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Cerebellum is a key-structure for the modulation of motor, cognitive, social and affective functions, contributing to automatic behaviours through interactions with the cerebral cortex, basal ganglia and spinal cord. The predictive mechanisms used by the cerebellum cover not only sensorimotor functions but also reward-related tasks. Cerebellar circuits appear to encode temporal difference error and reward prediction error. From a chemical standpoint, cerebellar catecholamines modulate the rate of cerebellar-based cognitive learning, and mediate cerebellar contributions during complex behaviours. Reward processing and its associated emotions are tuned by the cerebellum which operates as a controller of adaptive homeostatic processes based on interoceptive and exteroceptive inputs. Lobules VI-VII/areas of the vermis are candidate regions for the cortico-subcortical signaling pathways associated with loss aversion and reward sensitivity, together with other nodes of the limbic circuitry. There is growing evidence that the cerebellum works as a hub of regional dysconnectivity across all mood states and that mental disorders involve the cerebellar circuitry, including mood and addiction disorders, and impaired eating behaviors where the cerebellum might be involved in longer time scales of prediction as compared to motor operations. Cerebellar patients exhibit aberrant social behaviour, showing aberrant impulsivity/compulsivity. The cerebellum is a master-piece of reward mechanisms, together with the striatum, ventral tegmental area (VTA) and prefrontal cortex (PFC). Critically, studies on reward processing reinforce our view that a fundamental role of the cerebellum is to construct internal models, perform predictions on the impact of future behaviour and compare what is predicted and what actually occurs. [ABSTRACT FROM AUTHOR]

The widespread use of smartphones implies that intelligent systems must address the negative emotions experienced by a diverse user group when encountering usability difficulties. Therefore, exploring the relationship between users' emotions and operational behaviors is crucial for further enhancing the accessibility of intelligent systems. Touch dynamics, as a method of dynamic touch data monitoring, has already provided many models for emotion recognition, which are beneficial in gaining insight into users' accessibility needs for smartphones and exploring intelligent solutions to design issues. This paper validates this aspect through interviews and experimental studies. The study first conducted an open-ended interview, and then used a touch behavior experiment with emotional labels and a questionnaire to validate the association between accessibility design flaws in intelligent systems and user emotions. The interview results show that middle-aged and older smartphone users have difficulty activating accessibility tools independently. Consequently, this paper establishes a dataset for emotion classification based on touch gesture data, which is used to gain insight into implicit difficulties in the interaction process through negative emotions. From a touch experiment and questionnaire survey of 57 sample users, there is approximately a 78.57% match between the accessibility needs of intelligent systems and users' negative emotions, with interaction fluency and interface cognition being the main reasons for this phenomenon. Therefore, touch dynamics, as a non-invasive method of emotion recognition, can use negative emotional feedback to gain insights into user needs that are difficult to detect by other methods. This has application value for enhancing users' emotional experience and promoting a transformation in interaction design thinking. [ABSTRACT FROM AUTHOR]

Kazuo Ishiguro's novel Klara and the Sun (2021) philosophizes on how in the current technologically saturated culture, the gradual evolution of the empathetic humanoids has, on one hand, problematized our normative notions of cognitive and affective categories, and on the other, has triggered an order of emotional uncanniness due to our reliance on hyperreal real objects for receiving solace and companionship. The novel may be conceived to be a commentary on the emerging discourse in the domain of cognitive and emotional computing that aspires to transform the inner life and social relationships of the human community. The novelty of the paper lies in its ability to showcase how Kazuo Ishiguro's Klara and the Sun (2021) creates a rupture in the existing research and literary narrative by critiquing the theoretical underpinnings of emotional computing that optimistically foresees a future where simulated empathetic minds will be able to decode the complexities of the human emotions. It discusses how literature turns into an apt tool to reflect on the limitations of the programmed machines to decode the elusiveness of the human mind that defies the one-to-one correlation between words, multiple connotations, and their underlying emotions. Through the lenses of the fictional narrative, the paper foregrounds how the concept of the social robot designed to offer empathy, care, and companionship turns into a failed project. The paper draws on critical perspectives from disposability theory, posthuman affect, and immaterial bodies to foreground the noncodified feature of affective experientialities that emerge as a result of the interface between humans and nonanimate beings. [ABSTRACT FROM AUTHOR]

