecosystem that addresses the prevailing public concerns.This study aimed to bridge this gap by analyzing user-generated content on prominent social media platforms such as Twitter,Weibo,and Reddit.Employing text clustering and topic modeling techniques,such as Latent Dirichlet Allocation,we constructed an analytical framework to delve into the intricacies of the NFT ecosystem.Our investigation revealed seven distinct topics from Twitter and Reddit data and eight topics from Weibo data.Weibo users predominantly engaged in reviews and critiques,whereas Twitter and Reddit users emphasized personal experiences and perceptions.The NFT ecosystem encompasses several crucial elements,including transactions,customers,infrastructure,products,environments,and perceptions.By identifying the prevailing trends and common issues,this study offers valuable guidance for the development of NFT ecosystems.
基金supported by College Student Innovation Training Program(202410289163Y).
文摘This study systematically investigates adverb translation in political texts through quantitative and qualitative analysis under the theoretical framework of“political equivalence,”utilizing the Government Work Report(2001-2024)as its database.The research aims to reveal the evolutionary trajectory of translation strategies across different historical periods and their underlying socio-cultural motivations.Findings demonstrate a notable shift in adverb translation strategies within political texts:transitioning from faithful reproduction to adaptive transformation.Throughout this progression,translations maintain discursive coherence while increasingly conforming to English idiomatic conventions,ultimately achieving dynamic equilibrium between accurate conveyance of political connotations and compliance with target-language norms.
