A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

交互式数字叙事:加拿大Writing New Body Worlds阅读治疗新探索

文章基于相关研究文献与语料,对加拿大WNB阅读治疗实践进行梳理与归纳。WNB首次使用交互式数字叙事技术,构建面向公众的阅读治疗平台,以其交互式特征,融合了阅读和治疗引导两个过程,提供可访问的非临床方式,可面向大规模人群实现预防性健康干预。基于数据与计算的精准化阅读治疗是数字人文视角下的新变革,更具推广应用价值;以在线阅读平台建设为依托,推进实施平台化的阅读和治疗应成为未来发展方向之一;基于阅读形态与体验因技术赋能而改变的现状,图书馆应发挥引领作用,积极收集、创建或协助创建数字故事,推进阅读推广、服务与治疗的融合性发展,成为社区建设促进者、历史欣赏者和社区参与倡导者,也使阅读借助交互式数字叙事这一新载体,更好地发挥变革的力量,促进健康与幸福。

网络新媒体视阈下客家议题在日传播特征分析——基于日本Google News的考察

以日本Google News中的客家相关日文报道为语料,通过文本挖掘工具KH Coder进行文本数据挖掘,发现日本网络新闻媒体的客家议题日文报道具有分布零散性及以图片报道为主的特点。报道关注的客家区域主要涉及台湾地区、福建、香港地区、广东等地,其中,台湾地区的客家关注度最高。报道聚焦客家的观光旅游、产业振兴、客家书籍出版等相关内容,形成了客家传统建筑类、台湾地区客家文化交流类、客家文学作品推介类、客家观光旅游类等四大主要议题。本文提出要充分运用Google News等网络新媒体强大的技术功能和传播优势加大客家文化的传播力度。

Epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China:a hospital-based retrospective study

Traumatic spinal cord injury is potentially catastrophic and can lead to permanent disability or even death.China has the largest population of patients with traumatic spinal cord injury.Previous studies of traumatic spinal cord injury in China have mostly been regional in scope;national-level studies have been rare.To the best of our knowledge,no national-level study of treatment status and economic burden has been performed.This retrospective study aimed to examine the epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China at the national level.We included 13,465 traumatic spinal cord injury patients who were injured between January 2013 and December 2018 and treated in 30 hospitals in 11 provinces/municipalities representing all geographical divisions of China.Patient epidemiological and clinical features,treatment status,and total and daily costs were recorded.Trends in the percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department and cost of care were assessed by annual percentage change using the Joinpoint Regression Program.The percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department did not significantly change overall(annual percentage change,-0.5%and 2.1%,respectively).A total of 10,053(74.7%)patients underwent surgery.Only 2.8%of patients who underwent surgery did so within 24 hours of injury.A total of 2005(14.9%)patients were treated with high-dose(≥500 mg)methylprednisolone sodium succinate/methylprednisolone(MPSS/MP);615(4.6%)received it within 8 hours.The total cost for acute traumatic spinal cord injury decreased over the study period(-4.7%),while daily cost did not significantly change(1.0%increase).Our findings indicate that public health initiatives should aim at improving hospitals’ability to complete early surgery within 24 hours,which is associated with improved sensorimotor recovery,increasing the awareness rate of clinical guidelines related to high-dose MPSS/MP to reduce the use of the treatment with insufficient evidence.

Endoscopic features and treatments of gastric cystica profunda

BACKGROUND Gastric cystica profunda(GCP)represents a rare condition characterized by cystic dilation of gastric glands within the mucosal and/or submucosal layers.GCP is often linked to,or may progress into,early gastric cancer(EGC).AIM To provide a comprehensive evaluation of the endoscopic features of GCP while assessing the efficacy of endoscopic treatment,thereby offering guidance for diagnosis and treatment.METHODS This retrospective study involved 104 patients with GCP who underwent endoscopic resection.Alongside demographic and clinical data,regular patient followups were conducted to assess local recurrence.RESULTS Among the 104 patients diagnosed with GCP who underwent endoscopic resection,12.5%had a history of previous gastric procedures.The primary site predominantly affected was the cardia(38.5%,n=40).GCP commonly exhibited intraluminal growth(99%),regular presentation(74.0%),and ulcerative mucosa(61.5%).The leading endoscopic feature was the mucosal lesion type(59.6%,n=62).The average maximum diameter was 20.9±15.3 mm,with mucosal involvement in 60.6%(n=63).Procedures lasted 73.9±57.5 min,achieving complete resection in 91.3%(n=95).Recurrence(4.8%)was managed via either surgical intervention(n=1)or through endoscopic resection(n=4).Final pathology confirmed that 59.6%of GCP cases were associated with EGC.Univariate analysis indicated that elderly males were more susceptible to GCP associated with EGC.Conversely,multivariate analysis identified lesion morphology and endoscopic features as significant risk factors.Survival analysis demonstrated no statistically significant difference in recurrence between GCP with and without EGC(P=0.72).CONCLUSION The findings suggested that endoscopic resection might serve as an effective and minimally invasive treatment for GCP with or without EGC.

