基于多任务学习的同行评审细粒度情感分析模型

学术论文同行评审能够直接反映审稿人对论文的主观评价,对审稿文本进行情感分析有利于挖掘审稿人对论文多维度的评价信息。现有的情感分析模型仅能挖掘专家单一的评审维度和相应的情感倾向,本文提出了一种基于多任务学习的同行评审细粒度情感分析模型。该模型在多任务学习框架下,通过在BERT-LCF模型的基础上增加BiLSTM-CRF模块,使其具备了同时完成属性词抽取和细粒度情感分析任务的能力。与传统的基于Pipeline模式的单任务细粒度情感分析模型相比,本模型在保证精度的情况下可以同时完成评审属性提取和情感分析任务。在这两项任务中,所提出模型的F1分数分别达到了89.01%和90.71%。对比实验证明,在多任务场景下,引入BiLSTM-CRF模块对评审文本属性词提取任务有一定的提升作用。

Research on Multi-modal In-Vehicle Intelligent Personal Assistant Design

Intelligent personal assistants play a pivotal role in in-vehicle systems,significantly enhancing life efficiency,driving safety,and decision-making support.In this study,the multi-modal design elements of intelligent personal assistants within the context of visual,auditory,and somatosensory interactions with drivers were discussed.Their impact on the driver’s psychological state through various modes such as visual imagery,voice interaction,and gesture interaction were explored.The study also introduced innovative designs for in-vehicle intelligent personal assistants,incorporating design principles such as driver-centricity,prioritizing passenger safety,and utilizing timely feedback as a criterion.Additionally,the study employed design methods like driver behavior research and driving situation analysis to enhance the emotional connection between drivers and their vehicles,ultimately improving driver satisfaction and trust.

英语教学也关“情”

教育的本质是教书育人,是培养人,是把学生从一个自然发展的人变成为一个社会人。英语作为一种交流的工具,在教学中多关注"人情",对学生健康人格的培养,至关重要。学习英语难,但学习做人更难。让我们少一些枯燥的说教,多一些人文的渗透与关怀,关注学生的内心世界,进行人性化的教育,让他们在爱的世界里学习。

“情”“导”“评”--新时代中职思想政治教学三部曲

思想政治教育是学校工作的生命线。文章根据新时代思政教育的新特点、新要求,立足中职教学实践,"以学生发展为主体",从"情""导""评"等三方面展开探究,以期优化思政课堂,发挥其更为强大的"能量枢纽"作用。

A novel speech emotion recognition algorithm based on combination of emotion data field and ant colony search strategy

In order to effectively conduct emotion recognition from spontaneous, non-prototypical and unsegmented speech so as to create a more natural human-machine interaction; a novel speech emotion recognition algorithm based on the combination of the emotional data field （EDF） and the ant colony search （ACS） strategy, called the EDF-ACS algorithm, is proposed. More specifically, the inter- relationship among the turn-based acoustic feature vectors of different labels are established by using the potential function in the EDF. To perform the spontaneous speech emotion recognition, the artificial colony is used to mimic the turn- based acoustic feature vectors. Then, the canonical ACS strategy is used to investigate the movement direction of each artificial ant in the EDF, which is regarded as the emotional label of the corresponding turn-based acoustic feature vector. The proposed EDF-ACS algorithm is evaluated on the continueous audio）＇visual emotion challenge （AVEC） 2012 dataset, which contains the spontaneous, non-prototypical and unsegmented speech emotion data. The experimental results show that the proposed EDF-ACS algorithm outperforms the existing state-of-the-art algorithm in turn-based speech emotion recognition.

Support vector machines for emotion recognition in Chinese speech

Support vector machines (SVMs) are utilized for emotion recognition in Chinese speech in this paper. Both binary class discrimination and the multi class discrimination are discussed. It proves that the emotional features construct a nonlinear problem in the input space, and SVMs based on nonlinear mapping can solve it more effectively than other linear methods. Multi class classification based on SVMs with a soft decision function is constructed to classify the four emotion situations. Compared with principal component analysis (PCA) method and modified PCA method, SVMs perform the best result in multi class discrimination by using nonlinear kernel mapping.

Emotional speaker recognition based on prosody transformation

A novel emotional speaker recognition system （ESRS） is proposed to compensate for emotion variability. First, the emotion recognition is adopted as a pre-processing part to classify the neutral and emotional speech. Then, the recognized emotion speech is adjusted by prosody modification. Different methods including Gaussian normalization, the Gaussian mixture model （GMM） and support vector regression （SVR） are adopted to define the mapping rules of F0s between emotional and neutral speech, and the average linear ratio is used for the duration modification. Finally, the modified emotional speech is employed for the speaker recognition. The experimental results show that the proposed ESRS can significantly improve the performance of emotional speaker recognition, and the identification rate （IR） is higher than that of the traditional recognition system. The emotional speech with F0 and duration modifications is closer to the neutral one.

