Deep Emotions Research Articles

Pet-like learning companions: past research and future directions

The objective of this paper is to offer a design framework for pet-like learning companions (P-LCSs) whose concept extends from learning companion systems and integrates with pet-like characteristics, such as pet appearance, pet-owner relationship, and emotional support. Such a close relationship and deep emotions toward pets bring significant potential to benefit student learning in terms of social, cognitive, and affective aspects. After introducing some previous work on the P-LCSs, two further directions are proposed. One is linked to the research field of game-based and gamified design, while the other is connected to interest-driven creator (IDC) theory. Finally, a long-term goal of P-LCSs is discussed, and the reasons why P-LCSs might contribute to realizing the goal are also articulated in the paper.

Spiritual Exaltation in Timeless Places of Worship: Insights from the New Maimonides Central Sephardic Synagogue, Hadera, Israel

The deep emotion we feel when entering any timeless place of worship—a synagogue, a church, or a Buddhist temple—stems from the patterns of space that generated them. These are patterns that reflect the innate patterns printed in our minds from the outset and that are thus common to us all as human beings of any culture or religion. In designing the Maimonides Central Sephardic Synagogue I endeavored to capture the timeless spiritual exaltation one experiences in the places of worship where Maimonides prayed, such as the Iben Denan Synagogue in Fez, Morocco or the Ben-Ezra Synagogue in Cairo, Egypt, and to revive the traditional patterns based on Maimonides’ halachic rulings—the laws he set down in his book Hayad Hahazaka, as passed on to me in the synagogues of Tzfat, the holy Kabbalah city of Galilee, birthplace of Judaism’s mystical strand, and hometown of my family since the early nineteenth century.

Parental Burnout—A Model of Risk Factors and Protective Resources Among Mothers of Children with/Without Special Needs

Parents of children With Special Needs and Disabilities (W-SND) who require long-term healthcare are at high risk of Parental Burnout (PB). However, most studies have focused on PB among parents of children Without Special Needs (WO-SN). This study aimed to develop a new model explaining PB of mothers of children W-SND/WO-SN. The main hypothesis was that the nexus of correlations between risk factors of PB (severity of child’s disability/challenge, perceived caregiver burden) and protective resources (social support, learned resourcefulness, deep emotion work) will explain the variance of PB of mothers of children W-SND and WO-SN. A questionnaire assessing PB, its risk factors, and protective resources was completed by 352 Israeli mothers of children W-SND (mean age 36.9) or WO-SN (mean age 32.3). The child’s disabilities were communicative, physical, intellectual and developmental. The main results are that mothers of children W-SND reported higher PB, higher caregiver burden, and a higher severity of disability. About 50% of PB variance was significantly explained by the nexus of correlations between selected risk and protective factors. Among all mothers, the more social support they received, the higher their learned-resourcefulness. However, learned resourcefulness mediates the correlation between caregiver burden and PB among mothers of children W-SND. Accordingly, it is important to increase awareness among healthcare professionals regarding the risk factors and symptoms of PB, and to develop workshops on protective resources in order to prevent PB and promote mothers’ well-being. Further research should be conducted among fathers and parents from diverse cultures.

Deep emotion recognition in textual conversations: a survey

Emotion Recognition in Conversations (ERC) is a key step towards successful human–machine interaction. While the field has seen tremendous advancement in the last few years, new applications and implementation scenarios present novel challenges and opportunities. These range from leveraging the conversational context, speaker, and emotion dynamics modelling, to interpreting common sense expressions, informal language, and sarcasm, addressing challenges of real-time ERC, recognizing emotion causes, different taxonomies across datasets, multilingual ERC, and interpretability. This survey starts by introducing ERC, elaborating on the challenges and opportunities of this task. It proceeds with a description of the emotion taxonomies and a variety of ERC benchmark datasets employing such taxonomies. This is followed by descriptions comparing the most prominent works in ERC with explanations of the neural architectures employed. Then, it provides advisable ERC practices towards better frameworks, elaborating on methods to deal with subjectivity in annotations and modelling and methods to deal with the typically unbalanced ERC datasets. Finally, it presents systematic review tables comparing several works regarding the methods used and their performance. Benchmarking these works highlights resorting to pre-trained Transformer Language Models to extract utterance representations, using Gated and Graph Neural Networks to model the interactions between these utterances, and leveraging Generative Large Language Models to tackle ERC within a generative framework. This survey emphasizes the advantage of leveraging techniques to address unbalanced data, the exploration of mixed emotions, and the benefits of incorporating annotation subjectivity in the learning phase.

