Search Results (1,370)

Recently, zero-shot image captioning (ZSIC) has gained significant attention, given its potential to describe unseen objects in images. This is important for real-world applications such as human–computer interaction, intelligent education, and service robots. However, the zero-shot image captioning method based on large-scale pretrained models may generate descriptions containing objects that are not present in the image, which is a phenomenon termed “object hallucination”. This is because large-scale models tend to predict words or phrases with high frequency, as seen in the training phase. Additionally, the method set a limitation to the description length, which often leads to an improper ending. In this paper, a novel approach is proposed to address and reduce the object hallucination and improper ending problem in the ZSIC task. We introduce additional emotion signals as guidance for sentence generation, and we find that proper emotion will filter words that do not appear in the image. Moreover, we propose a novel strategy that gradually extends the number of words in a sentence to confirm the generated sentence is properly completed. Experimental results show that the proposed method achieves the leading performance on unsupervised metrics. More importantly, the subjective examples illustrate the effect of our method in improving hallucination and generating properly ending sentences. Full article

This paper introduces a novel method for emotion classification within virtual reality (VR) environments, which integrates biosignal processing with advanced machine learning techniques. It focuses on the processing and analysis of electrocardiography (ECG) and galvanic skin response (GSR) signals, which are established indicators of emotional states. To develop a predictive model for emotion classification, we extracted key features, i.e., heart rate variability (HRV), morphological characteristics, and Hjorth parameters. We refined the dataset using a feature selection process based on statistical techniques to optimize it for machine learning applications. The model achieved an accuracy of 97.78% in classifying emotional states, demonstrating that by accurately identifying and responding to user emotions in real time, VR systems can become more immersive, personalized, and emotionally resonant. Ultimately, the potential applications of this method are extensive, spanning various fields. Emotion recognition in education would allow further implementation of adapted learning environments through responding to the current emotional states of students, thereby fostering improved engagement and learning outcomes. The capability for emotion recognition could be used by virtual systems in psychotherapy to provide more personalized and effective therapy through dynamic adjustments of the therapeutic content. Similarly, in the entertainment domain, this approach could be extended to provide the user with a choice regarding emotional preferences for experiences. These applications highlight the revolutionary potential of emotion recognition technology in improving the human-centric nature of digital experiences. Full article

Our current understanding of the relationship between personality traits and subjective well-being, or happiness, is limited to the conceptualization of subjective well-being as being life satisfaction and a positive affective experience (i.e., the presence of positive emotions and the absence of negative ones), thus lacking the sense of acceptance, balance, adaptation, and self-transcendent unity (i.e., harmony in life) that is appreciated as part of the good life in many ancient and modern cultures. Moreover, most studies use the Big Five Model to understand which personality traits predict subjective well-being. Here, I examine the predictive power of personality on harmony in life using the Big Five Model, the Dark Triad, and Cloninger’s Biopsychosocial Model. The present study utilized past published data from three cross-sectional studies. In each separate sample, participants self-reported personality by answering the Big Five Inventory (N₁ = 297), the Short Dark Triad (N₂ = 1876), or the Temperament and Character Inventory (N₃ = 436). All participants (N_Total = 3698) answered to the Harmony in Life Scale. The traits in the Biopsychosocial Model explained the highest variance in harmony in life (R² = 0.435, F(7, 428) = 47.136, p < 0.001), followed by the Big Five (R² = 0.341, F(5, 291) = 30.110, p < 0.001) and the Dark Triad (R² = 0.096, F(3, 1872) = 66.055, p < 0.001). The key significant predictors were Self-Directedness, Self-Transcendence, and Harm Avoidance from the Biopsychosocial Model and Agreeableness, Conscientiousness, and Neuroticism from the Big Five. Narcissism was the only predictor from the Dark Triad, although this relationship was very small. The findings underscore the importance of a multidimensional approach for understanding subjective well-being and the inclusion of harmony in life as its third component. The Biopsychosocial Model’s inclusion of both temperament and character dimensions provided the most comprehensive understanding of harmony in life. While positive traits like Agreeableness, Self-Directedness, and Self-Transcendence enhance harmony, negative traits like Neuroticism and Harm Avoidance diminish it. Moreover, research only including “dark traits” might give the impression that an inflated sense of self-importance, a deep need for admiration, and a lack of empathy for others (i.e., Narcissism) is predictive of balance in life. However, this association was not only extremely low but can be interpreted as misguided since the results using the other models showed that helpful, empathetic, kind, and self-transcendent behavior predicted harmony. These results suggest that interventions aimed at enhancing well-being should consider a broad range of personality traits, especially those that are not present in the Big Five Model, thus advocating for a biopsychosocial approach to well-being interventions. Full article

