Expression Classification Research Articles

ТАКСОНОМИЯ ПАРЕМИЙ В КИТАЙСКОМ ЯЗЫКОВЕДЕНИИ: СТРУКТУРНО-СЕМАНТИЧЕСКИЕ И ЛИНГВОПРАГМАТИЧЕСКИЕ АСПЕКТЫ

The article systematizes linguistic approaches to the classification of Chinese language utterances and ready-made expressions. Stable expressions, known in Chinese linguistics as shuyu, are an integral part of language and culture. Based on the analysis of theoretical works on Chinese paremiology, as well as the author's observation of linguistic facts, the formal-structural and speech features of several varieties of shuyu (yanyu, suyu, sehoyu, chengyu, guanyungyu) are considered, the linguapragmatic properties of each of these types of speech expressions are described, and specific examples are considered. The results of the study contribute to the modern theory of Chinese paremiology, and will also be useful as a theoretical source for comparative studies of Chinese and Russian paremias.

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

A Novel Deep Learning-Based Model for Classification of Wheat Gene Expression

THE ARTISTIC FEATURES AND STYLISTIC EFFECTS OF TRANSLATION OF CHINESE AND KAZAKH PROVERBS

This study delves into the extensive body of research on proverbs in both Chinese and Kazakh languages. Scholars examining Chinese proverbs often approach the categorization and conceptual distinctions from three primary perspectives: 1) proverbs and sayings are grouped under the broader concept of "common sayings"; 2) within the concept of "proverbs," both terms are encompassed under "folk speech"; and 3) in translation, "proverbs" correspond to the Chinese term for proverbs, while "common sayings" align with the concept of proverbs as understood in Kazakh. Our research adopts the third perspective. Additionally, we explore the classification of Chinese expressions known as "歇后语" (Afterwords), which translates into Kazakh as "аллегориялық астарлы сөз" (metaphorical word). Unlike Chinese, where proverbs and proverbs are distinct, Kazakh language sources such as books, collections, and dictionaries often conflate the two, distinguishing proverbs with dashes and writing proverbs as a single unit without commas. Therefore, distinguishing between proverbs, sayings, and metaphors is crucial for accurate translation and understanding in both languages.

Classification of gene expression from RNA-seq data for pancreatic cancer prognosis using ensemble learning

Classification of gene expression from RNA-seq data for pancreatic cancer prognosis using ensemble learning

Completed Robust Local Binary Pattern Texture Descriptor for Classification of Facial Expression

Completed Robust Local Binary Pattern Texture Descriptor for Classification of Facial Expression

Microarray Gene Expression Dataset Feature Selection and Classification with Swarm Optimization to Diagnosis Diseases

Microarray Gene Expression Dataset Feature Selection and Classification with Swarm Optimization to Diagnosis Diseases

Discussion on the Deep Integration of College Students’ Innovation and Entrepreneurship and College Students’ Civic and Political Education under the Background of Data Technology

Abstract Currently, most of the studies related to the integration of innovation and entrepreneurship education into the ideological and political work of college students are theoretical discussions, lacking empirical evidence and rarely quantitative analysis. Based on data technology, this paper explores the deep integration of innovation and entrepreneurship with college students’ ideological and political education. After mining the significance of the integration of innovation and entrepreneurship and college students’ civic and political education, the study utilizes the Bayesian theory and face recognition model to assess the effect of the deep integration of civic innovation and entrepreneurship education from the perspective of emotional change. Its algorithmic model, which is mainly based on classification criteria, recognizes students’ classroom expressions according to the classroom expression classification criteria and the emotion score calculation formula so as to judge the effect of students’ classroom listening. Through empirical analysis, all dimensions of ideological and political education self-efficacy and entrepreneurial values of innovation and entrepreneurship are significantly positively correlated at the level of 0.01. The average value of emotion scores of innovation and entrepreneurship education classes integrating ideological and political education is higher than that of traditional innovation and entrepreneurship classes by 0.04 and by 4 more students. The innovative entrepreneurship classroom integrated with Civic and Political Education has achieved good teaching results among college students.

