Objective: Smoking cessation during pregnancy is a modifiable intervention that can improve maternal and neonatal outcomes. Encouraging smoking cessation is an assessed measure of the Meaningful Use incentives to ensu...Objective: Smoking cessation during pregnancy is a modifiable intervention that can improve maternal and neonatal outcomes. Encouraging smoking cessation is an assessed measure of the Meaningful Use incentives to ensure best practices with the increased use of the electronic medical record (EMR). Physician EMR prompts have been used shown to be successful with preventive care but there is a paucity of data evaluating prompts within obstetrics. The objective of this study is to determine the effectiveness of enhanced smoking cessation prompts in a prenatal EMR. Methods: A retrospective cohort study of an enhanced smoking cessation prompting system within our prenatal EMR was performed. Pregnant women who reported tobacco use at first prenatal visit were included. The number of times a smoking cessation method was offered and documented, the number of documented attempts at smoking cessation, and the final number of cigarettes smoked were compared pre and post the enhancement of the smoking cessation prompting system. Results: 95 patients were included (48 pre-enhancement;47 post-enhancement). Post-enhancement, the documentation of smoking cessation method offered increased (0 vs. 1, p = 0.03) and documentation of smoking cessation attempts increased (1 vs. 2, p = 0.006). There was no change in the final number of cigarettes smoked (p = 0.9). Conclusions: Enhanced prompting systems increase documentation related to smoking cessation with no change in number of cigarettes smoked. In the era of Meaningful Use guidelines which focus on documentation in the EMR, continued research must be done to assure that software enhancements and improved documentation truly result in improved patient care.展开更多
Image emotion classification(IEC)aims to extract the abstract emotions evoked in images.Recently,language-supervised methods such as con-trastive language-image pretraining(CLIP)have demonstrated superior performance ...Image emotion classification(IEC)aims to extract the abstract emotions evoked in images.Recently,language-supervised methods such as con-trastive language-image pretraining(CLIP)have demonstrated superior performance in image under-standing.However,the underexplored task of IEC presents three major challenges:a tremendous training objective gap between pretraining and IEC,shared suboptimal prompts,and invariant prompts for all instances.In this study,we propose a general framework that effectively exploits the language-supervised CLIP method for the IEC task.First,a prompt-tuning method that mimics the pretraining objective of CLIP is introduced,to exploit the rich image and text semantics associated with CLIP.Subsequently,instance-specific prompts are automatically composed,conditioning them on the categories and image content of instances,diversifying the prompts,and thus avoiding suboptimal problems.Evaluations on six widely used affective datasets show that the proposed method significantly outperforms state-of-the-art methods(up to 9.29%accuracy gain on the EmotionROI dataset)on IEC tasks with only a few trained parameters.The code is publicly available at https://github.com/dsn0w/PT-DPC/for research purposes.展开更多
Artificial intelligence(AI)is advancing swiftly and integrating into various societal domains,including international Chinese language teaching.While AI provides diverse advantages,it also presents inherent risks,akin...Artificial intelligence(AI)is advancing swiftly and integrating into various societal domains,including international Chinese language teaching.While AI provides diverse advantages,it also presents inherent risks,akin to a“double-edged sword”.This paper delves into the challenges and opportunities associated with AI and suggests a strategy to transform AI from a potential adversary to an ally:not avoidance,but mastery.Mastery entails crafting targeted prompts suitable for distinct contexts to attain desired AI-driven outcomes.Ultimately,by presenting case studies across various proficiency levels in Chinese language instruction that utilize AI tools,this paper aims to stimulate constructive dialogue within the academic community of Chinese language teaching.展开更多
Large Language Models(LLMs)have significantly advanced human-computer interaction by improving natural language understanding and generation.However,their vulnerability to adversarial prompts–carefully designed input...Large Language Models(LLMs)have significantly advanced human-computer interaction by improving natural language understanding and generation.However,their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges.This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features.Elevenmachine learning models were evaluated based on key metrics such as accuracy,precision,recall,and F1-score.The results show that the Convolutional Neural Network–Long Short-Term Memory(CNN-LSTM)cascade model delivers the best performance,especially when using prompt features,achieving an accuracy of over 97%in all adversarial scenarios.Furthermore,the Support