Multimodal Sentiment Analysis Based on a Cross-Modal Multihead Attention Mechanism

Multimodal sentiment analysis aims to understand people’s emotions and opinions from diverse data.Concate-nating or multiplying various modalities is a traditional multi-modal sentiment analysis fusion method.This fusion method does not utilize the correlation information between modalities.To solve this problem,this paper proposes amodel based on amulti-head attention mechanism.First,after preprocessing the original data.Then,the feature representation is converted into a sequence of word vectors and positional encoding is introduced to better understand the semantic and sequential information in the input sequence.Next,the input coding sequence is fed into the transformer model for further processing and learning.At the transformer layer,a cross-modal attention consisting of a pair of multi-head attention modules is employed to reflect the correlation between modalities.Finally,the processed results are input into the feedforward neural network to obtain the emotional output through the classification layer.Through the above processing flow,the model can capture semantic information and contextual relationships and achieve good results in various natural language processing tasks.Our model was tested on the CMU Multimodal Opinion Sentiment and Emotion Intensity(CMU-MOSEI)and Multimodal EmotionLines Dataset(MELD),achieving an accuracy of 82.04% and F1 parameters reached 80.59% on the former dataset.

Posture Detection of Heart Disease Using Multi-Head Attention Vision Hybrid(MHAVH)Model

Cardiovascular disease is the leading cause of death globally.This disease causes loss of heart muscles and is also responsible for the death of heart cells,sometimes damaging their functionality.A person’s life may depend on receiving timely assistance as soon as possible.Thus,minimizing the death ratio can be achieved by early detection of heart attack(HA)symptoms.In the United States alone,an estimated 610,000 people die fromheart attacks each year,accounting for one in every four fatalities.However,by identifying and reporting heart attack symptoms early on,it is possible to reduce damage and save many lives significantly.Our objective is to devise an algorithm aimed at helping individuals,particularly elderly individuals living independently,to safeguard their lives.To address these challenges,we employ deep learning techniques.We have utilized a vision transformer(ViT)to address this problem.However,it has a significant overhead cost due to its memory consumption and computational complexity because of scaling dot-product attention.Also,since transformer performance typically relies on large-scale or adequate data,adapting ViT for smaller datasets is more challenging.In response,we propose a three-in-one steam model,theMulti-Head Attention Vision Hybrid(MHAVH).Thismodel integrates a real-time posture recognition framework to identify chest pain postures indicative of heart attacks using transfer learning techniques,such as ResNet-50 and VGG-16,renowned for their robust feature extraction capabilities.By incorporatingmultiple heads into the vision transformer to generate additional metrics and enhance heart-detection capabilities,we leverage a 2019 posture-based dataset comprising RGB images,a novel creation by the author that marks the first dataset tailored for posture-based heart attack detection.Given the limited online data availability,we segmented this dataset into gender categories(male and female)and conducted testing on both segmented and original datasets.The training accuracy of our model reached an impressive 99.77%.Upon testing,the accuracy for male and female datasets was recorded at 92.87%and 75.47%,respectively.The combined dataset accuracy is 93.96%,showcasing a commendable performance overall.Our proposed approach demonstrates versatility in accommodating small and large datasets,offering promising prospects for real-world applications.

Association of Serotonin Receptors with Attention Deficit Hyperactivity Disorder： A Systematic Review and Meta-analysis