Research on artistry and practicability of album design in visual communication

In the field of visual communication, album design, as a unique art form, not only carries the core spirit of music, but also conveys deep emotions and ideas to the audience through its unique visual language. As the intersection of music and art, album design's artistry and practicability have always been the dual goals pursued by designers. Artistry makes album design unique charm and attracts people's attention; Practicality ensures that the design works can effectively convey the musical message and meet the needs of the market. Therefore, to explore the artistry and practicability of album design in visual communication is not only an in-depth study of design aesthetics, but also an accurate grasp of market demand.

MORPHOLOGICAL ANALYSIS OF COMPOUNDING IN THE NOVEL THE BURNING MAZE: THE TRIALS OF APOLLO BY RICK RIORDAN

This research concerned a morphological analysis of compounding within The Burning Maze: The Trials of Apollo by Rick Riordan. This study morphologically described the importance of language in conveying deep emotion and thematic richness. This descriptive qualitative linguistic research employed the observation and auto-expert judgment methods for the data collection and morphological note-taking techniques for the data analysis. The results revealed patterns of word combinations that contributed to the linguistic richness and depth of the themes. The discussion interpreted these findings in the context of the novel’s storytelling, providing further understanding of the role of morphology in character development and plot complexity. The results showed that compounding in The Burning Maze: The Trials of Apollo by Rick Riordan had a combination of elements with the form of the Noun (N) + Verb (V); Noun (N) + Noun (N); Verb (V) + Verb (V); Adjective (Adj) + Noun (N); Verb (V) + Noun (N); Adverb (Adv) + Noun (N); Noun (N) + Adverb (Adv); Adjective (Adj) + Verb (V); Adjective (Adj) + Adjective (Adj); Adverb (Adv) + Adjective (Adj); Verb (V) + Adverb (Adv); Noun (N) + Adjective(Adj); Verb (V) + Adjective (Adj). Besides revealing linguistic nuances in literary works, this research has strived to deepen our understanding of the intricate layers within literature, showcasing how the careful selection of words enhanced the reader’s immersive journey into the literary landscape.

Deep Learning-based Emotion Recognition Algorithms in Music Performance

In the realm of artificial intelligence and musicology, emotion recognition in music performance has emerged as a pivotal area of research. This paper introduces EmoTrackNet, an integrated deep learning framework that combines sparse attention networks, enhanced one-dimensional residual Convolutional Neural Networks (CNNs) with an improved Inception module, and Gate Recurrent Units (GRUs). The synergy of these technologies aims to decode complex emotional cues embedded in music. Our methodology revolves around leveraging the sparse attention network to efficiently process temporal sequences, thereby capturing the intricate dynamics of musical pieces. The incorporation of the 1D residual CNN with an upgraded Inception module facilitates the extraction of nuanced features from audio signals, encompassing a broad spectrum of musical tones and textures. The GRU component further refines the model’s capability to retain and process sequential information over longer timeframes, essential for understanding evolving emotional expressions in music. We evaluated EmoTrackNet on the Soundtrack dataset a comprehensive collection of music pieces annotated with emotional labels. The results demonstrated remarkable improvements in the accuracy of emotion recognition, outperforming existing models. This enhanced performance can be attributed to the integrated approach, which efficiently combines the strengths of each component, leading to a more robust and sensitive emotion detection system. EmoTrackNet’s novel architecture and promising results pave the way for new avenues in musicology, particularly in understanding and interpreting the emotional depth of musical performances. This framework not only contributes significantly to the field of music emotion recognition but also has potential applications in music therapy, entertainment, and interactive media where emotional engagement is key.