Relationship-Guided Knowledge Transfer for Class-Incremental Facial Expression Recognition.

Human emotions contain both basic and compound facial expressions. In many practical scenarios, it is difficult to access all the compound expression categories at one time. In this paper, we investigate comprehensive facial expression recognition (FER) in the class-incremental learning paradigm, where we define well-studied and easily-accessible basic expressions as initial classes and learn new compound expressions incrementally. To alleviate the stability-plasticity dilemma in our incremental task, we propose a novel Relationship-Guided Knowledge Transfer (RGKT) method for class-incremental FER. Specifically, we develop a multi-region feature learning (MFL) module to extract fine-grained features for capturing subtle differences in expressions. Based on the MFL module, we further design a basic expression-oriented knowledge transfer (BET) module and a compound expression-oriented knowledge transfer (CET) module, by effectively exploiting the relationship across expressions. The BET module initializes the new compound expression classifiers based on expression relevance between basic and compound expressions, improving the plasticity of our model to learn new classes. The CET module transfers expression-generic knowledge learned from new compound expressions to enrich the feature set of old expressions, facilitating the stability of our model against forgetting old classes. Extensive experiments on three facial expression databases show that our method achieves superior performance in comparison with several state-of-the-art methods.

Automated identification of protein expression intensity and classification of protein cellular locations in mouse brain regions from immunofluorescence images.

Knowledge of protein expression in mammalian brains at regional and cellular levels can facilitate understanding of protein functions and associated diseases. As the mouse brain is a typical mammalian brain considering cell type and structure, several studies have been conducted to analyze protein expression in mouse brains. However, labeling protein expression using biotechnology is costly and time-consuming. Therefore, automated models that can accurately recognize protein expression are needed. Here, we constructed machine learning models to automatically annotate the protein expression intensity and cellular location in different mouse brain regions from immunofluorescence images. The brain regions and sub-regions were segmented through learning image features using an autoencoder and then performing K-means clustering and registration to align with the anatomical references. The protein expression intensities for those segmented structures were computed on the basis of the statistics of the image pixels, and patch-based weakly supervised methods and multi-instance learning were used to classify the cellular locations. Results demonstrated that the models achieved high accuracy in the expression intensity estimation, and the F1 score of the cellular location prediction was 74.5%. This work established an automated pipeline for analyzing mouse brain images and provided a foundation for further study of protein expression and functions.

Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance

Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76–0.90) with overall accuracy of 81.6% (95% CI 72.7–88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Tracking and Detecting Depression Level using Facial Recognition and PEN&IQ Test

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications including, but not limited to, human behaviour understanding, detection of mental disorders, and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in four-stages consisting of pre-processing, face detection, feature extraction, and expression classification. The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control

Application of Multi-sensory interaction design Based on Machine Learning in Virtual Reality

Virtual reality (VR) technology is well-known to the public for its unparalleled sense of environmental immersion, which is accompanied by a large amount of human-computer interaction between users and objects in the virtual environment. Users transmit and obtain information through their senses, so the processing mechanism of information in various senses under the background of VR technology has become the foundation of research. In the process of natural information dissemination, users’ senses are synergistically involved, so whether there is a correlation between various senses that affects users’ final perception has become an important theoretical support for studying multi-sensory interaction. Facial expression recognition is an important component of multi-sensory interaction technology. Therefore, this article mainly uses machine learning algorithms to achieve real-time detection of facial targets in images or videos, as well as classification and recognition of facial expressions. To meet the task requirements of the expression recognition module, this article selects the DeepID network model based on deep learning in the field of facial recognition as the basic model for the expression recognition function. This study utilizes the excellent feature extraction ability of the original network for facial images and introduces residual modules and attention mechanisms, making the modified network model more suitable for facial expression multi classification recognition tasks. The recognition accuracy of the facial expression classification algorithm proposed by this work reaches 97.2%.