Boehmeria nivea var. strigosa (Urticaceae), a new variety from Southwest China

Boehmeria nivea var.strigosa Zeng Y.Wu&Y.Zhao,a new variety of B.nivea(Urticaceae)from Southwest China,is here described based on evidence from morphology and molecular phylogeny.This new variety is mainly characterized by its green abaxial leaf blade,partly connate stipules,and densely patent strigose hairs on stems and potioles.The phylogenetic analysis based on rbc L,nrDNA and rbc L+nrDNA datasets,revealed that all individuals of B.nivea var.strigosa formed a monophyletic group.The conservation status of B.nivea var.strigosa is assessed as“Near Threatened”(NT)according to IUCN evaluation criteria.The discovery of this new variety is not only crucial for the taxonomy of ramie,but also provides reference for the exploration and utilization of ramie.

Multimodal Social Media Fake News Detection Based on Similarity Inference and Adversarial Networks

As social networks become increasingly complex, contemporary fake news often includes textual descriptionsof events accompanied by corresponding images or videos. Fake news in multiple modalities is more likely tocreate a misleading perception among users. While early research primarily focused on text-based features forfake news detection mechanisms, there has been relatively limited exploration of learning shared representationsin multimodal (text and visual) contexts. To address these limitations, this paper introduces a multimodal modelfor detecting fake news, which relies on similarity reasoning and adversarial networks. The model employsBidirectional Encoder Representation from Transformers (BERT) and Text Convolutional Neural Network (Text-CNN) for extracting textual features while utilizing the pre-trained Visual Geometry Group 19-layer (VGG-19) toextract visual features. Subsequently, the model establishes similarity representations between the textual featuresextracted by Text-CNN and visual features through similarity learning and reasoning. Finally, these features arefused to enhance the accuracy of fake news detection, and adversarial networks have been employed to investigatethe relationship between fake news and events. This paper validates the proposed model using publicly availablemultimodal datasets from Weibo and Twitter. Experimental results demonstrate that our proposed approachachieves superior performance on Twitter, with an accuracy of 86%, surpassing traditional unimodalmodalmodelsand existing multimodal models. In contrast, the overall better performance of our model on the Weibo datasetsurpasses the benchmark models across multiple metrics. The application of similarity reasoning and adversarialnetworks in multimodal fake news detection significantly enhances detection effectiveness in this paper. However,current research is limited to the fusion of only text and image modalities. Future research directions should aimto further integrate features fromadditionalmodalities to comprehensively represent themultifaceted informationof fake news.

New direction for surgery:Super minimally invasive surgery

The top goal of modern medicine is treating disease without destroying organ structures and making patients as healthy as they were before their sickness.Minimally invasive surgery(MIS)has dominated the surgical realm because of its lesser invasiveness.However,changes in anatomical structures of the body and reconstruction of internal organs or different organs are common after traditional surgery or MIS,decreasing the quality of life of patients post-operation.Thus,I propose a new treatment mode,super MIS(SMIS),which is defined as“curing a disease or lesion which used to be treated by MIS while preserving the integrity of the organs”.In this study,I describe the origin,definition,operative channels,advantages,and future perspectives of SMIS.

New Fragile Watermarking Technique to Identify Inserted Video Objects Using H.264 and Color Features

The transmission of video content over a network raises various issues relating to copyright authenticity,ethics,legality,and privacy.The protection of copyrighted video content is a significant issue in the video industry,and it is essential to find effective solutions to prevent tampering and modification of digital video content during its transmission through digital media.However,there are stillmany unresolved challenges.This paper aims to address those challenges by proposing a new technique for detectingmoving objects in digital videos,which can help prove the credibility of video content by detecting any fake objects inserted by hackers.The proposed technique involves using two methods,the H.264 and the extraction color features methods,to embed and extract watermarks in video frames.The study tested the performance of the system against various attacks and found it to be robust.The evaluation was done using different metrics such as Peak-Signal-to-Noise Ratio(PSNR),Mean Squared Error(MSE),Structural Similarity Index Measure(SSIM),Bit Correction Ratio(BCR),and Normalized Correlation.The accuracy of identifying moving objects was high,ranging from 96.3%to 98.7%.The system was also able to embed a fragile watermark with a success rate of over 93.65%and had an average capacity of hiding of 78.67.The reconstructed video frames had high quality with a PSNR of at least 65.45 dB and SSIMof over 0.97,making them imperceptible to the human eye.The system also had an acceptable average time difference(T=1.227/s)compared with other state-of-the-art methods.