How did Chinese leaders Deng Xiaoping and Zhao Ziyang feel about Hong Kong? The existing literature has tended to see the Sino-British negotiations over the future of Hong Kong through the economic lens favoured by the British side and described the Chinese side as primarily motivated by nationalism. However, 'nationalism' remains a vague concept in need of further definition. This paper focuses on the Chinese side of the negotiation table. It unpacks what 'nationalism' meant to Deng and Zhao within the context of the negotiations by examining three interrelated components: history, identity and emotion. This paper then analyses how the Chinese side used history, identity and emotion strategically during the negotiations, focusing on the September 1982 leaders' meetings as a case study. Adopting a constructivist lens, this paper examines historical documents against frameworks and theories from the social sciences, producing an interdisciplinary analysis of Chinese negotiation tactics. It unravels how China's leaders used the country's past to broadcast the Party's stance to the people, bolster their leadership position and win the advantage over Britain in the Hong Kong negotiations. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is becoming part of the everyday. During this transition, people's intention to use AI technologies is still unclear and emotions such as fear are influencing it. In this paper, we focus on autonomous cars to first verify empirically the extent to which people fear AI and then examine the impact that fear has on their intention to use AI-driven vehicles. Our research is based on a systematic survey and it reveals that while individuals are largely afraid of cars that are driven by AI, they are nonetheless willing to adopt this technology as soon as possible. To explain this tension, we extend our analysis beyond just fear and show that people also believe that AI-driven cars will generate many individual, urban and global benefits. Subsequently, we employ our empirical findings as the foundations of a theoretical framework meant to illustrate the main factors that people ponder when they consider the use of AI tech. In addition to offering a comprehensive theoretical framework for the study of AI technology acceptance, this paper provides a nuanced understanding of the tension that exists between the fear and adoption of AI, capturing what exactly people fear and intend to do. [ABSTRACT FROM AUTHOR]

In this introduction, we set the stage for a collection of papers from the Co-constructing Mathematics Motivation in Primary Education–A Longitudinal Study in Six European Countries Project (MATHMot for short), an international study aiming to identify the factors that shape the development of motivation in mathematics from a comparative perspective in primary education. Students' motivation, performance, academic emotions, and subject-related identity and agency are observed across six countries: Norway, Sweden, Finland, Estonia, Portugal, and Serbia. MATHMot builds on the belief that one of the main goals of mathematics teaching should be children's long-term motivation for learning the subject, which in turn supports students in striving for exemplary achievement in mathematics. This special section attempts to observe students' mathematics motivation in early grades and how different contributions from the classroom, home or the student's individual characteristics shed light on its development and adjacent concepts like academic emotions and math-related identity and agency. [ABSTRACT FROM AUTHOR]

Undercover videos have become a popular tool among NGOs to influence public opinion and generate engagement for the NGO's cause. These videos are seen as a powerful and cost-effective way of bringing about social change, as they provide first-hand evidence and generate a strong emotional response among those who see them. In this paper, we empirically assess the impact of undercover videos on support for the cause. We in addition analyze whether the increased engagement among viewers is driven by the negative emotional reactions produced by the video. To do so, we design an online experiment that enables us to estimate both the total and emotion-mediated treatment effects on engagement by randomly exposing participants to an undercover video (of animal abuse) and randomly introducing a cooling-off period. Using a representative sample of the French population (N=3,310), we find that the video successfully increases actions in favor of animals (i.e., donations to NGOs and petitions), but we fail to prove that this effect is due to the presence of primary emotions induced by the video. Last, we investigate whether activists correctly anticipate their undercover videos' (emotional) impact via a prediction study involving activists (exploratory analysis). Protocol registration: This manuscript is a Stage-2 working paper of a Registered Report that received In-Principle-Acceptance from Scientific Reports on November 20th, 2023 [Link to Stage-1]. The Stage-1 that received In-Principal-Acceptance can be found here: https://osf.io/8cg2d. [ABSTRACT FROM AUTHOR]