Auditory attention model based on Chirplet for cross-corpus speech emotion recognition

To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.

Speech emotion recognition via discriminant-cascading dimensionality reduction

In order to accurately identify speech emotion information, the discriminant-cascading effect in dimensionality reduction of speech emotion recognition is investigated. Based on the existing locality preserving projections and graph embedding framework, a novel discriminant-cascading dimensionality reduction method is proposed, which is named discriminant-cascading locality preserving projections （DCLPP）. The proposed method specifically utilizes supervised embedding graphs and it keeps the original space for the inner products of samples to maintain enough information for speech emotion recognition. Then, the kernel DCLPP （KDCLPP） is also proposed to extend the mapping form. Validated by the experiments on the corpus of EMO-DB and eNTERFACE＇05, the proposed method can clearly outperform the existing common dimensionality reduction methods, such as principal component analysis （PCA）, linear discriminant analysis （LDA）, locality preserving projections （LPP）, local discriminant embedding （LDE）, graph-based Fisher analysis （GbFA） and so on, with different categories of classifiers.

Affective Factors in English Teaching

Affective factors and cognitive factors are two important parts of English learning, However, English teaching in recent years have taken into special consideration the necessity of knowledge and intelligence and comparatively ignored the development of affection. It may greatly reduce students＇ English learning capacity and limit the whole development of students. This paper will discuss the definition and the importance of affection and some main affective factors, and illustrate some implications of how to implement practical affective activities.

Speech emotion recognition using semi-supervised discriminant analysis

Semi-supervised discriminant analysis SDA which uses a combination of multiple embedding graphs and kernel SDA KSDA are adopted in supervised speech emotion recognition.When the emotional factors of speech signal samples are preprocessed different categories of features including pitch zero-cross rate energy durance formant and Mel frequency cepstrum coefficient MFCC as well as their statistical parameters are extracted from the utterances of samples.In the dimensionality reduction stage before the feature vectors are sent into classifiers parameter-optimized SDA and KSDA are performed to reduce dimensionality.Experiments on the Berlin speech emotion database show that SDA for supervised speech emotion recognition outperforms some other state-of-the-art dimensionality reduction methods based on spectral graph learning such as linear discriminant analysis LDA locality preserving projections LPP marginal Fisher analysis MFA etc. when multi-class support vector machine SVM classifiers are used.Additionally KSDA can achieve better recognition performance based on kernelized data mapping compared with the above methods including SDA.

Dimensional emotion recognition in whispered speech signal based on cognitive performance evaluation

The cognitive performance-based dimensional emotion recognition in whispered speech is studied.First,the whispered speech emotion databases and data collection methods are compared, and the character of emotion expression in whispered speech is studied,especially the basic types of emotions.Secondly,the emotion features for whispered speech is analyzed,and by reviewing the latest references,the related valence features and the arousal features are provided. The effectiveness of valence and arousal features in whispered speech emotion classification is studied.Finally,the Gaussian mixture model is studied and applied to whispered speech emotion recognition. The cognitive performance is also considered in emotion recognition so that the recognition errors of whispered speech emotion can be corrected.Based on the cognitive scores,the emotion recognition results can be improved.The results show that the formant features are not significantly related to arousal dimension,while the short-term energy features are related to the emotion changes in arousal dimension.Using the cognitive scores,the recognition results can be improved.

Annoyance-type speech emotion detection in working environment

In order to recognize people＇s annoyance emotions in the working environment and evaluate emotional well- being, emotional speech in a work environment is induced to obtain adequate samples of emotional speech, and a Mandarin database with two thousands samples is built. In searching for annoyance-type emotion features, the prosodic feature and the voice quality feature parameters of the emotional statements are extracted first. Then an improved back propagation （BP） neural network based on the shuffled frog leaping algorithm （SFLA） is proposed to recognize the emotion. The recognition capability of the BP, radical basis function （RBF） and the SFLA neural networks are compared experimentally. The results show that the recognition ratio of the SFLA neural network is 4. 7% better than that of the BP neural network and 4. 3% better than that of the RBF neural network. The experimental results demonstrate that the random initial data trained by the SFLA can optimize the connection weights and thresholds of the neural network, speed up the convergence and improve the recognition rate.

Novel feature fusion method for speech emotion recognition based on multiple kernel learning

In order to improve the performance of speech emotion recognition, a novel feature fusion method is proposed. Based on the global features, the local information of different kinds of features is utilized. Both the global and the local features are combined together. Moreover, the multiple kernel learning method is adopted. The global features and each kind of local feature are respectively associated with a kernel, and all these kernels are added together with different weights to obtain a mixed kernel for nonlinear mapping. In the reproducing kernel Hilbert space, different kinds of emotional features can be easily classified. In the experiments, the popular Berlin dataset is used, and the optimal parameters of the global and the local kernels are determined by cross-validation. After computing using multiple kernel learning, the weights of all the kernels are obtained, which shows that the formant and intensity features play a key role in speech emotion recognition. The classification results show that the recognition rate is 78. 74% by using the global kernel, and it is 81.10% by using the proposed method, which demonstrates the effectiveness of the proposed method.