Attention deficit hyperactivity disorder （ADHD） is one of the most common mental disorders in childhood, with a high heritability about 60% to 90%. Serotonin is a monoamine neurotransmitter. Numerous studies have reported the association between the serotonin receptor family （5-HTR） gene polymorphisms and ADHD, but the results are still controversial. In this study, we conducted a meta-analysis of the association between 5-HTRIB, 5-HTR2A, and 5-HTR2C genetic variants and ADHD. The results showed that the 861G allele of 5-HTRIB SNP rs6296 could significantly increase the risk of ADHD （OR= 1.09, 95% CI： 1.01-1.18）; the 5-HTR2C gene rs518147 （OR=1.69, 95% CI： 1.38-2.07） and rs3813929 （OR = 1.57, 95% CI： 1.25-1.97） were all associated with the risk of ADHD. In addition, we also carried on a case- control study to explore the relevance between potential candidate genes 5-HTR1A, 5-HTRIE, 5-HTR3A and ADHD. The results indicated that 5-HTRIA rs6295 genotype （CC＋CG vs. GG OR=Z00, 95% CI： 1.23-3.27） and allele （OR=1.77, 95% CI： 1.16-2.72） models were statistically significantly different between case group and control group. This study is the first comprehensive exploration and summary of the association between serotonin receptor family genetic variations and ADHD, and it also provides more evidence for the etiology of ADHD.

ROC and SAT Analysis of Different Grayscale Test Images (Distractors L and Target T) to Customize a Visual-Search Attention Task

Nowadays, there is a great need to investigate the effects of fatigue on physical as well as mental performance. The issues that are generally associated with extreme fatigue are that one can easily lose one’s focus while performing any particular activity whether it is physical or mental and this decreases one’s motivation to complete the task at hand efficiently and successfully. In the same line of thought, myriads of research studies posited the negative effects of fatigue on mental performance, and most techniques to induce fatigue to require normally long-time and repetitive visual search tasks. In this study, a visual search algorithm task was devised and customized using performance measures such as d’ (d-prime) and Speed Accuracy Trade-Off (SATF) as well as ROC analysis for classifier performance. The visual search algorithm consisted of distractors (L) and a target (T) whereby human participants had to press the appropriate keyboard button as fast as possible if they notice a target or not upon presentation of a visual stimulus. It was administered to human participants under laboratory conditions, and the reaction times, as well as accuracy of the participants, were monitored. It was found that the test image Size35Int255 was the best image to be used in terms of sensitivity and AUC (Area under Curve). Therefore, ongoing researches can use these findings to create their visual stimuli in such a way that the target and distractor images follow the size and intensity characteristics as found in this research.

About the structure of posturography: Sampling duration, parametrization, focus of attention (part II)

There exists a great variety of posturographic parameters which complicates the evaluation of center of pressure (COP) data. Hence, recommendations were given to use a set of complementary parameters to explain most of the variance. However, it is unknown whether a dual task paradigm leads to different parametrization sets. On account of this problem an exploratory factor analysis approach was conducted in a dual task experiment. 16 healthy subjects stood on a force plate performing a posture-cognition dual task (DT, focus of attention on a secondary task) with respect to different sampling durations. The subjects were not aware of being measured in contrast to a baseline task condition (BT, internal focus of attention) in the previously published part I. In compareson to BT a different factor loading pattern appears. In addition, factor loadings are strongly affected by different sampling durations. DT reveals a change of factor loading structure with longer sampling durations compared to BT. Specific recommendations concerning a framework of posturographic parametrization are given.

Multimodal Sentiment Analysis Using BiGRU and Attention-Based Hybrid Fusion Strategy

Recently,multimodal sentiment analysis has increasingly attracted attention with the popularity of complementary data streams,which has great potential to surpass unimodal sentiment analysis.One challenge of multimodal sentiment analysis is how to design an efficient multimodal feature fusion strategy.Unfortunately,existing work always considers feature-level fusion or decision-level fusion,and few research works focus on hybrid fusion strategies that contain feature-level fusion and decision-level fusion.To improve the performance of multimodal sentiment analysis,we present a novel multimodal sentiment analysis model using BiGRU and attention-based hybrid fusion strategy(BAHFS).Firstly,we apply BiGRU to learn the unimodal features of text,audio and video.Then we fuse the unimodal features into bimodal features using the bimodal attention fusion module.Next,BAHFS feeds the unimodal features and bimodal features into the trimodal attention fusion module and the trimodal concatenation fusion module simultaneously to get two sets of trimodal features.Finally,BAHFS makes a classification with the two sets of trimodal features respectively and gets the final analysis results with decision-level fusion.Based on the CMU-MOSI and CMU-MOSEI datasets,extensive experiments have been carried out to verify BAHFS’s superiority.