Generative models have shown excellent results in aspect-based sentiment analysis tasks by predicting quadruples by setting specific template formats. The existing research predicts sentiment elements and enhances the dependency between elements using the multi-template prompting method, but it does not realize the information interaction in the generation process, and it ignores the dependency between the prompt template and the aspect terms and opinion terms in the input sequence. In this paper, we propose a Dual-template Prompted Mutual Learning (DPML) generative model to enhance the information interaction between generation modules. Specifically, this paper designs a dual template based on prompt learning and, at the same time, develops a mutual learning information enhancement module to guide each generated training process to interact with iterative information. Secondly, in the decoding stage, a label marking the interactive learning module is added to share the explicit emotional expression in the sequence, which can enhance the ability of the model to capture implicit emotion. On two public datasets, our model achieves an average improvement of 5.3% and 3.4% in F1 score compared with the previous state-of-the-art model. In the implicit sentiment analysis experiment, the F1 score of the proposed model in the data subset containing implicit words is increased by 2.75% and 3.42%, respectively. Full article

Objective: The aims of this study were to investigate the effects and mechanisms of physical exercise input, sports emotional intelligence, and sports self-efficacy on exercise adherence, and to examine the chain-mediating role of sports emotional intelligence→sports self-efficacy. Methods: The Physical Exercise Input Scale, Exercise Adherence Scale, Sports Emotional Intelligence Scale, and Sports Self-Efficacy Scale were used to investigate 1390 college students in three universities in the Henan Province. Results: (1) Physical exercise input was a significant positive predictor of exercise adherence (β = 0.29, t = 5.78, p < 0.001); (2) sports emotional intelligence and sports self-efficacy mediated the relationship between physical exercise input and exercise adherence; (3) physical exercise input influenced exercise adherence through the separate mediating role of sports emotional intelligence (β = 0.10, t = 5.98, p < 0.001), the separate mediating role of sports self-efficacy (β = 0.13, t = 2.64, p < 0.01), and the chain mediating role of sports emotional intelligence→sports self-efficacy (β = 0.09, t = 2.80, p < 0.01). Conclusions: (1) Physical exercise input can positively predict the level of sports emotional intelligence and sports self-efficacy of college students; (2) Physical exercise input can not only directly influence college students’ exercise adherence but can also indirectly influence it through sports emotional intelligence or sports self-efficacy levels alone, as well as through the chain mediation of the two. Full article

We examined the connections that narcissistic and borderline personality traits had with hypothetical responses to romantic infidelity in a sample of Israeli community members (N = 997). We distinguished between four forms of narcissism: extraverted narcissism (characterized by assertive self-enhancement), antagonistic narcissism (characterized by defensiveness and hostility), neurotic narcissism (characterized by emotional distress), and communal narcissism (characterized by attempts to emphasize superiority over others by exaggerating communal characteristics such as being extraordinarily helpful). We also measured levels of borderline personality traits. Results showed that neurotic narcissism was strongly associated with heightened negative emotional responses, particularly in high-threat infidelity scenarios, aligning with predictions regarding emotional volatility. Antagonistic and communal narcissism showed detrimental effects on relationship evaluations primarily under low-threat conditions, indicating distinct patterns of defensiveness and vulnerability. Extraverted narcissism showed no significant association with emotional responses. Borderline traits were linked to intense emotional reactions across conditions, emphasizing their broad impact on perceived relational threats. These findings suggest that while some personality traits exacerbate reactions in less severe conditions, infidelity trauma can overwhelm these differences, underscoring the potential need for personalized therapeutic approaches. Discussion is focused on the implications for understanding personality traits in relational contexts and future research directions exploring varied threat manipulations. Full article

