From text to image:challenges in integrating vision into ChatGPT for medical image interpretation

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).

一种利用词典扩展数据库模式信息的Text2SQL方法

现有Text2SQL方法严重依赖表名和列名在自然语言查询中的显式提及,在同物异名的实际应用场景中准确率急剧下降.此外,这些方法仅仅依赖数据库模式捕捉数据库建模的领域知识,而数据库模式作为结构化的元数据,其表达领域知识的能力是非常有限的,即使有经验的程序员也很难仅从数据库模式完全领会该数据库建模的领域知识,因此程序员必须依赖详细的数据库设计文档才能构造SQL语句以正确地表达特定的查询.为此,本文提出一种利用词典扩展数据库模式信息的Text2SQL方法,该方法从数据库表名和列名解析出其中的单词或短语,查询词典获取这些单词或短语的语义解释,将这些解释看成是相应表名或列名的扩展内容,与表名、列名及其他数据库模式信息(主键、外键等)相结合,作为模型的输入,从而使模型能够更全面地学习数据库建模的应用领域知识.在Spider-syn和Spider数据集上进行的实验说明了所提出方法的有效性,即使自然语言查询中使用的表名和列名与数据库模式中对应的表名和列名完全不同,本文方法也能够得到较好的SQL翻译结果,明显优于最新提出的抗同义词替换攻击的方法.

BSTFNet:An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

While encryption technology safeguards the security of network communications,malicious traffic also uses encryption protocols to obscure its malicious behavior.To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic,we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features,called BERT-based Spatio-Temporal Features Network(BSTFNet).At the packet-level granularity,the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers(BERT)model.At the byte-level granularity,we initially employ the Bidirectional Gated Recurrent Unit(BiGRU)model to extract temporal features from bytes,followed by the utilization of the Text Convolutional Neural Network(TextCNN)model with multi-sized convolution kernels to extract local multi-receptive field spatial features.The fusion of features from both granularities serves as the ultimate multidimensional representation of malicious traffic.Our approach achieves accuracy and F1-score of 99.39%and 99.40%,respectively,on the publicly available USTC-TFC2016 dataset,and effectively reduces sample confusion within the Neris and Virut categories.The experimental results demonstrate that our method has outstanding representation and classification capabilities for encrypted malicious traffic.

树立行业发展新方向——Techtextil&Texprocess 2024亮点回顾

为期4天的法兰克福国际产业用纺织品及非织造布展览会及国际纺织品及柔性材料缝制加工展览会(Techtextil&Texprocess 2024)吸引了来自全球53个国家和地区的1700家领先企业参展和来自102个国家和地区的38000名观众,展会规模再创新高,充分彰显了纺织行业蓬勃的生命力与持续的创新力。

Identifying multidisciplinary problems from scientific publications based on a text generation method

Purpose:A text generation based multidisciplinary problem identification method is proposed,which does not rely on a large amount of data annotation.Design/methodology/approach:The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique;second,it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model;third,it extracts problem phrases from generated titles according to regular expression rules;fourth,it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm;finally,it identifies multidisciplinary problems based on the disciplinary labels of papers.Findings:Experiments in the“Carbon Peaking and Carbon Neutrality”field show that the proposed method can effectively identify multidisciplinary research problems.The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field.Research limitations:It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness.Practical implications:Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments,fund valuable multidisciplinary problems for research management authorities,and borrow ideas from other disciplines for researchers.Originality/value:This approach proposes a novel multidisciplinary problem identification method based on text generation,which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.