The complexity of emotions has thus far limited our understanding of them. To obtain a clear understanding of the nature of emotion, this paper proposes a novel emotion theory and establishes a universal model of the conscious world in the human brain, the substanguage and interaction model (SIM). Based on an analysis of the possibilities of the interaction process in the SIM, two basic emotions that are indecomposable factors within all emotions—hope and fear—are identified. A questionnaire survey reveals that this basic emotion exhibits high acceptability. Based on emotion theory, this paper reasonably explains the phenomena of facial attraction and infantile facial preference and discusses the psychological reasons for phonocentrism, the phenomenon of preferring the spoken word over the written word. In addition, this paper explores the possibility of artificial intelligence possessing self-emotions. Emotions are relevant to many areas of human knowledge, as well as to everyone's daily lives, and the simple, clear way to understand emotions provided in this paper may be instructive for everyone. [ABSTRACT FROM AUTHOR]

Background: Emotion is a complex phenomenon that greatly affects human behavior and thinking in daily life. Electroencephalography (EEG), one of the human physiological signals, has been emphasized by most researchers in emotion recognition as its specific properties are closely associated with human emotion. However, the number of human emotion recognition studies using computer games as stimuli is still insufficient as there were no relevant publicly available datasets provided in the past decades. Most of the recent studies using the Gameemo public dataset have not clarified the relationship between the EEG signal's changes and the emotion elicited using computer games. Thus, this paper is proposed to introduce the use of data mining techniques in investigating the relationships between the frequency changes of EEG signals and the human emotion elicited when playing different kinds of computer games. The data acquisition stage, data pre-processing, data annotation and feature extraction stage were designed and conducted in this paper to obtain and extract the EEG features from the Gameemo dataset. The cross-subject and subject-based experiments were conducted to evaluate the classifiers' performance. The top 10 association rules generated by the RCAR classifier will be examined to determine the possible relationship between the EEG signal's frequency changes and game-induced emotions. Results: The RCAR classifier constructed for cross-subject experiment achieved highest accuracy, precision, recall and F1-score evaluated with over 90% in classifying the HAPV, HANV and LANV game-induced emotions. The 20 experiment cases' results from subject-based experiments supported that the SVM classifier could accurately classify the 4 emotion states with a kappa value over 0.62, demonstrating the SVM-based algorithm's capabilities in precisely determining the emotion label for each participant's EEG features' instance. Conclusion: The findings in this study fill the existing gap of game-induced emotion recognition field by providing an in-depth evaluation on the ruleset algorithm's performance and feasibility of applying the generated rules on the game-induced EEG data for justifying the emotional state prediction result. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models' effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features. [ABSTRACT FROM AUTHOR]

Understanding users' exact feelings and enhancing enterprise customer relationship management depend heavily on emotion and sentiment analysis in intelligent customer service conversations. However, the research that is currently available analyzes either emotion or sentiment. This paper proposes a multi-task ensemble model that can perform multiple tasks of emotion and sentiment analysis simultaneously. This ensemble model via a multi-layer perceptron (MLP) network develops three deep learning models based on convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) for predicting intelligent customer service dialogue emotion and sentiment analysis, including "emotion classification and intensity", "valence and arousal for emotion", "valence and arousal for sentiment", and "3-class categorical classification for sentiment". The underlying problems cover two granularity analysis (i.e., coarse-grained and fine-grained) in the intelligent customer service domain. Experimental results suggest that the proposed multi-task ensemble model outperforms the single-task framework, and the method performs well in emotion and sentiment analysis tasks in intelligent service conversation. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to add to the research literature and begin to fill the gap in knowledge about shared parenting arrangements for women with young children and who have experienced domestic violence (DV), in three Canadian provinces; Alberta, Manitoba and Ontario. Method: This paper reports on the qualitative findings from a mixed methods study on shared parenting from women with children ages 4 and under. Twenty women participated in one-on-one individual interviews through electronic platform utilizing Zoom. Thematic analysis is used to analyze the data. Results: Despite efforts to not exclusively recruit women who had experienced domestic violence (DV) from their former partner, all participants identified as survivors of DV and ongoing survivors of DV, specifically, coercive controlling behaviours. Five themes describe the women's experiences of shared parenting with young children: 1) walking a tight-rope; 2) navigating post-separation relationships; 3) emotional realities of shared parenting; 4) shared parenting outcomes; and, 5) structural challenges. Conclusions: This paper not only adds to the significant and longstanding gap in knowledge directly from women with children ages 4 and under in shared parenting relationships, and who have experienced DV, and also helps to inform social service and legal actors. It is timely with the amendments to the Divorce Act in Canada which now includes DV as a factor in determining the best interests of the child. [ABSTRACT FROM AUTHOR]