Accuracy is not enough: a heterogeneous ensemble model versus FGSM attack

AbstractIn this paper, based on facial landmark approaches, the possible vulnerability of ensemble algorithms to the FGSM attack has been assessed using three commonly used models: convolutional neural network-based antialiasing (A_CNN), Xc_Deep2-based DeepLab v2, and SqueezeNet (Squ_Net)-based Fire modules. Firstly, the three individual deep learning classifier-based Facial Emotion Recognition (FER) classifications have been developed; the predictions from all three classifiers are then merged using majority voting to develop the HEM_Net-based ensemble model. Following that, an in-depth investigation of their performance in the case of attack-free has been carried out in terms of the Jaccard coefficient, accuracy, precision, recall, F1 score, and specificity. When applied to three benchmark datasets, the ensemble-based method (HEM_Net) significantly outperforms in terms of precision and reliability while also decreasing the dimensionality of the input data, with an accuracy of 99.3%, 87%, and 99% for the Extended Cohn-Kanade (CK+), Real-world Affective Face (RafD), and Japanese female facial expressions (Jaffee) data, respectively. Further, a comprehensive analysis of the drop in performance of every model affected by the FGSM attack is carried out over a range of epsilon values (the perturbation parameter). The results from the experiments show that the advised HEM_Net model accuracy declined drastically by 59.72% for CK + data, 42.53% for RafD images, and 48.49% for the Jaffee dataset when the perturbation increased from A to E (attack levels). This demonstrated that a successful Fast Gradient Sign Method (FGSM) can significantly reduce the prediction performance of all individual classifiers with an increase in attack levels. However, due to the majority voting, the proposed HEM_Net model could improve its robustness against FGSM attacks, indicating that the ensemble can lessen deception by FGSM adversarial instances. This generally holds even as the perturbation level of the FGSM attack increases.

Komunikasi Tanpa Nama yang Jujur dan Terbuka : Studi Kasus pada Akun @Komunitasmaunangisaja di Twitter (X)

This study analyzes the phenomenon of anonymous communication on the Twitter account (X) @komunitasmaunangisaja and its impact on users' mental health using a transcendental phenomenology approach. Data collection methods include direct observation, content analysis of tweets, and in-depth interviews with account users. Findings indicate that anonymous communication on this account positively impacts mental health by reducing feelings of isolation and enhancing emotional well-being. Users feel more free to express their feelings without fear of judgment, helping to alleviate emotional burdens and gain community support. However, there are also negative impacts, such as dissatisfaction with received responses and the risk of anonymity abuse for negative behavior. The study also reveals that users frequently turn to anonymous communication to cope with stress, anxiety, and depression. This platform provides a space for sharing deep emotions and experiences, facilitating a sense of togetherness and emotional release. Community support helps users feel validated and understood, contributing to overall mental health improvement. This study highlights the importance of such platforms in creating a safe environment for emotional expression, ultimately contributing to better mental well-being, and emphasizes the need for more supportive online communities

Chimp Optimization Algorithm with Deep Learning-Driven Fine-grained Emotion Recognition in Arabic Corpus

Recently, emotion analysis and classification of tweets have become a crucial area of research. The Arabic language had experienced difficulties with emotion classification on Twitter(X), needing preprocessing more than other languages. Emotion detection is a major challenge in Natural Language Processing (NLP), which allows machines to ascertain the emotions expressed in the text. The task includes recognizing and identifying human feelings such as fear, anger, sadness, and joy. The discovered sentiments and feelings expressed in tweets have gained much recognition in recent years. The Arab region has played a substantial role in international politics and the global economy needs to scrutinize the emotions and sentiments in the Arabic language. Lexicon-based and machine-learning techniques are two common models that address the problems of emotion classification. This study introduces a Chimp Optimization Algorithm with a Deep Learning-Driven Arabic Fine-grained Emotion Recognition (COADL-AFER) technique. The presented COADL-AFER technique mainly aims to detect several emotions in Arabic tweets. In addition to its academic significance, the COADL-AFER technique has practical applications in various fields, including enhancing applications of E-learning, aiding psychologists in recognising terrorist performance, improving product quality, and enhancing customer service. The COADL-AFER technique applies the long short-term memory (LSTM) model for emotion detection. Finally, the hyperparameter selection of the LSTM method can be accomplished by COA. The experimental validation of the COADL-AFER system, a crucial step in our research, is verified utilizing the Arabic tweets dataset. The simulation results stated the betterment of the COADL-AFER technique, further reinforcing the reliability of our research.