Vector Machine(SVM)model performed best with prompt response features,particularly excelling in prompt type classification tasks.Classification results revealed that certain types of adversarial attacks,such as“Word Level”and“Adversarial Prefix”,were particularly difficult to detect,as indicated by their low recall and F1-scores.These findings suggest that more subtle manipulations can evade detection mechanisms.In contrast,attacks like“Sentence Level”and“Adversarial Insertion”were easier to identify,due to the model’s effectiveness in recognizing inserted content.Natural Language Processing(NLP)techniques played a critical role by enabling the extraction of semantic and syntactic features from both prompts and their corresponding responses.These insights highlight the importance of combining traditional and deep learning approaches,along with advanced NLP techniques,to build more reliable adversarial prompt detection systems for LLMs.展开更多
The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper...The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN model.The proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate images.This paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the model.The GAN model successfully generates artwork from descriptions using speech and text prompts.Experimental outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input prompts.The model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse images.The proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output images.This capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.展开更多
Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. Howeve...Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.展开更多
Transactions of Nanjing University of Aeronautics&Astronautics (TNUAA) is a bimonthly journal facing international academic community.Emphasizing prompt and effective dissemination of key data and new scientific i...Transactions of Nanjing University of Aeronautics&Astronautics (TNUAA) is a bimonthly journal facing international academic community.Emphasizing prompt and effective dissemination of key data and new scientific insights,TNUAA publishes experimental and theoretical papers bearing on applications to all branches of aeronautics,astronautics and civil aviation.展开更多
In December 2023,I was part of a doctoral viva at the University of Edinburgh,and one colleague reminded us of the novelty effect notion in education.In a nutshell,this term refers to individuals or institutions quick...In December 2023,I was part of a doctoral viva at the University of Edinburgh,and one colleague reminded us of the novelty effect notion in education.In a nutshell,this term refers to individuals or institutions quickly embracing a process,idea or product,because it is perceived and experienced as innovative and beneficial,but as time passes by,the novelty becomes normalized and interest begins to decrease.For example,this effect has been examined in relation to digital technologies in education(e.g.,Jeno et al.,2019;Tsay et al.,2020).The discussion that ensued in the viva lingered in my head for a few days as it prompted me to reflect on the influence that content and language integrated learning(CLIL)continues to have around the world.In other words,research and practice have demonstrated that CLIL has overcome the novelty effect,and this special issue attests to it.展开更多
Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and ...Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.展开更多
文摘Objective: Smoking cessation during pregnancy is a modifiable intervention that can improve maternal and neonatal outcomes. Encouraging smoking cessation is an assessed measure of the Meaningful Use incentives to ensure best practices with the increased use of the electronic medical record (EMR). Physician EMR prompts have been used shown to be successful with preventive care but there is a paucity of data evaluating prompts within obstetrics. The objective of this study is to determine the effectiveness of enhanced smoking cessation prompts in a prenatal EMR. Methods: A retrospective cohort study of an enhanced smoking cessation prompting system within our prenatal EMR was performed. Pregnant women who reported tobacco use at first prenatal visit were included. The number of times a smoking cessation method was offered and documented, the number of documented attempts at smoking cessation, and the final number of cigarettes smoked were compared pre and post the enhancement of the smoking cessation prompting system. Results: 95 patients were included (48 pre-enhancement;47 post-enhancement). Post-enhancement, the documentation of smoking cessation method offered increased (0 vs. 1, p = 0.03) and documentation of smoking cessation attempts increased (1 vs. 2, p = 0.006). There was no change in the final number of cigarettes smoked (p = 0.9). Conclusions: Enhanced prompting systems increase documentation related to smoking cessation with no change in number of cigarettes smoked. In the era of Meaningful Use guidelines which focus on documentation in the EMR, continued research must be done to assure that software enhancements and improved documentation truly result in improved patient care.