Comprehensive Evaluation of Flower Border Application Value of New and Superior Plants in Hefei Area

An analytic hierarchy process(AHP)was employed to assess the applicability of 18 new and superior varieties of flowers in Hefei City flower border applications.A total of 12 indicators were selected from three distinct aspects of adaptability,ornamental characteristics and use traits,in order to establish a comprehensive evaluation model.The results demonstrate that grade I(J≥2.685)exhibits excellent application value,encompassing six species of plants,such asHydrangeamacrophylla‘Endless Summer’;grade II(2.684≤J≤2.420)is also of notable application value,encompassing five species of plants,such asCallistemonrigidus;grade III(2.419≤J≤2.615)is of average application value,including five species of plants,such asCrocosmiacrocosmiflora;grade IV(J≤2.16)is of relatively poor application value.The evaluation results may be utilized as a theoretical reference for the promotion of new and superior varieties in the flower border of Hefei.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

Constitution identification model in traditional Chinese medicine based on multiple features

Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions;thereby offering optimized guidance for clinical diagnosis and treatment plan-ning;and ultimately enhancing medical efficiency and treatment outcomes.Methods First;TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people;from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ);and a dataset encompassing labelled constitutions was constructed.Second;heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition;a dual-branch deep network was employed to collect deep features from the full-body standing images.Last;the random forest(RF)algorithm was utilized to learn the extracted multifea-tures;which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy;precision;and F1 score were the three measures selected to assess the perfor-mance of the model.Results It was found that the accuracy;precision;and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842;0.868;and 0.790;respectively.In comparison with the identification models that encompass a single feature;either a single facial complexion feature;a body shape feature;or deep features;the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105;0.105;and 0.079;the precision increased by 0.164;0.164;and 0.211;and the F1 score rose by 0.071;0.071;and 0.084;respectively.Conclusion The research findings affirmed the viability of the proposed model;which incor-porated multifeatures;including the facial complexion feature;the body shape feature;and the deep feature.In addition;by employing the proposed model;the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.

A Concise and Varied Visual Features-Based Image Captioning Model with Visual Selection

Image captioning has gained increasing attention in recent years.Visual characteristics found in input images play a crucial role in generating high-quality captions.Prior studies have used visual attention mechanisms to dynamically focus on localized regions of the input image,improving the effectiveness of identifying relevant image regions at each step of caption generation.However,providing image captioning models with the capability of selecting the most relevant visual features from the input image and attending to them can significantly improve the utilization of these features.Consequently,this leads to enhanced captioning network performance.In light of this,we present an image captioning framework that efficiently exploits the extracted representations of the image.Our framework comprises three key components:the Visual Feature Detector module(VFD),the Visual Feature Visual Attention module(VFVA),and the language model.The VFD module is responsible for detecting a subset of the most pertinent features from the local visual features,creating an updated visual features matrix.Subsequently,the VFVA directs its attention to the visual features matrix generated by the VFD,resulting in an updated context vector employed by the language model to generate an informative description.Integrating the VFD and VFVA modules introduces an additional layer of processing for the visual features,thereby contributing to enhancing the image captioning model’s performance.Using the MS-COCO dataset,our experiments show that the proposed framework competes well with state-of-the-art methods,effectively leveraging visual representations to improve performance.The implementation code can be found here:https://github.com/althobhani/VFDICM(accessed on 30 July 2024).

Endoscopic diagnosis and management of gallbladder carcinoma in minimally invasive era:New needs,new models

Gallbladder cancer(GBC)is a rare and lethal malignancy;however,it represents the most common type of biliary tract cancer.Patients with GBC are often diagnosed at an advanced stage,thus,unfortunately,losing the opportunity for curative surgical intervention.This situation leads to lower quality of life and higher mortality rates.In recent years,the rapid development of endoscopic equipment and techniques has provided new avenues and possibilities for the early and minimally invasive diagnosis and treatment of GBC.This editorial comments on the article by Pavlidis et al.Building upon their work,we explore the new needs and corresponding models for managing GBC from the endoscopic diagnosis and treatment perspective.