Research on Deep Learning-based Speech Emotion Recognition System

Research on Deep Learning-based Speech Emotion Recognition System

DEEP LEARNING BASED EMOTION DETECTION SYSTEM

Emotion detection plays a crucial role in human-computer interaction, affective computing, and psychological research. Traditional methods of emotion detection often rely on manual annotations or subjective assessments, which can be time- consuming and prone to biases. In this paper, we propose a Deep Learning-Based Emotion Detection System that leverages advanced neural network architectures to automatically recognize emotions from facial expressions in real-time. The system utilizes convolutional neural networks (CNNs) for feature extraction from facial images and recurrent neural networks (RNNs) for sequence modeling of temporal dynamics in facial expressions. Our proposed system addresses key challenges in emotion detection, including variations in facial expressions, lighting conditions, and occlusions. By training on large-scale datasets of labeled facial images, the system learns to accurately classify emotions into predefined categories such as happiness, sadness, anger, fear, disgust, and surprise. We evaluate the performance of the system using standard metrics such as accuracy, precision, recall, and F1-score, demonstrating its effectiveness in real- world scenarios. The results indicate that the Deep Learning-Based Emotion Detection System achieves state-of-the-art performance in recognizing emotions from facial expressions, outperforming traditional methods. The system's ability to analyze facial dynamics in real-time opens up opportunities for applications in human-computer interaction, virtual reality, mental health monitoring, and personalized user experiences. Keywords: Deep Learning, Emotion Detection, Facial Expression Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Affective Computing. Keywords: emotion Detection, Machine Learning, Classifier, Natural Language Processing

Nostalgia in the entertainment industry: the case of Pokémon Go and The Little Mermaid

Nostalgia-based marketing strategies have garnered significant attention in recent times owing to their ability to evoke deep emotions and influence consumer behaviour. However, the success of nostalgia-based marketing is not a foregone conclusion. The effectiveness of nostalgia depends on the characteristics of the product and the target audience. This study uses qualitative research to explore the boundaries of strong nostalgia-triggered feelings in two different entertainment products: the 2015 video game Pokémon Go and Disney’s 2023 remake of The Little Mermaid. Interpretative analysis revealed two key concepts that are crucial to understanding target audience attitudes and behaviour: habituation and changes in the core features of the original product. A delicate, nuanced balance is required between respect for the essence of the original nostalgic product and the introduction of innovations to prevent habituation. This balance is crucial in order to preserve the core features that lead to the formation of personal and collective childhood memories.

UNRAVELING THE PHILOSOPHICAL ANALYSIS ON RELIGIOUS VIOLENCE

Religious violence has been a recurring issue throughout history, evoking deep emotions and raising profound questions about the nature of faith, humanity, and conflict. In this article, we embark on a philosophical analysis to unravel the complex dynamics behind religious violence, especially on the meaning dynamics among various contexts, its causes, and moral justification. This article uses conceptual analysis as the method which involves examining and clarifying the key concepts related to religious violence. It examines how individuals and groups use religious beliefs to justify violence and examines various case studies from different religious traditions, including Christianity, Judaism, and Islam. It provides insights into the motivations and ideologies that underlie acts of violence perpetrated in the name of God. This research provides a foundation for developing strategies to counteract and promote peaceful coexistence in diverse societies.Religious violence has been a recurring issue throughout history, evoking deep emotions and raising profound questions about the nature of faith, humanity, and conflict. In this article, we embark on a philosophical analysis to unravel the complex dynamics behind religious violence, especially on the meaning dynamics among various contexts, its causes, and moral justification. This article uses conceptual analysis as the method which involves examining and clarifying the key concepts related to religious violence. It examines how individuals and groups use religious beliefs to justify violence and examines various case studies from different religious traditions, including Christianity, Judaism, and Islam. It provides insights into the motivations and ideologies that underlie acts of violence perpetrated in the name of God. This research provides a foundation for developing strategies to counteract and promote peaceful coexistence in diverse societies.