基金supported in part by the National Natural Science Foundation of China under Grant Nos.62106010,61976010,62176011,62236010.
文摘Image emotion classification(IEC)aims to extract the abstract emotions evoked in images.Recently,language-supervised methods such as con-trastive language-image pretraining(CLIP)have demonstrated superior performance in image under-standing.However,the underexplored task of IEC presents three major challenges:a tremendous training objective gap between pretraining and IEC,shared suboptimal prompts,and invariant prompts for all instances.In this study,we propose a general framework that effectively exploits the language-supervised CLIP method for the IEC task.First,a prompt-tuning method that mimics the pretraining objective of CLIP is introduced,to exploit the rich image and text semantics associated with CLIP.Subsequently,instance-specific prompts are automatically composed,conditioning them on the categories and image content of instances,diversifying the prompts,and thus avoiding suboptimal problems.Evaluations on six widely used affective datasets show that the proposed method significantly outperforms state-of-the-art methods(up to 9.29%accuracy gain on the EmotionROI dataset)on IEC tasks with only a few trained parameters.The code is publicly available at https://github.com/dsn0w/PT-DPC/for research purposes.
文摘Artificial intelligence(AI)is advancing swiftly and integrating into various societal domains,including international Chinese language teaching.While AI provides diverse advantages,it also presents inherent risks,akin to a“double-edged sword”.This paper delves into the challenges and opportunities associated with AI and suggests a strategy to transform AI from a potential adversary to an ally:not avoidance,but mastery.Mastery entails crafting targeted prompts suitable for distinct contexts to attain desired AI-driven outcomes.Ultimately,by presenting case studies across various proficiency levels in Chinese language instruction that utilize AI tools,this paper aims to stimulate constructive dialogue within the academic community of Chinese language teaching.
文摘Large Language Models(LLMs)have significantly advanced human-computer interaction by improving natural language understanding and generation.However,their vulnerability to adversarial prompts–carefully designed inputs that manipulate model outputs–presents substantial challenges.This paper introduces a classification-based approach to detect adversarial prompts by utilizing both prompt features and prompt response features.Elevenmachine learning models were evaluated based on key metrics such as accuracy,precision,recall,and F1-score.The results show that the Convolutional Neural Network–Long Short-Term Memory(CNN-LSTM)cascade model delivers the best performance,especially when using prompt features,achieving an accuracy of over 97%in all adversarial scenarios.Furthermore,the Support Vector Machine(SVM)model performed best with prompt response features,particularly excelling in prompt type classification tasks.Classification results revealed that certain types of adversarial attacks,such as“Word Level”and“Adversarial Prefix”,were particularly difficult to detect,as indicated by their low recall and F1-scores.These findings suggest that more subtle manipulations can evade detection mechanisms.In contrast,attacks like“Sentence Level”and“Adversarial Insertion”were easier to identify,due to the model’s effectiveness in recognizing inserted content.Natural Language Processing(NLP)techniques played a critical role by enabling the extraction of semantic and syntactic features from both prompts and their corresponding responses.These insights highlight the importance of combining traditional and deep learning approaches,along with advanced NLP techniques,to build more reliable adversarial prompt detection systems for LLMs.
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology SydneyMoreover,supported by the Researchers Supporting Project,King Saud University,Riyadh,Saudi Arabia,under Ongoing Research Funding(ORF-2025-14).