The emotional health benefits of urban green space have been widely recognized. Flower borders, as a perennial plant landscape, have gradually become a current form of plant application in urban green spaces due to their rich color configurations. However, the related research primarily focuses on the impact of urban green spaces on public health, with relatively little attention given to how the colors of flower borders affect public emotional health. This study explored the relationship between the flower borders color characteristics and the public emotional health. In this study, 24 sample images were used as experimental materials, which selected based on their color richness and harmony. Additionally, face recognition technology and online random questionnaires were utilized to measure the public basic emotions and pleasure, respectively. The result shows that, based on the HSV color model and expert recommendations, 19 color characteristics were identified. The correlation analysis of the results from the public emotion with these color characteristics revealed that 13 color characteristics correlated with public emotional pleasure. Among them, blue, neutral purple, and low saturation were positively correlated. Through factor analysis, these thirteen color characteristics were summarized and categorized into four common factors (F1–F4), three of which are related to color. They are “low saturation of blue-violet percentage” (F1), “color configuration diversity” (F2), “bright red percentage” (F3), and “base green percentage” (F4), with F1 having the largest variance explained (27.88%). Finally, an evaluation model of color characteristics was constructed based on the variance explained by these four factors, which was demonstrated to effectively predict the level of public emotional pleasure when viewing flower borders. The results shed light on the effects of color characteristics on public emotions and provide new perspectives for subsequent flower border evaluations. Our results provide a valuable reference for future flower border color design, aiming to better improve public emotional health. Full article

Psychological distress is widespread among university students, with depression being notably more prevalent compared to the general population. University counseling services are crucial for addressing these mental health challenges, and numerous studies have demonstrated their effectiveness in reducing psychological distress and improving overall well-being. However, there is limited research on what factors predict the success of university counseling. This study aims to evaluate whether counseling improves well-being, specifically by reducing depressive symptoms, and to explore whether personality traits influence counseling outcomes. Participants included 125 Italian university students (64.8% female, mean age = 22.69; SD = 3.04) who utilized counseling services. They completed a socio-demographic questionnaire, the Beck Depression Inventory-II (BDI-II), and the Personality Inventory for DSM-5-TR (PID-5-TR) at three points: immediately after the intake interview (T0), just before the intervention (T1), and after the fourth session (T2). Linear mixed models were used to analyze changes in depression levels, revealing a significant reduction in depressive symptoms from pre- to post-intervention. Among personality traits, only antagonism showed a significant interaction with time. Additionally, higher detachment scores were associated with higher depression levels. These findings emphasize the need for focused attention on students’ emotional issues and suggest that personality traits may influence the effectiveness of counseling. Full article

The Internet of Health Things (IoHT) is a broader version of the Internet of Things. The main goal is to intervene autonomously from geographically diverse regions and provide low-cost preventative or active healthcare treatments. Smart wearable IMUs for human motion analysis have proven to provide valuable insights into a person’s psychological state, activities of daily living, identification/re-identification through gait signatures, etc. The existing literature, however, focuses on specificity i.e., problem-specific deep models. This work presents a generic BiGRU-CNN deep model that can predict the emotional state of a person, classify the activities of daily living, and re-identify a person in a closed-loop scenario. For training and validation, we have employed publicly available and closed-access datasets. The data were collected with wearable inertial measurement units mounted non-invasively on the bodies of the subjects. Our findings demonstrate that the generic model achieves an impressive accuracy of 96.97% in classifying activities of daily living. Additionally, it re-identifies individuals in closed-loop scenarios with an accuracy of 93.71% and estimates emotional states with an accuracy of 78.20%. This study represents a significant effort towards developing a versatile deep-learning model for human motion analysis using wearable IMUs, demonstrating promising results across multiple applications. Full article

Companies are increasingly using artificial intelligence (AI) to provide users with product recommendations, but its efficacy is inconsistent. Drawing upon social exchange theory, we examine the effects of product recommenders and their levels of self-disclosure on transaction costs. Specifically, we recruited 78 participants and conducted a 2 × 2 online experiment in which we manipulated product recommenders (human versus AI) and examined how self-disclosure levels (high versus low) affect consumers’ return intentions. We predicted and found that a low level of self-disclosure from human recommenders instead of AI counterparts results in higher emotional support, which leads to lower transaction costs. However, under high levels of self-disclosure, consumers’ emotional support and subsequent transaction costs do not differ between human and AI recommenders. Accordingly, we provide theoretical insights into the roles of self-disclosure and emotional support in human–machine interactions, and we contribute to sustainable AI practices by enhancing the efficiency of business operations and advancing broader sustainability objectives. Full article

Sharing of emotional states is reduced for individuals wearing face coverings, but the mechanism behind this reduction remains unknown. Here, we investigated if face occlusion by masks reduces empathy by disrupting configural processing of emotional faces. Participants rated their empathy for happy and neutral faces which were presented in upright or inverted orientation and wore opaque, clear, or no face masks. Empathy ratings were reduced for masked faces (opaque or clear) as well as for inverted faces. Importantly, face inversion disrupted empathy more for faces wearing opaque masks relative to those wearing clear or no masks, which stands in contrast to the predictions generated by the classic configural processing models. We discuss these data within the context of classic and novel configural face perception models, and highlight that studying inverted occluded faces presents an informative case worthy of further investigation. Full article