Multimodal speech emotion recognition can utilize features from different modalities simultaneously to improve the modeling capabilities in affective computing. However, the rough feature combining method may not effectively promote interaction and facilitate learning between different modalities. This paper proposes a novel multimodal Speech Emotion Recognition (SER) framework that maps two feature vectors from different modalities to the same feature space by a Relative Entropy Alignment (REA) mechanism, which facilitates the modalities to complement mutually in learning emotional representations. Specifically, we employ KLD (Kullback-Leibler Divergence) to perform the feature alignment, capturing the temporal correlation between audio and text while ensuring that the features of both modalities tend to align in the feature space, thereby alleviating modality conflicts. Meanwhile, we construct a Multi-level Attention Fusion (MAF) mechanism to capture the emotion representations from different modalities, facilitating information exchange between different modalities while mitigating some redundant features. Furthermore, we extract multi-level acoustic information by wavelet packet transform to enrich the audio modality's emotional features further. Experimental results on some multimodal emotion datasets, such as Interactive Emotional Dyadic Motion Capture (IEMOCAP) and Multi-modal Emotion Lines Dataset (MELD), demonstrate that our proposed method outperforms the state-of-the-art model. [ABSTRACT FROM AUTHOR]

A human-like dialogue system should prioritize expressing empathy towards others, which entails two crucial aspects: (1) semantic cognition and (2) emotion detection. Previous approaches mainly model semantic and emotional dependencies by capturing emotion signal or leveraging external commonsense knowledge. However, they often ignore the syntactic information inherent in the utterances, which is crucial to understanding the subtle differences in the semantic and emotional meaning of words in different contexts. In this paper, we propose a novel framework for empathetic dialogue generation with Knowledge enhanced Dependency Tree (KnowDT). Specifically, the KnowDT captures relationships between token-level emotions by comprehensively considering of dependency tree and external knowledge. To incorporate syntactic and semantic information into context encoding, we improve the relation attention mechanism for a joint representation of adjacent nodes in dependency tree, and design a flexible tree positional encoding scheme for a broad variety of topologies without affecting their syntactic constituents. Furthermore, we present a level-ordered attention mechanism that extracts emotional features by encoding recursively along the topological structure of a dependency tree. Experimental results demonstrate that our model outperforms the state-of-the-art models in both automatic and human evaluation, in which the accuracy of emotion classification and the perplexity of generated responses are significantly improved. [ABSTRACT FROM AUTHOR]

Facial emotion recognition has become a complicated task due to individual variations in facial characteristics, as well as racial and cultural variances. Different psychological studies show that there are complex expressions other than basic emotions which are made up of two basic emotions like"Happily Disgusted", "Happily Surprised", "Sadly Surprised", etc. Compound emotion recognition is challenging due to very less publicly available compound emotion datasets which are imbalanced too. In this paper, we have proposed an LSwin-CBAM for the classification of compound emotions. To address the problem of the imbalanced dataset, the proposed model exploits the cutmix augmentation technique for data augmentation. It also incorporates the CBAM attention mechanism to emphasize the relevant features in an image and swin transformer with fewer swin transformer blocks which leads to less computational complexity in terms of trainable parameters and improves the overall classification accuracy as well. The experimental results of LSwin-CBAM on RAF-DB and EmotioNet datasets show that the proposed transformer-based network can well recognize compound emotions. [ABSTRACT FROM AUTHOR]