Detecting abnormalities for empty nest elder in smart monitoring system

In order to implement the real-time detection of abnormality of elder and devices in an empty nest home,multi-modal joint sensors are used to collect discrete action sequences of behavior,and the improved hierarchical hidden Markov model is adopted to Abstract these discrete action sequences captured by multi-modal joint sensors into an occupant’s high-level behavior—event,then structure representation models of occupant normality are modeled from large amounts of spatio-temporal data. These models are used as classifiers of normality to detect an occupant’s abnormal behavior.In order to express context information needed by reasoning and detection,multi-media ontology （MMO） is designed to annotate and reason about the media information in the smart monitoring system.A pessimistic emotion model （PEM） is improved to analyze multi-interleaving events of multi-active devices in the home.Experiments demonstrate that the PEM can enhance the accuracy and reliability for detecting active devices when these devices are in blind regions or are occlusive. The above approach has good performance in detecting abnormalities involving occupants and devices in a real-time way.

An Explorative Research into the Factors Affecting Chinese Students＇ Retention and Application of English New Words

Vocabulary has a direct impact on people＇s communication competence. A good mastery of an English word covers its pronunciation, spelling, part of speech, meaning and application. This article will focus only on factors affecting the retention and application of a word from the perspective of semantic disparities, different defining angles in English and Chinese, emotional coloring and styles, morphological changes, and collocation. The author hopes that this article will be able to provide some insights into vocabulary teaching.

Adjustment to affective factors in English learning by using Internet English Curriculum Resource

The new curriculum standard points out that affection is one of the most important goals of fundamental education. The non-target language environment is easier to cause the affective change of middle school students who are changeable in their affective state. Based on the affective filter hypothesis, this paper deals with the adjustment to affective factors in English learning by using Internet English Curriculum Resource, such as attitude and motivation, anxiety and inhibition, self-esteem and self-confidence. At last, some suggestions are offered to judge Internet English Curriculum Resource.

RIGHT INFERIOR FRONTAL GYRUS PROCESSING OF VISUAL EROTIC INFORMATION BASED ON fMRI

Visually-induced erotic arousal evoked by pornographic visual stimuli, such as films or photographs, is a common occurrence in human behavior. The brain activation associated with visual erotic stimuli in heterosexual right handed females is studied. Functional magnetic resonance imaging is used to investigate 15 female partici- panterotic arousal induced by visual stimuli in film and picture forms, respectively, performing three or more times during their menstrual cycle on a 3.0T magnetic resonance imaging scanner. There is activation of a set of bilateral brain areas, including the inferior lateral occipital cortex, the anterior supramarginal gyrus, the parietal operculum cortex, the superior parietal lobules, the right inferior frontal gyrus, the cerebellum, the hypothalamus, the thalamus, the hippocampus, and the mid-brain. From different regions, the brain activation is observed and the inferior frontal gyrus has found to be task-independent. Furthermore, the right inferior frontal gyrus has more activation than the left inferior frontal gyrus. The result shows that the right inferior frontal gyrus plays an important role in pornographic information processing rather than being activated stimuli property specific. It is presented for the first time that the functional laterization of the inferior frontal gyrus is bi-directional rather than single （left） directional.

On the mode of CRI English assisting college English teaching

The English News of CRI possesses many advantages such as abundance of information and being easily accessible for English learners within the mainland of China. Unfortunately, it has been ignored by most teachers of English and students. Based on the input hypothesis, affective filter hypothesis and schemata theory, this paper probes the mode of CRI English assisting English teaching in college.

Emotional State Transition Model Based on Stimulus and Personality Characteristics

In order to use mathematical methods to study how cognitive reappraisal strategies affect the output state of emotions,Gross's cognitive reappraisal strategy is transformed into a quantitative parameter which is proposed to describe the general perception of emotional events on the basis of the emotion regulation.According to Gross's emotional regulation model,the Finite State Machine(FSM) model is used for describing the process of emotional state transition and the Likert 5 grading scale is introduced to study the level of an individual's reappraisal according to the participant's self-evaluation.The experimental results verify that the algorithm can effectively describe the relationship between the reappraisal strategy,emotional events and an emotiongenerative process.There are multiple dimensions of a human's emotional state.Thus,in the field of human-computer interaction,further research requires the development of a specific algorithm which can be implemented by a computer for the emotion regulation process.