文摘The development of generative architectures has resulted in numerous novel deep-learning models that generate images using text inputs.However,humans naturally use speech for visualization prompts.Therefore,this paper proposes an architecture that integrates speech prompts as input to image-generation Generative Adversarial Networks(GANs)model,leveraging Speech-to-Text translation along with the CLIP+VQGAN model.The proposed method involves translating speech prompts into text,which is then used by the Contrastive Language-Image Pretraining(CLIP)+Vector Quantized Generative Adversarial Network(VQGAN)model to generate images.This paper outlines the steps required to implement such a model and describes in detail the methods used for evaluating the model.The GAN model successfully generates artwork from descriptions using speech and text prompts.Experimental outcomes of synthesized images demonstrate that the proposed methodology can produce beautiful abstract visuals containing elements from the input prompts.The model achieved a Frechet Inception Distance(FID)score of 28.75,showcasing its capability to produce high-quality and diverse images.The proposed model can find numerous applications in educational,artistic,and design spaces due to its ability to generate images using speech and the distinct abstract artistry of the output images.This capability is demonstrated by giving the model out-of-the-box prompts to generate never-before-seen images with plausible realistic qualities.
基金supported by the National Key Research and Development Program of China(2023YFA1507601)the National Natural Science Foundation of China(22278127,22378038)+2 种基金the Fundamental Research Funds for the Central Universities(2022ZFJH004)the Shanghai Pilot Program for Basic Research(22T01400100-18)the Natural Science Foundation of Liaoning Province,China(2024-MSBA-15).
文摘Data-driven approaches are extensively employed to model complex chemical engineering processes, such as hydrotreating, to address the challenges of mechanism-based methods demanding deep process understanding. However, the development of such models requires specialized expertise in data science, limiting their broader application. Large language models (LLMs), such as GPT-4, have demonstrated potential in supporting and guiding research efforts. This work presents a novel AI-assisted framework where GPT-4, through well-engineered prompts, facilitates the construction and explanation of multi-objective neural networks. These models predict hydrotreating products properties (such as distillation range), including refined diesel and refined gas oil, using feedstock properties, operating conditions, and recycle hydrogen composition. Gradient-weighted class activation mapping was employed to identify key features influencing the output variables. This work illustrates an innovative AI-guided paradigm for chemical engineering applications, and the designed prompts hold promise for adaptation to other complex processes.
文摘Transactions of Nanjing University of Aeronautics&Astronautics (TNUAA) is a bimonthly journal facing international academic community.Emphasizing prompt and effective dissemination of key data and new scientific insights,TNUAA publishes experimental and theoretical papers bearing on applications to all branches of aeronautics,astronautics and civil aviation.
文摘In December 2023,I was part of a doctoral viva at the University of Edinburgh,and one colleague reminded us of the novelty effect notion in education.In a nutshell,this term refers to individuals or institutions quickly embracing a process,idea or product,because it is perceived and experienced as innovative and beneficial,but as time passes by,the novelty becomes normalized and interest begins to decrease.For example,this effect has been examined in relation to digital technologies in education(e.g.,Jeno et al.,2019;Tsay et al.,2020).The discussion that ensued in the viva lingered in my head for a few days as it prompted me to reflect on the influence that content and language integrated learning(CLIL)continues to have around the world.In other words,research and practice have demonstrated that CLIL has overcome the novelty effect,and this special issue attests to it.
文摘Processing police incident data in public security involves complex natural language processing(NLP)tasks,including information extraction.This data contains extensive entity information—such as people,locations,and events—while also involving reasoning tasks like personnel classification,relationship judgment,and implicit inference.Moreover,utilizing models for extracting information from police incident data poses a significant challenge—data scarcity,which limits the effectiveness of traditional rule-based and machine-learning methods.To address these,we propose TIPS.In collaboration with public security experts,we used de-identified police incident data to create templates that enable large language models(LLMs)to populate data slots and generate simulated data,enhancing data density and diversity.We then designed schemas to efficiently manage complex extraction and reasoning tasks,constructing a high-quality dataset and fine-tuning multiple open-source LLMs.Experiments showed that the fine-tuned ChatGLM-4-9B model achieved an F1 score of 87.14%,nearly 30%higher than the base model,significantly reducing error rates.Manual corrections further improved performance by 9.39%.This study demonstrates that combining largescale pre-trained models with limited high-quality domain-specific data can greatly enhance information extraction in low-resource environments,offering a new approach for intelligent public security applications.