Reinforcement learning (RL) is an effective method in training dialogue policies to steer the conversation towards successful task completion. However, most RL-based methods only rely on semantic inputs that lack empathy as they ignore the user emotional information. Moreover, these methods suffer from delayed rewards caused by the user simulator returning valuable results only at dialogue end. Recently, some methods have been proposed to learn the reward function together with user emotions, but they omit considering user emotion in each dialogue turn. In this paper, we proposed an emotion-sensitive dialogue policy model (ESDP), it incorporates user emotions information into dialogue policy and selects the optimal action by the combination of top-k actions with the user emotions. The user emotion information in each turn is used as an immediate reward for the current dialogue state to solve sparse rewards and the dependency on termination. Extensive experiments validate that our method outperforms the baseline approaches when combined with different Q-Learning algorithms, and also surpasses other popular existing dialog policies' performance. [ABSTRACT FROM AUTHOR]

This paper explores the effects of incidental guilt on Socially Responsible Investment (SRI) decisions of retail investors. Do investors who feel guilty invest more in SRIs to clear their conscience? Are guilty investors willing to sacrifice returns to restore their moral selves? Using survey data from an online quasi-experiment among a sample of US retail investors, we find that individuals who experience incidental guilt are willing to invest more in SRI funds than those in a neutral state. We show that this effect, albeit moderate in magnitude, cannot be explained solely by differences in retail investors' moral reasoning, attitudes towards social responsibility, risk tolerance and demographic factors. When presented with a trade-off between sustainability, risk and return characteristics of the funds, guilty investors are more willing to sacrifice returns for greater sustainability than non-guilty participants. Our research provides new evidence of the effect that incidental guilt has on the sustainable investing decisions of retail investors. [ABSTRACT FROM AUTHOR]

Lesbian, gay, bisexual, trans, and queer (LGBTQ+) people are experiencing increasingly varied visibility on social media due to ongoing digitalization. In this paper, I draw on social epistemology and phenomenological accounts of the digital (Frost-Arnold in: Lackey (ed) The epistemic dangers of context collapse online, Oxford University Press, Oxford, 2021; Krueger and Osler in Philos Topics 47(2):205–231, 2019; Hine in: Ethnography for the internet: embedded, embodied and everyday, Bloomsbury, London, 2015), and argue that, for LGBTQ+ individuals, social media provides a space for connecting with people with shared lived experiences. This, in turn, makes it possible for social media to enable feelings of belonging. By interacting with other LGBTQ+ people online, LGBTQ+ individuals are enabled to imagine their own being in the world and to feel like they belong. This is especially important when we consider that, for LGBTQ+ identities, it may be more complicated to feel connected due to marginalization and (fear of) discrimination. This paper not only draws on literature from phenomenology and social epistemology on the digital, but also presents and analyzes interviews that were conducted in order to explore the social media experiences of LGBTQ+ people through a phenomenology and social epistemology informed framework. [ABSTRACT FROM AUTHOR]

Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection. [ABSTRACT FROM AUTHOR]

This paper experimentally analyzes the effect of incidental emotions on destructive tendencies of the entrepreneurially self-efficacious. Subjects experience an emotion induction procedure that involves a visual stimulus triggering a negative or positive emotion. At this point, they participate in the joy-of-destruction mini-game in which two players simultaneously decide whether they have to reduce the other player's payoff at their own cost. The results show that entrepreneurial self-efficacy could be related to more destructive behavior. Such behavior increases when individuals experience a positive emotional state. These results contribute to the growing literature on the potential downsides of high self-efficacy in entrepreneurial contexts and underscore the importance of considering the role of emotions in shaping economic decision-making. [ABSTRACT FROM AUTHOR]