Comparative Study of K-NN, Naive Bayes and SVM for Face Expression Classification Techniques

Abstract Classification is a data mining technique used to predict group membership for data instances within a given dataset. Classification is considered as an example of supervised learning as training data associated with class labels is given as input. This comparative study focuses on the application of K-Nearest Neighbors (K-NN), Naive Bayes, and Support Vector Machines (SVM) classification techniques in the context of face expression recognition. The study aims to evaluate and compare the performance of these techniques in accurately classifying facial expressions, their advantages and disadvantages. Facial expression recognition is an important area in computer vision and emotion analysis. It involves identifying and classifying different facial expressions such as happiness, sadness, anger, fear, surprise and disgust. Accurate classification of facial expressions is crucial for various applications, including human-computer interaction, affective computing and psychological research. K-NN is a non-parametric algorithm that classifies data based on the majority class of its K-nearest neighbors. Naive Bayes is a probabilistic algorithm that makes classification decisions based on the Bayes theorem and assumes independence between features. SVM is a machine learning algorithm that constructs a hyperplane to separate different classes of data. Dataset such as Affectnet is used to conduct the study including labeled facial expression images. Three algorithms are tested on this dataset to evaluate their performance in correctly classifying facial expressions. Performance metrics such as accuracy, precision, recall, and F1-score are used to assess the classification performance of each technique. Software used for conducting the experiments is Python.

Interpreting Convolutional Layers in DNN Model Based on Time–Frequency Representation of Emotional Speech

Abstract The paper describes the relations of speech signal representation in the layers of the convolutional neural network. Using activation maps determined by the Grad-CAM algorithm, energy distribution in the time–frequency space and their relationship with prosodic properties of the considered emotional utterances have been analysed. After preliminary experiments with the expressive speech classification task, we have selected the CQT-96 time–frequency representation. Also, we have used a custom CNN architecture with three convolutional layers in the main experimental phase of the study. Based on the performed analysis, we show the relationship between activation levels and changes in the voiced parts of the fundamental frequency trajectories. As a result, the relationships between the individual activation maps, energy distribution, and fundamental frequency trajectories for six emotional states were described. The results show that the convolutional neural network in the learning process uses similar fragments from time–frequency representation, which are also related to the prosodic properties of emotional speech utterances. We also analysed the relations of the obtained activation maps with time-domain envelopes. It allowed observing the importance of the speech signals energy in classifying individual emotional states. Finally, we compared the energy distribution of the CQT representation in relation to the regions’ energy overlapping with masks of individual emotional states. In the result, we obtained information on the variability of energy distributions in the selected signal representation speech for particular emotions.

POLE2 Regulates Apoptosis of Oral Squamous Cell Carcinoma Cells through the PI3K/AKT Signaling Pathway.

Oral squamous cell carcinoma (OSCC) is the most common malignant tumor of the head and neck, but its occurrence and progression mechanisms remain unclear. In addition-there is a lack of effective targeting drugs. The second major subunit of DNA polymerase (POLE2) catalyzes the prolongation of new strand replication and modifies exonuclease domain activity. Our previous study found that POLE2 was associated with OSCC progression, but the mechanism remains unclear. The expression of POLE2 in OSCC tissues was detected using immunological assays. Mann-Whitney U analysis was used to investigate the relationship between POLE2 gene expression and tumor classification and prognosis of OSCC. POLE2 expression was inhibited in OSCC cells, and the effects of gene and protein expression were detected using RT-PCR and Western blotting. The POLE2 knockout model was constructed by transfecting a lentiviral vector. Cell proliferation, apoptosis, and migration were detected using various assays including colony formation, MTT, flow cytometry, wound healing assay, Transwell assay, and the Human Apoptosis Antibody Array. The animal model of OSCC was established by subcutaneous injection of transfected HN6 into 4-week-old female nude mice. After 30 days, tumors were removed under anesthesia and tumor weight and dimension were recorded. Tumor cell proliferation was analyzed using Ki67 staining. POLE2 gene levels were significantly higher in the OSCC tissues than in the normal tissues. In addition, POLE2 gene levels were statistically correlated with tumor classification and prognosis. Silencing POLE2 inhibited the proliferation of oral cancer cells and promoted apoptosis in vitro. Animal experiments also supported a positive correlation between POLE2 and OSCC tumor formation. We further demonstrated that POLE2 could upregulate the expression of apoptosis-related proteins such as caspase-3, CD40, CD40L, DR6, Fas, IGFBP-6, p21, and SMAC. In addition, POLE2 regulated OSCC development by inhibiting the PI3K/AKT signaling pathway. POLE2 is closely related to the progression of OSCC. Thus, POLE2 may be a potential target for OSCC treatment.