HSCA-Net: A Hybrid Spatial-Channel Attention Network in Multiscale Feature Pyramid for Document Layout Analysis

Document images often contain various page components and complex logical structures,which make document layout analysis task challenging.For most deep learning-based document layout analysis methods,convolutional neural networks(CNNs)are adopted as the feature extraction networks.In this paper,a hybrid spatial-channel attention network(HSCA-Net)is proposed to improve feature extraction capability by introducing attention mechanism to explore more salient properties within document pages.The HSCA-Net consists of spatial attention module(SAM),channel attention module(CAM),and designed lateral attention connection.CAM adaptively adjusts channel feature responses by emphasizing selective information,which depends on the contribution of the features of each channel.SAM guides CNNs to focus on the informative contents and capture global context information among page objects.The lateral attention connection incorporates SAM and CAM into multiscale feature pyramid network,and thus retains original feature information.The effectiveness and adaptability of HSCA-Net are evaluated through multiple experiments on publicly available datasets such as PubLayNet,ICDAR-POD,and Article Regions.Experimental results demonstrate that HSCA-Net achieves state-of-the-art performance on document layout analysis task.

Temporal continuity of visual attention for future gaze prediction in immersive virtual reality

Background Eye tracking te chnology is receiving increased attention in the field of virtual reality.Specifically,future gaze prediction is crucial in pre-computation for many applications such as gaze-contingent rendering,advertisement placement,and content-based design.To explore future gaze prediction,it is necessary to analyze the temporal continuity of visual attention in immersive virtual reality.Methods In this paper,the concept of temporal continuity of visual attention is presented.Subsequently,an autocorrelation function method is proposed to evaluate the temporal continuity.Thereafter,the temporal continuity is analyzed in both free-viewing and task-oriented conditions.Results Specifically,in free-viewing conditions,the analysis of a free-viewing gaze dataset indicates that the temporal continuity performs well only within a short time interval.A task-oriented game scene condition was created and conducted to collect users'gaze data.An analysis of the collected gaze data finds the temporal continuity has a similar performance with that of the free-viewing conditions.Temporal continuity can be applied to future gaze prediction and if it is good,users'current gaze positions can be directly utilized to predict their gaze positions in the future.Conclusions The current gaze's future prediction performances are further evaluated in both free-viewing and task-oriented conditions and discover that the current gaze can be efficiently applied to the task of short-term future gaze prediction.The task of long-term gaze prediction still remains to be explored.

Erratum to “About the Structure of Posturography: Sampling Duration, Parametrization, Focus of Attention (Part II)” [Journal of Biomedical Science and Engineering 5 (2012) 508-516]

After reading the above mentioned article of [1], we identified a mistake considering the results of the paragraph “3.6. Nonlinear Parameters AP” and the related Table 5 (both on p. 512). Unfortunately, published Table 5 is a duplicate of Table 4, and therefore it is not possible for the reader to comprehend any underlying interrelations. To correct this mistake, we would like to offer the corrected table (Table 5) as follows.

About the structure of posturography: Sampling duration, parametrization, focus of attention (part I)

This study investigates the choice of posturographic parameter sets with respect to the influence of different sampling durations (30 s, 60 s, 300 s). Center of pressure (COP) data are derived from 16 healthy subjects standing quietly on a force plate. They were advised to focus on the postural control process ( i.e. internal focus of attention). 33 common linear and 10 nonlinear parameters are calculated and grouped into five classes. Component structure in each group is obtained via exploratory factor analysis. We demonstrate that COP evaluation—irrespective of sampling duration—necessitates a set of diverse parameters to explain more variance of the data. Further more, parameter sets are uniformly invariant towards sampling durations and display a consistent factor loading pattern. These findings pose a structure for COP parametrization. Hence, specific recommendations are preserved in order to avoid redundancy or misleading basis for inter-study comparisons. The choice of 11 parameters from the groups is recommended as a framework for future research in posturography.

