Validation of the South Korean Version of the Beliefs about Emotions Scale

Background Beliefs about the unacceptability of experiencing or expressing negative emotions can contribute to diverse psychological symptoms and associated with poor treatment outcomes and low treatment attempts. The Beliefs about Emotions Scale (BES) was developed to assess such beliefs based on the cognitive-behavioral models; however, no study has reported on the psychometric properties of the BES in Korea. The present study aimed to cross-culturally adapt and validate the BES for the Korean population (BES-K). Methods The BES-K was administered to 592 Korean adults (323 men and 269 women) aged 20–59 years. Exploratory and confirmatory factor analysis were used to assess the factor model of the scale. Pearson correlation coefficients were used to evaluate the relationships between the BES-K and other psychological measures. Results The result showed a two-factor model of the BES-K, with Factor 1 relating to Interpersonal and Factor 2 representing Intrapersonal aspects. The scale had significant yet moderately low correlations with measures of depression, anxiety, and difficulties in emotion regulation. Conclusion The BES-K is a useful instrument in evaluating the beliefs about emotions in the Korean population

Contextual Linear Bandits under Noisy Features: Towards Bayesian Oracles

We study contextual linear bandit problems under uncertainty on features; they are noisy with missing entries. To address the challenges from the noise, we analyze Bayesian oracles given observed noisy features. Our Bayesian analysis finds that the optimal hypothesis can be far from the underlying realizability function, depending on noise characteristics, which is highly non-intuitive and does not occur for classical noiseless setups. This implies that classical approaches cannot guarantee a non-trivial regret bound. We thus propose an algorithm aiming at the Bayesian oracle from observed information under this model, achieving $\tilde{O}(d\sqrt{T})$ regret bound with respect to feature dimension $d$ and time horizon $T$. We demonstrate the proposed algorithm using synthetic and real-world datasets.Comment: 30 page

Emotion regulation from a virtue perspective

Background The ability to regulate one’s emotional state is an important predictor of several behaviors such as reframing a challenging situation to reduce anger or anxiety, concealing visible signs of sadness or fear, or focusing on reasons to feel happy or calm. This capacity is referred to as emotion regulation. Deficits in this ability can adversely affect one’s adaptive coping, thus are associated with a variety of other psychopathological symptoms, including but not limited to depression, borderline personality disorder, substance use disorders, eating disorders, and somatoform disorders. Methods The present study examined emotion regulation in relation to the virtue-based psychosocial adaptation model (V-PAM). 595 participants were clustered based on their Difficulties in Emotion Regulation Scale (DERS) score, producing two clusters (i.e., high functioning vs. low functioning). Then, emotion regulation group membership was discriminated by using five V-PAM virtue constructs, including courage, integrity, practical wisdom, committed action, and emotional transcendence. Results Results show that five virtues contribute to differentiating group membership. Practical wisdom was the strongest contributor, followed by integrity, emotional transcendence, committed action, and courage. Predictive discriminant analysis was conducted and 71% of cases were correctly classified. A discussion of the relationship between emotion regulation and virtues was elaborated. Conclusion The concept of virtue holds significant importance in the comprehension of an individual’s capacity to regulate their emotions, meriting future study. Methods: The present study examined emotion regulation in relation to the virtue-based psychosocial adaptation model (V-PAM). 595 participants were clustered based on their Difficulties in Emotion Regulation Scale (DERS) score, producing two clusters (i.e., high functioning vs. low functioning). Then, emotion regulation group membership was discriminated by using five V-PAM virtue constructs, including courage, integrity, practical wisdom, committed action, and emotional transcendence. Results: Results show that five virtues contribute to differentiating group membership. Practical wisdom was the strongest contributor, followed by integrity, emotional transcendence, committed action, and courage. Predictive discriminant analysis was conducted and 71% of cases were correctly classified. A discussion of the relationship between emotion regulation and virtues was elaborated. Conclusion: The concept of virtue holds significant importance in the comprehension of an individual\u27s capacity to regulate their emotions, meriting future study

Lip Reading for Low-resource Languages by Learning and Combining General Speech Knowledge and Language-specific Knowledge

This paper proposes a novel lip reading framework, especially for low-resource languages, which has not been well addressed in the previous literature. Since low-resource languages do not have enough video-text paired data to train the model to have sufficient power to model lip movements and language, it is regarded as challenging to develop lip reading models for low-resource languages. In order to mitigate the challenge, we try to learn general speech knowledge, the ability to model lip movements, from a high-resource language through the prediction of speech units. It is known that different languages partially share common phonemes, thus general speech knowledge learned from one language can be extended to other languages. Then, we try to learn language-specific knowledge, the ability to model language, by proposing Language-specific Memory-augmented Decoder (LMDecoder). LMDecoder saves language-specific audio features into memory banks and can be trained on audio-text paired data which is more easily accessible than video-text paired data. Therefore, with LMDecoder, we can transform the input speech units into language-specific audio features and translate them into texts by utilizing the learned rich language knowledge. Finally, by combining general speech knowledge and language-specific knowledge, we can efficiently develop lip reading models even for low-resource languages. Through extensive experiments using five languages, English, Spanish, French, Italian, and Portuguese, the effectiveness of the proposed method is evaluated.Comment: Accepted at ICCV 202

AKVSR: Audio Knowledge Empowered Visual Speech Recognition by Compressing Audio Knowledge of a Pretrained Model

Visual Speech Recognition (VSR) is the task of predicting spoken words from silent lip movements. VSR is regarded as a challenging task because of the insufficient information on lip movements. In this paper, we propose an Audio Knowledge empowered Visual Speech Recognition framework (AKVSR) to complement the insufficient speech information of visual modality by using audio modality. Different from the previous methods, the proposed AKVSR 1) utilizes rich audio knowledge encoded by a large-scale pretrained audio model, 2) saves the linguistic information of audio knowledge in compact audio memory by discarding the non-linguistic information from the audio through quantization, and 3) includes Audio Bridging Module which can find the best-matched audio features from the compact audio memory, which makes our training possible without audio inputs, once after the compact audio memory is composed. We validate the effectiveness of the proposed method through extensive experiments, and achieve new state-of-the-art performances on the widely-used datasets, LRS2 and LRS3