PD-L1-expressing natural killer cells predict favorable prognosis and response to PD-1/PD-L1 blockade in neuroblastoma

ABSTRACT T/NK cell-based immunotherapy has achieved remarkable success in adult cancers but has limited efficacy in pediatric malignancies including high-risk neuroblastoma (NB). Immune defects of NB tumor microenvironment are poorly understood compared with adults. Here, we described the unique characteristics of NB immune contexture and determined the phenotype signatures of PD-L1-expressing CD8+ T and NK cells in NB tumors by systemically analyzing the spatial distribution of T and NK cells and the distinct expression of programmed death 1 (PD-1) and its ligand (PD-L1) in patients with NB. We found that PD-L1-expressing CD8+ T and NK cells in NB tumors were highly activated and functionally competent and associated with better clinical outcomes. Intratumoral NK cells were a favorable prognostic biomarker independent of CD8+ T cells, PD-1/PD-L1 expression, tumor stage, MYCN amplification, and risk classification. NK cells combined with anti-PD-1/PD-L1 antibodies showed potent antitumor activity against both MYCN-amplified and non-amplified NBs in vitro and in vivo, and PD-L1-expressing NK cells associated with improved antitumor efficacy. Collectively, we raise novel insights into the role of PD-L1 expression on CD8+ T-cell and NK-cell activation. We highlight the great potential of intratumoral NK cells in better defining risk stratification, and predicting survival and response to anti-PD-1/PD-L1 therapy in NB. These findings explain why single anti-PD-1/PD-L1 therapy may not be successful in NB, suggesting its combination with NK cell-adoptive cellular therapy as a promising strategy for relapsing/refractory NB. This study provides a potential prospect that patients with PD-L1-expressing NK cells may respond to anti-PD-1/PD-L1 therapy.

CNN combined with attention module for facial expression recognition

In contemporary times, the proliferation of information technology has prompted an increased interest among scholars towards artificial intelligence, particularly its constituent algorithms, such as deep learning, multilayer perceptron and convolutional neural networks. Of specific interest is the analysis of facial expressions, which has emerged as a popular research topic. However, classifying facial expressions presents a significant challenge due to variations in expressions associated with different emotions, as well as similarities between the various emotions. The task of expression classification is further complicated by the abundance of facial features that must be considered. In this paper, the basic Convolutional Neural Network (CNN) model was first trained on the FRE-2013 face expression dataset and optimized by adding an attention module to learn and analyse the key features of faces, thereby improving the classification accuracy of the model. In short, this model achieved an accuracy of 71.65% and an F1-Score of 0.66 while the model without the attention module only achieved a model accuracy of 69.86% and an F1-Score of 0.65, an improvement of about 2%. The analysis demonstrated that by adding the attention mechanism, some important features e.g. the eyes and mouth, are given more weight, thus improving the classification accuracy.