CVTD: A Robust Car-Mounted Video Text Detector

Text perception is crucial for understanding the semantics of outdoor scenes,making it a key requirement for building intelligent systems for driver assistance or autonomous driving.Text information in car-mounted videos can assist drivers in making decisions.However,Car-mounted video text images pose challenges such as complex backgrounds,small fonts,and the need for real-time detection.We proposed a robust Car-mounted Video Text Detector(CVTD).It is a lightweight text detection model based on ResNet18 for feature extraction,capable of detecting text in arbitrary shapes.Our model efficiently extracted global text positions through the Coordinate Attention Threshold Activation(CATA)and enhanced the representation capability through stacking two Feature Pyramid Enhancement Fusion Modules(FPEFM),strengthening feature representation,and integrating text local features and global position information,reinforcing the representation capability of the CVTD model.The enhanced feature maps,when acted upon by Text Activation Maps(TAM),effectively distinguished text foreground from non-text regions.Additionally,we collected and annotated a dataset containing 2200 images of Car-mounted Video Text(CVT)under various road conditions for training and evaluating our model’s performance.We further tested our model on four other challenging public natural scene text detection benchmark datasets,demonstrating its strong generalization ability and real-time detection speed.This model holds potential for practical applications in real-world scenarios.

A Method for Detecting and Recognizing Yi Character Based on Deep Learning

Aiming at the challenges associated with the absence of a labeled dataset for Yi characters and the complexity of Yi character detection and recognition,we present a deep learning-based approach for Yi character detection and recognition.In the detection stage,an improved Differentiable Binarization Network(DBNet)framework is introduced to detect Yi characters,in which the Omni-dimensional Dynamic Convolution(ODConv)is combined with the ResNet-18 feature extraction module to obtain multi-dimensional complementary features,thereby improving the accuracy of Yi character detection.Then,the feature pyramid network fusion module is used to further extract Yi character image features,improving target recognition at different scales.Further,the previously generated feature map is passed through a head network to produce two maps:a probability map and an adaptive threshold map of the same size as the original map.These maps are then subjected to a differentiable binarization process,resulting in an approximate binarization map.This map helps to identify the boundaries of the text boxes.Finally,the text detection box is generated after the post-processing stage.In the recognition stage,an improved lightweight MobileNetV3 framework is used to recognize the detect character regions,where the original Squeeze-and-Excitation(SE)block is replaced by the efficient Shuffle Attention(SA)that integrates spatial and channel attention,improving the accuracy of Yi characters recognition.Meanwhile,the use of depth separable convolution and reversible residual structure can reduce the number of parameters and computation of the model,so that the model can better understand the contextual information and improve the accuracy of text recognition.The experimental results illustrate that the proposed method achieves good results in detecting and recognizing Yi characters,with detection and recognition accuracy rates of 97.5%and 96.8%,respectively.And also,we have compared the detection and recognition algorithms proposed in this paper with other typical algorithms.In these comparisons,the proposed model achieves better detection and recognition results with a certain reliability.

Introducing MagBERT:A language model for magnesium textual data mining and analysis

Magnesium(Mg)based materials hold immense potential for various applications due to their lightweight and high strength-to-weight ratio.However,to fully harness the potential of Mg alloys,structured analytics are essential to gain valuable insights from centuries of accumulated knowledge.Efficient information extraction from the vast corpus of scientific literature is crucial for this purpose.In this work,we introduce MagBERT,a BERT-based language model specifically trained for Mg-based materials.Utilizing a dataset of approximately 370,000 abstracts focused on Mg and its alloys,MagBERT is designed to understand the intricate details and specialized terminology of this domain.Through rigorous evaluation,we demonstrate the effectiveness of MagBERT for information extraction using a fine-tuned named entity recognition(NER)model,named MagNER.This NER model can extract mechanical,microstructural,and processing properties related to Mg alloys.For instance,we have created an Mg alloy dataset that includes properties such as ductility,yield strength,and ultimate tensile strength(UTS),along with standard alloy names.The introduction of MagBERT is a novel advancement in the development of Mg-specific language models,marking a significant milestone in the discovery of Mg alloys and textual information extraction.By making the pre-trained weights of MagBERT publicly accessible,we aim to accelerate research and innovation in the field of Mg-based materials through efficient information extraction and knowledge discovery.

HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

Class Title:Radiological imaging method a comprehensive overview purpose.This GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the field.Materials and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed tomography.Additionally recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis systems.Results:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical conditions.Conclusion:Radiological imaging is an extremely important tool in modern medicine to assist in medical diagnosis.This work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

Assessing trends in wildland-urban interface fire research through text mining: a comprehensive analysis of published literature

Research on fires at the wildland-urban inter-face(WUI)has generated significant insights and advance-ments across various fields of study.Environmental,agri-culture,and social sciences have played prominent roles in understanding the impacts of fires in the environment,in protecting communities,and addressing management challenges.This study aimed to create a database using a text mining technique for global researchers interested in WUI-projects and highlighting the interest of countries in this field.Author’s-Keywords analysis emphasized the dominance of fire science-related terms,especially related to WUI,and identified keyword clusters related to the WUI fire-risk-assessment-system-“exposure”,“danger”,and“vulnerability”within wildfire research.Trends over the past decade showcase shifting research interests with a growing focus on WUI fires,while regional variations highlighted that the“exposure”keyword cluster received greater atten-tion in the southern Europe and South America.However,vulnerability keywords have relatively a lower representation across all regions.The analysis underscores the interdisci-plinary nature of WUI research and emphasizes the need for targeted approaches to address the unique challenges of the wildland-urban interface.Overall,this study provides valu-able insights for researchers and serves as a foundation for further collaboration in this field through the understanding of the trends over recent years and in different regions.

Relational Turkish Text Classification Using Distant Supervised Entities and Relations

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

YOLOv5ST:A Lightweight and Fast Scene Text Detector

Scene text detection is an important task in computer vision.In this paper,we present YOLOv5 Scene Text(YOLOv5ST),an optimized architecture based on YOLOv5 v6.0 tailored for fast scene text detection.Our primary goal is to enhance inference speed without sacrificing significant detection accuracy,thereby enabling robust performance on resource-constrained devices like drones,closed-circuit television cameras,and other embedded systems.To achieve this,we propose key modifications to the network architecture to lighten the original backbone and improve feature aggregation,including replacing standard convolution with depth-wise convolution,adopting the C2 sequence module in place of C3,employing Spatial Pyramid Pooling Global(SPPG)instead of Spatial Pyramid Pooling Fast(SPPF)and integrating Bi-directional Feature Pyramid Network(BiFPN)into the neck.Experimental results demonstrate a remarkable 26%improvement in inference speed compared to the baseline,with only marginal reductions of 1.6%and 4.2%in mean average precision(mAP)at the intersection over union(IoU)thresholds of 0.5 and 0.5:0.95,respectively.Our work represents a significant advancement in scene text detection,striking a balance between speed and accuracy,making it well-suited for performance-constrained environments.

Leveraging Uncertainty for Depth-Aware Hierarchical Text Classification

Hierarchical Text Classification(HTC)aims to match text to hierarchical labels.Existing methods overlook two critical issues:first,some texts cannot be fully matched to leaf node labels and need to be classified to the correct parent node instead of treating leaf nodes as the final classification target.Second,error propagation occurs when a misclassification at a parent node propagates down the hierarchy,ultimately leading to inaccurate predictions at the leaf nodes.To address these limitations,we propose an uncertainty-guided HTC depth-aware model called DepthMatch.Specifically,we design an early stopping strategy with uncertainty to identify incomplete matching between text and labels,classifying them into the corresponding parent node labels.This approach allows us to dynamically determine the classification depth by leveraging evidence to quantify and accumulate uncertainty.Experimental results show that the proposed DepthMatch outperforms recent strong baselines on four commonly used public datasets:WOS(Web of Science),RCV1-V2(Reuters Corpus Volume I),AAPD(Arxiv Academic Paper Dataset),and BGC.Notably,on the BGC dataset,it improvesMicro-F1 andMacro-F1 scores by at least 1.09%and 1.74%,respectively.