Deep learning-based dimensional emotion recognition for conversational agent-based cognitive behavioral therapy.

Internet-based cognitive behavioral therapy (iCBT) offers a scalable, cost-effective, accessible, and low-threshold form of psychotherapy. Recent advancements explored the use of conversational agents such as chatbots and voice assistants to enhance the delivery of iCBT. These agents can deliver iCBT-based exercises, recognize and track emotional states, assess therapy progress, convey empathy, and potentially predict long-term therapy outcome. However, existing systems predominantly utilize categorical approaches for emotional modeling, which can oversimplify the complexity of human emotional states. To address this, we developed a transformer-based model for dimensional text-based emotion recognition, fine-tuned with a novel, comprehensive dimensional emotion dataset comprising 75,503 samples. This model significantly outperforms existing state-of-the-art models in detecting the dimensions of valence, arousal, and dominance, achieving a Pearson correlation coefficient of r=0.90, r=0.77, and r=0.64, respectively. Furthermore, a feasibility study involving 20 participants confirmed the model's technical effectiveness and its usability, acceptance, and empathic understanding in a conversational agent-based iCBT setting, marking a substantial improvement in personalized and effective therapy experiences.

(119) COPING WITH BREAST CANCER: A LOOK AT EXPRESSIVE WRITING INTERVENTIONS

Abstract Introduction Introduction: Coping with a breast cancer diagnosis is individualized and patient strategies vary throughout their dealing with this cancer. Coping methods include, but are not limited to: support groups, counseling, practicing mindfulness, spending time with family and friends, etc. One such technique is expressive writing (EW); writing that is focused on intrusive thoughts and emotional aspects related to their cancer. Objective Objectives: This meta-analysis reviewed published data regarding EW and its effects on the quality of life (QOL), including the sexual life, of breast cancer patients and survivors. Writing is a form of self-expression, which not only allows patients control of their emotions and treatment but also serves to improve the quality of care given to breast cancer patients and survivors. Thus, the ability to better implement personalized medicine remains at the center of holistic care. Additionally, we aimed to emphasize the existing clinical gap between minority groups, specifically Asian Americans, and ethnically prevalent groups. Methods Methods: A literature search was performed using PubMed to identify Randomized Controlled Trials (RCT) conducted within the United States between the years 2008 and 2023. Only RCTs with quantitative results from pre- and post-writing intervention were considered. While the structure of RCTs varied, all included interventions incorporated one or more EW group(s) and/or one or more NW (neutral writing) group(s). Measures of interest included: self-reflections from participants, linguistics, sleep quality, and Post-traumatic Stress (PTSD) symptoms. Results Results: The writing interventions studied included both NW and EW groups. These interventions resulted in various reductions of physical symptoms (ex: pain, nausea, headaches, etc.) and psychological changes (in mood, sleep, depression, and anxiety), after patients in EW groups self-reflected. Writing interventions with ethnic minorities are limited; however, writing interventions with Asian American participants improved PTSD-related symptoms and quality of life. Chinese-American women face cultural stigmatism, which may cause patients to fear sharing their deepest emotions, a possible explanation for the observed positive influence of EW for these participants. Unfortunately, no RCTs that associated EW with sexual well-being were analyzed. Conclusions Conclusions: The findings of this meta-analysis note that RCTs suggest that there is a link between EW and better coping with breast cancer. However, results may differ based on population, demographics, and cancer stage/treatment. The narrow scope of RCTs with ethnic minorities highlights that EW as a coping method for such populations remains understudied. As well, there are no data on its impact on sexual well-being-an area of research that requires more attention as women of all walks of life navigate their respective breast cancer diagnoses. Disclosure No.