The evolution of central environmental protection inspection policy attention in China: An investigation based on inspection reports

The central environmental protection inspection (CEPI) system in China is a significant institutional innova‐tion in national environmental governance. The CEPI applies a joint supervision strategy to address salient en‐vironmental issues and strictly enforce the environmental responsibilities of local governments. This study col‐lects and organizes CEPI inspection reports covering three stages that encompass the first round, the “look back”, and the second round, applying text analysis to obtain sample data and conduct statistical quantifica‐tion of word frequency in inspection reports and identify notable changes. The study explores the allocation of CEPI attention between policy objectives and the intensity of policy instruments. We determine that in con‐junction with public opinion feedback, the CEPI conducts targeted inspections and focuses more on pollutant governance, which has high severity and can be addressed quickly. The CEPI fills the gap of normalized gover‐nance with a campaign-style governance approach. Regarding the intensity of policy measures, the CEPI pri‐marily uses economic incentive policy instruments, supplemented by command-and-control and public guid‐ance approaches, advancing the sustainability of regulatory effectiveness through economic, social, and politi‐cal activities. This study extends knowledge in the field of CEPI policy priorities and implementation, expand‐ing the literature related to outcomes of environmental policy in developing countries.

Analysis on the Formation and Elimination of Ambiguity in English Syntactic Ambiguity from the Cognitive Perspective

English ambiguity expressions have been a heat topic in language research for a long time with a variety of theories and methods.Among them,the cognitive approach,just like the Figure-Ground theory,relevance theory and cognitive context theory,is a relatively new and vigorous perspective.However,as to studying ambiguity from the perspective of attention,very few researches have been done in this regard.By analyzing different types of English ambiguity expressions,it is necessary to explore how ambiguity expressions are formed and eliminated from the attentional view,and to provide some new insights into ambiguity study.

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.

基于BiGRU-attention的中文微博评论情感分析

文本情感分析是自然语言处理中的一项重要任务。近年来,深度学习技术的快速发展使得基于循环神经网络的模型在情感分析任务上取得了显著的进展。文章提出了一种基于门控循环网络(Gate Recurrent Unit,GRU)和注意力机制的情感分析模型,即BiGRU-attention,通过引入注意力机制,使得该模型能够自动学习到每个词汇对情感预测的重要性权重,从而有针对性地关注句子中最具表达力的部分。实验结果表明,所提出的基于BiGRU-attention的模型准确率达到了91.98%,均优于GRU、UCRNN、fastText-BiGRU等对比模型,平均提高了约7.86百分点。

Aspect-Level Sentiment Analysis Incorporating Semantic and Syntactic Information

Aiming at the problem that existing models in aspect-level sentiment analysis cannot fully and effectively utilize sentence semantic and syntactic structure information, this paper proposes a graph neural network-based aspect-level sentiment classification model. Self-attention, aspectual word multi-head attention and dependent syntactic relations are fused and the node representations are enhanced with graph convolutional networks to enable the model to fully learn the global semantic and syntactic structural information of sentences. Experimental results show that the model performs well on three public benchmark datasets Rest14, Lap14, and Twitter, improving the accuracy of sentiment classification.