Neuromarketing: Evaluating Consumer Emotions and Preferences to Improve Business Marketing Management

The invention of neuroscience has benefited medical practitioners and businesses in improving their management and leadership. Neuromarketing, a field that combines neuroscience and marketing, helps businesses understand consumer behaviour and how they respond to advertising stimuli. This study aims to investigate the consumer purchase intention and preferences to improve the marketing management of the brand, based on neuroscientific tools such as emotional arousal using Galvanic Skin Response (GSR) sensors, eye-tracking, and emotion analysis through facial expressions classification. The stimuli for the experiment are two advertisement videos from the Macau tea brand “Guanding Teahouse” followed by a survey. The experiment was conducted on 40 participants. 76.2% of participants that chose the same product in the first survey responded with the same choice of products in the second survey. The GSR peaks in video ad 1 measured a total of 60. On the other hand, video ad 2 counted a total of 55 GSR peaks. The emotions in ad1 and ad2 have similar responses, with an attention percentage of 76%. The results showed that ad1 has a higher engagement time of 11.1% and ad2 has 9.6%, but only 19 of the respondent’s conducted engagement in video ad1, and 31 showed engagement in video ad2. The results demonstrated that although ad 1 has higher engagement rates, the respondents are more attracted to video ad 2. Therefore, ad2 has better marketing power than ad 1. Overall, this study bridges the gap of no previous research on measuring tea brand advertisements with the neuroscientific method. The results provide valuable insights for marketers to develop better advertisements and marketing campaigns and understand consumer preferences by personalising and targeting advertisements based on consumers' emotional responses and behaviour of consumers' purchase intentions. Future research could explore advertisements targeting different demographics.

Translocator protein (TSPO) expression in neoplastic cells and tumor-associated macrophages in meningiomas.

Meningiomas are the most common primary intracranial tumors and show extensive infiltration of macrophages. The mitochondrial membrane protein translocator protein (TSPO) has been used as an in vivo marker of microglia and macrophage activation to visualize neuroinflammation. However, it is unknown which cell types express TSPO in meningiomas. Immunohistochemistry of 38 WHO grade 1-3 meningiomas was subjected to segmentation and deep learning classification of TSPO expression to either Iba1-positive tumor-associated macrophages (TAMs) or all other (mainly neoplastic) cells. A possible association between clinical data and TSPO expression intensities was also investigated. TAMs accounted for 15.9%-26% of all cells in the meningioma tissue. Mean fluorescence intensity of TSPO was significantly higher in TAMs (p < 0.0001), but the mass of neoplastic cells in the tumors exceeded that of TAMs. Thus, the summed fluorescence intensity of TSPO in meningioma cells was 64.1% higher than in TAMs (p = 0.0003). We observed no correlation between TSPO expression intensity and WHO grade. These results indicate that both macrophage-lineage and neoplastic cells in meningiomas express TSPO and that the SPECT-TSPO signal in meningiomas mainly reflects the latter; TSPO is expressed equally in parenchymal activated and resting macrophage/microglia lineage cells.

HEADSET: Human Emotion Awareness under Partial Occlusions Multimodal DataSET.

The volumetric representation of human interactions is one of the fundamental domains in the development of immersive media productions and telecommunication applications. Particularly in the context of the rapid advancement of Extended Reality (XR) applications, this volumetric data has proven to be an essential technology for future XR elaboration. In this work, we present a new multimodal database to help advance the development of immersive technologies. Our proposed database provides ethically compliant and diverse volumetric data, in particular 27 participants displaying posed facial expressions and subtle body movements while speaking, plus 11 participants wearing head-mounted displays (HMDs). The recording system consists of a volumetric capture (VoCap) studio, including 31 synchronized modules with 62 RGB cameras and 31 depth cameras. In addition to textured meshes, point clouds, and multi-view RGB-D data, we use one Lytro Illum camera for providing light field (LF) data simultaneously. Finally, we also provide an evaluation of our dataset employment with regard to the tasks of facial expression classification, HMDs removal, and point cloud reconstruction. The dataset can be helpful in the evaluation and performance testing of various XR algorithms, including but not limited to facial expression recognition and reconstruction, facial reenactment, and volumetric video. HEADSET and its all associated raw data and license agreement will be publicly available for research purposes.