A new diatom genus Spargeria gen.nov.(Bacillariophyceae)from Xizang,China,and the description of two new species

During our investigation of diatom biodiversity in Xizang,two species exhibited unique morphological features discriminative from all previously known genera.Herein we describe these two species and describe as new the genus,Spargeria gen.nov.The new genus features narrow to wide rectangular valves,narrow valve mantles,filiform raphe branches that occur on the valve face only,terminal raphe fissures straight or slightly deflected to same side,bow-tie shaped central areas,chambered striae present on the valve face only,being absent from the mantle,wider striae near the axial area and very narrow near the margin,multiseriate striae with small and round areolae that are occluded externally.Comparatively,Spargeria zhuii sp.nov.has larger and robust valves,radiate striae,with one divergent stria near the apices,while Spargeria chenia sp.nov.is smaller,with narrow valves,striae slightly radiate in the middle,becoming convergent or parallel near apices.This new genus belongs to the family Pinnulariaceae,and it was compared and contrasted with other genera of this family.Our work suggests the need for continued studies to document the biodiversity of diatoms in Xizang.

Lineaperpetua gen.nov.:a new diatom genus in the Thalassiosirales supported by morphology and molecular data

Based on a combination of morphology and molecular data of ribosomal DNA genes,a new diatom genus Lineaperpetua gen.nov.Yu,You,Kociolek&Wang is described.The features that help define Lineaperpetua at the level of genus include:a tangentially undulated valve face;continuous cribra areolae on the valve interior consisting of pores arranged as strips;single rimoportula located inside the ring of marginal fultoportulae.Additionally,phylogenetic analysis based on nuclear small subunit(SSU)rDNA sequences and nuclear large subunit(LSU)rDNA gene placed the three strains of L.lacustris in a single,monophyletic clade at a considerable sequence distance from the other genera(Thalassiosira,Conticribra,Planktoniella,Shinodiscus,and other genera)belonging to Thalassiosirales.Despite the similarities with some species of Thalassiosira,Conticribra,and Spicaticribra,the suite of features found in Lineaperpetua differentiate it from these other genera.These molecular data and morphological characters suggest an affinity of the new genus to the Thalassiosiraceae.

A News Media Bias and Factuality Profiling Framework Assisted by Modeling Correlation

News media profiling is helpful in preventing the spread of fake news at the source and maintaining a good media and news ecosystem.Most previous works only extract features and evaluate media from one dimension independently,ignoring the interconnections between different aspects.This paper proposes a novel news media bias and factuality profiling framework assisted by correlated features.This framework models the relationship and interaction between media bias and factuality,utilizing this relationship to assist in the prediction of profiling results.Our approach extracts features independently while aligning and fusing them through recursive convolu-tion and attention mechanisms,thus harnessing multi-scale interactive information across different dimensions and levels.This method improves the effectiveness of news media evaluation.Experimental results indicate that our proposed framework significantly outperforms existing methods,achieving the best performance in Accuracy and F1 score,improving by at least 1%compared to other methods.This paper further analyzes and discusses based on the experimental results.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

A new infrageneric classification of Gastrochilus(Orchidaceae:Epidendroideae)based on molecular and morphological data

Gastrochilus is an orchid genus containing 73 species of mainly epiphytic on trees or rocks in mountain forests of tropical and subtropical Asia.Previous phylogenetic analyses and morphological assessments have failed to produce a well-resolved phylogeny at the infrageneric level.In the present study,a new infrageneric classification of Gastrochilus is proposed based on thoroughly morphological and phylogenetic analyses based on 52 species.Our phylogenetic analysis divided the genus into six sections including three new sections,G.sect.Pseudodistichi,G.sect.Brachycaules and G.sect.Acinacifolii.We also reinstate G.suavis to the specific rank.Furthermore,two new species,G.armeniacus Jun Y.Zhang,B.Xu&Yue H.Cheng and G.minjiangensis Jun Y.Zhang,B.Xu&Yue H.Cheng,are described and illustrated.A key to six sections of the genus is presented.

Taxonomy of Two New Peritrich Ciliates(Protozoa:Ciliophora)from Inland Saltwaters of China

The inland saltwater lakes harbor exceptional biodiversity.Here,two new species of solitary sessile peritrich ciliates were isolated from Qinghai Lake,the largest inland saltwater lake in China.Their morphology,ciliature,silverline system,and molecular phylogeny were investigated based on live observation,silver staining,and analysis of the small subunit ribosomal DNA(SSU rDNA).Vorticella paraglobosa sp.n.is characterized mainly by its obconical or elongate bell-shaped zooid,C-shaped macronucleus,single ventrally located contractile vacuole,two-rowed infundibular polykinety 3,and 28-38 silverlines between peristome and aboral tro-chal band and 10-15 between aboral trochal band and scopula.Vorticella cotyliformis sp.n.differs from its congeners mainly by its double-layered peristomial lip,cup-shaped zooid,J-shaped macronucleus,single ventrally positioned contractile vacuole,three-rowed infundibular polykinety 3,and 70-85 silverlines between peristome and aboral trochal band and 21-25 between aboral trochal band and scopula.The SSU rDNA sequences of the two new species were obtained,and the subsequent molecular phylogenetic analysis supported their taxonomic classification.