基于时空规律的PCA-LSTM-Attention空气质量预测研究

空气质量指数(AQI)是考量空气质量好坏的综合指标,由于各地区空气受风向影响不断流动,使传统预测模型难以从时间单一角度进行建模。针对此问题提出一种利用主成分分析(PCA)降维思想,考虑不同地区时空规律的模型。通过收集目标城市和周边几个城市的所需数据,使用PCA求解所有城市的综合空气得分作为空间信息,再输入LSTM提取时间规律,最后通过注意力模块输出AQI预测。通过对沧州、唐山、廊坊、保定和天津的大气污染物和气象数据的分析,证明该算法比只考虑时间因素的LSTM模型、RNN模型和ARIMA(1,1,1)模型精度更高,可以有助于提高AQI预测精度。

融合Attention与改进LSTM的电力工程数据分析算法设计

针对传统数据分析算法处理高维、海量数据过程中出现的低效、准确率差的问题,基于改进LSTM提出了一种电力工程数据分析与预测算法。该算法使用双向LSTM作为训练模型,从而捕捉到更为广泛的上下文信息。对于模型处理高维数据时遇到数据冗余、噪声较高的问题,使用堆叠稀疏自编码器进行输入数据预处理,进而提升了模型的泛化能力,在模型输出部分结合自注意力机制,进一步聚焦关键特征,提高模型在不同序列集中的性能表现。实验结果表明,在所有对比算法中,文中所提算法的误差最低且性能最优,实际数据误差小于5%,满足电力工程的实际应用需求。

基于BERT-Attention-BiLSTM的多特征主题情感分析

关注微博用户对于事件的情感倾向,有利于平台了解用户心声,也能为决策者的舆情处理工作提供参考和方向。然而,当前大部分微博情感分析研究仍是基于文本的,忽略了表情、图片等要素。针对上述问题,本文提出了一个多模型融合的情感分析模型,以BERT预训练模型为基础,融合情感词典,并采用双向LSTM获取文本特征,有效联系前后文,并引入注意力机制,同时提出了一种emoji表情特征计算方法,得到一个情感分类更准确的多特征主题情感分析模型。

基于BERT和层次化Attention的微博情感分析研究

微博情感分析旨在挖掘网民对特定事件的观点和看法,是网络舆情监测的重要内容。目前的微博情感分析模型一般使用Word2Vector或GloVe等静态词向量方法,不能很好地解决一词多义问题;另外,使用的单一词语层Attention机制未能充分考虑文本层次结构的重要性,对句间关系捕获不足。针对这些问题,提出一种基于BERT和层次化Attention的模型BERT-HAN(bidirectional encoder representations from transformers-hierarchical Attention networks)。通过BERT生成蕴含上下文语意的动态字向量;通过两层BiGRU分别得到句子表示和篇章表示,在句子表示层引入局部Attention机制捕获每句话中重要的字,在篇章表示层引入全局Attention机制以区分不同句子的重要性;通过Softmax对情感进行分类。实验结果表明,提出的BERT-HAN模型能有效提升微博情感分析的Macro F1和Micro F1值,具有较大的实用价值。

我国省级层面县域医共体政策注意力演变研究

目的:分析省级县域医共体政策注意力演变过程与规律,以期为后续政策的制定与优化提供参考。方法:将省级县域医共体政策划分为探索期和试点期两个阶段,以及东部、中部、西部3个地区,以政策注意力理论为指导,分析各阶段、各地区的县域医共体政策注意力强度与政策注意力配置,并运用PMC指数模型对不同阶段、不同地区的县域医共体政策文本进行政策注意力综合量化评价。结果:省级县域医共体政策注意力存在时空差异。在时间维度表现为省级政策以中央政策为导向,试点期政策注意力强度大于探索期,政策注意力配置随中央政策注意力的侧重而转移;在空间维度上,省级县域医共体政策注意力水平地域差异性较大,东部省份较高,中部地区次之,西部地区较低。结论:省级县域医共体政策出台一方面要坚持中央导向与地方创新的统一,既要在中央政策的指导下进行政策制定,又要根据地域实际情况进行区域特征性政策创新;另一方面要坚持顶层支持与区域合作的统一,既需要由中央对县域医共体政策注意力水平较低的地区进行垂直的专项支持,又要发挥近邻效应与示范效应的作用进行区域间的平行合作,以提升县域医共体政策水平。