The Design and Implementation of Junior High School English Reading Teaching Activities Guided by the Concept of Authenticity

Based on the concept of authenticity, this paper explores how to create real situations, design real questions and implement real feedback in the reading class of English for People's Education Press, so as to arouse students' deep emotions, truly mobilize students' thinking to solve practical problems, and promote the consolidation of new knowledge in immediate feedback. Finally improve the core quality of students' English subject.

Joint enhancement and classification constraints for noisy speech emotion recognition

In the natural environment, the received speech signal is often interfered by noise, which reduces the performance of speech emotion recognition (SER) system. To this end, a noisy SER method based on joint constraints, including enhancement constraint and arousal-valence classification constraint (EC-AVCC), is proposed. This method introduces speech enhancement features and constraints into emotion recognition for the first time, which helps to improve the performance of noisy SER. It does not require denoising pre-processing for noisy speech, thus saving testing time. Specifically, it uses multi-domain statistical features (MDSF) composed of emotional and enhanced features as the input of the SER model based on convolution neural network (CNN) and long short-term memory-attention (ALSTM). In addition, the model is constrained jointly by speech enhancement and arousal-valence classification to obtain the robust and discriminative deep emotion features. Furthermore, in the auxiliary speech enhancement task, a joint loss function that simultaneously constrains the error of the ideal ratio mask and the error of the corresponding MDSF is introduced to obtain more robust features. The experimental results on the CASIA Chinese emotional dataset and the EMO-DB German emotional database show that compared with the CNN baseline, the proposed method improves the accuracy of SER in white noise and babble noise by 4.7%-9.9%.

The Personification and Simile Found in the Song All Too Well (10 Minutes Version) by Taylor Swift

The goal of this study is to look at how Taylor Swift uses figurative language in her song "All Too Well (10 Minutes Version)." The primary purpose of this study is to discover the meaning behind the figurative language used in song lyrics. A descriptive qualitative approach was used to collect the data. There are various kinds of figurative language applied to express meaning in the song, but the writer only focuses on two of them: personification and simile. To summarize the analysis, the song's figurative language, which draws on personification and simile, creates an effective tool for portraying the story's deep and sensitive emotions, such as sense of loss, emptiness, and sadness. Keywords: Semantics, Figurative language, Personification, Simile, Song

Una investigación sobre un sistema de recomendación musical basado en expresiones faciales mediante mecanismos de aprendizaje profundo

In this study, we propose a new music recommendation system (MRS) that combines facial expression recognition technology and deep learning algorithms to respond to the changing music industry environment and provide personalized music recommendations based on the user's emotional state. Our approach includes a thorough study of facial expression recognition, emotion-based music recommendation systems, and deep learning engines, as well as a detailed presentation of the MRS design, system architecture, and deep learning engines used. Through extensive experiments, we evaluate MRS's ability to accurately recognize facial expressions, filter music based on emotional states, and effectively recommend music to users. We analyze the results of follow-up experiments to identify the strengths and limitations of MRS compared to existing approaches, and conduct a comparative study with the latest music recommendation systems based on deep learning and emotion. This comparison highlights the originality and potential of the proposed MRS system to improve user experience and promote the development of artificial intelligence-based music recommendation systems. This study demonstrates the problem of accurately determining a user's emotional state from facial expressions, which requires the integration of facial expression recognition systems, deep learning, and music recommendation systems. Using advanced deep learning techniques and a comprehensive experimental setup, the proposed MRS provides a solution to this problem by facilitating accurate emotional state identification and personalized music recommendations. Overall, MRS represents a powerful and innovative response to the growing demand for accurate and reliable music recommendations and shows significant potential for future collaboration and development of AI-based music recommendation systems.