A Weighted Multi-Layer Analytics Based Model for Emoji Recommendation

The developed system for eye and face detection using Convolutional Neural Networks(CNN)models,followed by eye classification and voice-based assistance,has shown promising potential in enhancing accessibility for individuals with visual impairments.The modular approach implemented in this research allows for a seamless flow of information and assistance between the different components of the system.This research significantly contributes to the field of accessibility technology by integrating computer vision,natural language processing,and voice technologies.By leveraging these advancements,the developed system offers a practical and efficient solution for assisting blind individuals.The modular design ensures flexibility,scalability,and ease of integration with existing assistive technologies.However,it is important to acknowledge that further research and improvements are necessary to enhance the system’s accuracy and usability.Fine-tuning the CNN models and expanding the training dataset can improve eye and face detection as well as eye classification capabilities.Additionally,incorporating real-time responses through sophisticated natural language understanding techniques and expanding the knowledge base of ChatGPT can enhance the system’s ability to provide comprehensive and accurate responses.Overall,this research paves the way for the development of more advanced and robust systems for assisting visually impaired individuals.By leveraging cutting-edge technologies and integrating them into amodular framework,this research contributes to creating a more inclusive and accessible society for individuals with visual impairments.Future work can focus on refining the system,addressing its limitations,and conducting user studies to evaluate its effectiveness and impact in real-world scenarios.

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.

A comprehensive review of existing corpora and methods for creating annotated corpora for event extraction tasks

Purpose:The purpose of this study is to serve as a comprehensive review of the existing annotated corpora.This review study aims to provide information on the existing annotated corpora for event extraction,which are limited but essential for training and improving the existing event extraction algorithms.In addition to the primary goal of this study,it provides guidelines for preparing an annotated corpus and suggests suitable tools for the annotation task.Design/methodology/approach:This study employs an analytical approach to examine available corpus that is suitable for event extraction tasks.It offers an in-depth analysis of existing event extraction corpora and provides systematic guidelines for researchers to develop accurate,high-quality corpora.This ensures the reliability of the created corpus and its suitability for training machine learning algorithms.Findings:Our exploration reveals a scarcity of annotated corpora for event extraction tasks.In particular,the English corpora are mainly focused on the biomedical and general domains.Despite the issue of annotated corpora scarcity,there are several high-quality corpora available and widely used as benchmark datasets.However,access to some of these corpora might be limited owing to closed-access policies or discontinued maintenance after being initially released,rendering them inaccessible owing to broken links.Therefore,this study documents the available corpora for event extraction tasks.Research limitations:Our study focuses only on well-known corpora available in English and Chinese.Nevertheless,this study places a strong emphasis on the English corpora due to its status as a global lingua franca,making it widely understood compared to other languages.Practical implications:We genuinely believe that this study provides valuable knowledge that can serve as a guiding framework for preparing and accurately annotating events from text corpora.It provides comprehensive guidelines for researchers to improve the quality of corpus annotations,especially for event extraction tasks across various domains.Originality/value:This study comprehensively compiled information on the existing annotated corpora for event extraction tasks and provided preparation guidelines.

A Shuffling-Steganography Algorithm to Protect Data of Drone Applications

In Saudi Arabia,drones are increasingly used in different sensitive domains like military,health,and agriculture to name a few.Typically,drone cameras capture aerial images of objects and convert them into crucial data,alongside collecting data from distributed sensors supplemented by location data.The interception of the data sent from the drone to the station can lead to substantial threats.To address this issue,highly confidential protection methods must be employed.This paper introduces a novel steganography approach called the Shuffling Steganography Approach(SSA).SSA encompasses five fundamental stages and three proposed algorithms,designed to enhance security through strategic encryption and data hiding techniques.Notably,this method introduces advanced resistance to brute force attacks by employing predefined patterns across a wide array of images,complicating unauthorized access.The initial stage involves encryption,dividing,and disassembling the encrypted data.A small portion of the encrypted data is concealed within the text(Algorithm 1)in the third stage.Subsequently,the parts are merged and mixed(Algorithm 2),and finally,the composed text is hidden within an image(Algorithm 3).Through meticulous investigation and comparative analysis with existing methodologies,the proposed approach demonstrates superiority across various pertinent criteria,including robustness,secret message size capacity,resistance to multiple attacks,and multilingual support.