Background/Objectives: With the rapid expansion of the global sports market, the significance of sports sponsorship has attracted growing attention. However, during the golden age of the sports industry’s development in China, international sports brand giants such as Nike, Adidas, and Under Armour have rapidly captured a substantial share of the Chinese sports consumer market through their distinctive product designs and varied marketing strategies. This has resulted in a highly competitive environment for China’s sports goods industry. Therefore, fostering the improved development of domestic sports brands has become a crucial issue deserving of thorough scholarly investigation. This study examines how consumers’ differing levels of sports involvement and the degree of fit between the sponsoring brand and the sponsored event affect their cognitive and emotional responses to sports sponsorships. Methods: By employing Predictive Coding Theory and ERP (event-related potential) brainwave technology, this study delves into the psychological and neurobiological levels to analyze the impact of consumer sports involvement on the processing of sponsorship information. Results: The results indicate significant differences in cognitive and emotional responses between high-involvement and low-involvement consumers. Additionally, the fit between the sponsoring brand and the sponsored event also significantly affects consumers’ cognitive and emotional responses. These differences stem from consumers’ complex and sophisticated predictive coding models. Conclusions: This study not only provides scientific evidence for sports brands in selecting and executing sponsorship activities, but also offers new perspectives for evaluating and optimizing sponsorship effectiveness. Full article

This study aims to examine the role of emotional maturity and social support in predicting the level of quarter-life crisis in emerging adulthood. The employed research method was multiple linear regression analysis. The participants were individuals aged 18 to 29 years. Further, 122 participants were selected using convenience sampling. The data were collected using a questionnaire survey based on the Multidimensional Scale of Perceived Social Support to assess social support and the quarter-life crisis scale based on the theory by Robbins and Wilner. To assess emotional maturity, we used the emotional maturity scale based on the theory by Walgito. Emotional maturity and social support were important in predicting the level of quarter-life crisis in emerging adulthood. Higher levels of emotional maturity and social support were associated with lower levels of quarter-life crisis experiences in emerging adulthood. Full article

Accurate face detection and subsequent localization of facial landmarks are mandatory steps in many computer vision applications, such as emotion recognition, age estimation, and gender identification. Thanks to advancements in deep learning, numerous facial applications have been developed for human faces. However, most have to employ multiple models to accomplish several tasks simultaneously. As a result, they require more memory usage and increased inference time. Also, less attention is paid to other domains, such as animals and cartoon characters. To address these challenges, we propose an input-agnostic face model, AnyFace++, to perform multiple face-related tasks concurrently. The tasks are face detection and prediction of facial landmarks for human, animal, and cartoon faces, including age estimation, gender classification, and emotion recognition for human faces. We trained the model using deep multi-task, multi-domain learning with a heterogeneous cost function. The experimental results demonstrate that AnyFace++ generates outcomes comparable to cutting-edge models designed for specific domains. Full article

Personality traits significantly impact chronic diseases, affecting disease management, coping strategies, psychological well-being, and overall quality of life. People with Multiple Sclerosis (MS) often exhibit dysfunctional personality traits associated with negative disease outcomes, including personality changes and disorders. Our study explored personality traits and their connection to clinical aspects and cognitive functioning in MS patients. We used two assessment tools: the NEO-FFI and the Lüscher Color Test, which is based on color preferences. The aim was to investigate the applicability of the Lüscher Color Test in MS patients. The study included 20 participants from the Swiss Multiple Sclerosis Cohort. The results showed elevated scores in neuroticism, openness, agreeableness, and conscientiousness in MS patients, while there was no effect for extraversion. A significant positive correlation was found between neuroticism and the preference for green-blue color shades, as well as a rejection of orange-reddish color shades in the Lüscher Color Test, indicating avoidance of stimulation and engagement. Another notable positive association was found between openness and the preference for lighter shades in the Lüscher Color Test. Although this relation did not reach the level of statistical significance, it suggests a potential trend. Neuroticism on its own predicted anxiety and fatigue, while the preference for lighter shades in the Lüscher Color Test correlated with EDSS scores. No significant correlations were found between personality traits and cognitive aspects. Despite the limitations of this study, our results highlight the importance of assessing personality traits in MS patients, using either the NEO-FFI or the Lüscher Color Test, to improve treatment strategies and explore emotional conflicts related to the disease. Full article