Literature classification and its applications in condensed matter physics and materials science by natural language processing

The exponential growth of literature is constraining researchers’access to comprehensive information in related fields.While natural language processing(NLP)may offer an effective solution to literature classification,it remains hindered by the lack of labelled dataset.In this article,we introduce a novel method for generating literature classification models through semi-supervised learning,which can generate labelled dataset iteratively with limited human input.We apply this method to train NLP models for classifying literatures related to several research directions,i.e.,battery,superconductor,topological material,and artificial intelligence(AI)in materials science.The trained NLP‘battery’model applied on a larger dataset different from the training and testing dataset can achieve F1 score of 0.738,which indicates the accuracy and reliability of this scheme.Furthermore,our approach demonstrates that even with insufficient data,the not-well-trained model in the first few cycles can identify the relationships among different research fields and facilitate the discovery and understanding of interdisciplinary directions.

Cross-Target Stance Detection with Sentiments-Aware Hierarchical Attention Network

The task of cross-target stance detection faces significant challenges due to the lack of additional background information in emerging knowledge domains and the colloquial nature of language patterns.Traditional stance detection methods often struggle with understanding limited context and have insufficient generalization across diverse sentiments and semantic structures.This paper focuses on effectively mining and utilizing sentimentsemantics knowledge for stance knowledge transfer and proposes a sentiment-aware hierarchical attention network(SentiHAN)for cross-target stance detection.SentiHAN introduces an improved hierarchical attention network designed to maximize the use of high-level representations of targets and texts at various fine-grain levels.This model integrates phrase-level combinatorial sentiment knowledge to effectively bridge the knowledge gap between known and unknown targets.By doing so,it enables a comprehensive understanding of stance representations for unknown targets across different sentiments and semantic structures.The model’s ability to leverage sentimentsemantics knowledge enhances its performance in detecting stances that may not be directly observable from the immediate context.Extensive experimental results indicate that SentiHAN significantly outperforms existing benchmark methods in terms of both accuracy and robustness.Moreover,the paper employs ablation studies and visualization techniques to explore the intricate relationship between sentiment and stance.These analyses further confirm the effectiveness of sentence-level combinatorial sentiment knowledge in improving stance detection capabilities.

Detecting Malicious Uniform Resource Locators Using an Applied Intelligence Framework

The potential of text analytics is revealed by Machine Learning(ML)and Natural Language Processing(NLP)techniques.In this paper,we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators(URLs).Three categories of features,both ML and Deep Learning(DL)algorithms and a ranking schema are included in the proposed framework.We apply frequency and prediction-based embeddings,such as hash vectorizer,Term Frequency-Inverse Dense Frequency(TF-IDF)and predictors,word to vector-word2vec(continuous bag of words,skip-gram)from Google,to extract features from text.Further,we apply more state-of-the-art methods to create vectorized features,such as GloVe.Additionally,feature engineering that is specific to URL structure is deployed to detect scams and other threats.For framework assessment,four ranking indicators are weighted:computational time and performance as accuracy,F1 score and type error II.For the computational time,we propose a new metric-Feature Building Time(FBT)as the cutting-edge feature builders(like doc2vec or GloVe)require more time.By applying the proposed assessment step,the skip-gram algorithm of word2vec surpasses other feature builders in performance.Additionally,eXtreme Gradient Boost(XGB)outperforms other classifiers.With this setup,we attain an accuracy of 99.5%and an F1 score of 0.99.