According to the problem that emotional features cannot be well represented by a single feature and it is difficult to extract in the task of Speech Emotion Recognition (SER), we propose a Feature-Level (FL) fusion method for 9 types of acoustic features and a combined CNN-LSTM network with attention (CNN-A-LSTM). The feature vector set after feature-level fusion is used as the input of CNN-A-LSTM network, which contains prosodic features and spectral features. High-level Statistical Functions (HSFs) are also added to receive global features. The feature extraction network CNN-A-LSTM can more effectively read time-series input and extract speech emotion information. Finally, Softmax is applied as a classifier to obtain the final emotion classification results. The experiment is verified on SAVEE and CASIA datasets. The experimental results show that the method in this paper has the best effect compared with the state-of-the-art, and the accuracy rates of 94.5% and 96.7% are respectively gained on SAVEE and CASIA. The above results prove the effectiveness of the algorithm in this paper with some generalization. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is man-made with the purpose of serving humans. An emotional machine is designed to meet the demands of human emotion. Science fiction and films provide many stories about the emotional development of AI/robots/androids/clones. This paper argues that there is an evolutionary emotional process for AI and that humans should finally accept the coexistence of humans and AI. Paolo Bacigalupi, in his novel The Windup Girl, provides clues regarding AI's evolutionary emotions and subjectivity construction. The novel describes how one AI breaks away from rules designed to elicit obedience to develop herself as someone who knows the world in her own right. This paper explains the concepts of AI and its embodiment, concurs with emotional theorists in stressing the relationship between embodiment and emotion, and argues that there is an evolutionary emotional process in the character Emiko—the main character in The Windup Girl—in that it is bodily pain and a sense of humiliation that prompts her to fight back. She wrestles with the expectation of mere obedience rule and with her own hatred. Biopolitics, including patriarchy and anthropocentrism, eventually urges Emiko to become an independent entity. Finding and creating people like her enables Emiko to be a completely new and independent being. Inherited genes and an unjust social environment provide the impetus for Emiko to be the opposite of a human. My paper concludes with the view that accepting otherness is the key step for determining the relationship between humans and AI. To be just and caring toward others is the ultimate solution. [ABSTRACT FROM AUTHOR]

When people observe pictures, different pictures will generate different emotions, and the painters often convey emotional energy to the audience through the media. Through the effect of this emotional transfer, people get emotional satisfaction psychologically, and the author can better express the emotion he needs to express by using the principle. For those who do not know art appreciation, it is difficult to accurately describe the feelings brought by pictures and describe them accurately. It is also difficult to quickly and accurately find the picture of the corresponding feeling sought by oneself. This paper presents a quantitative picture emotion analysis method. Based on the analysis of the characteristics and colors of the pictures that arouse people's emotions, the classification planning is carried out in RGB space and HSV color space respectively. And the combination analysis of the color features with strong influence is carried out to select a more appropriate color space for the expression of human feelings. At the same time, the paper also discusses the effect of the black-and-white filling and the brightness saturation of the image. Finally, it makes use of the corresponding design principles to carry out a quantitative analysis of the emotion generated by it. The system performs a combined analysis of a picture from these directions and presents the results to the user. [ABSTRACT FROM AUTHOR]

Objectives: The current paper investigates the black sheep effect by testing how crime severity and shared identity with an offender affect retributive and restorative responses (study 1), and whether this relationship is mediated by emotion (study 2). Methods: Across two studies, we employed a 2 (crime severity) × 2 (shared identity) between-subjects factorial design. Study 2 served as a conceptual replication of study 1 and included emotions as mediators between our manipulations and justice responses. The studies were fielded to both undergraduate students and CloudResearch participants. Results: The black sheep effect was not supported in either study. Instead, severe crimes alone resulted in more retributive responses and this relationship was mediated by empathic anger. Conclusions: The results lend support to the notion that a violation of expected norms elicits a greater desire for just deserts. This finding was consistent and occurred regardless of shared values or interests with an offender. [ABSTRACT FROM AUTHOR]

Traditional machine learning classifiers can automatically evaluate human behaviour and emotion recognition tasks. However, prior research work does not secure users' privacy and personal information because they need complete access to sensitive physiological data. The recently introduced Federated Learning (FL) paradigm can address this problem. FL allows the local model updates to be sent to a central server, combining them to create a global model. It does not allow the global model to access the raw data used to train it. Motivated by the core concept of FL, this paper proposes a novel FL-based Multi-modal Emotion Recognition System (F-MERS) framework combining EEG, GSR, ECG, and RESP physiological sensors data. It uses Multi-layer Perceptron (MLP) as a base model for classifying complex emotions in three dimensions: Valence, Arousal, and Dominance (VAD). The work validates the F-MERS framework with three emotion benchmark datasets, DEAP, AMIGOS, and DREAMER, achieving accuracies of 87.90%, 89.02%, and 79.02%, respectively. It is the first FL-enabled framework for recognizing complex emotions in three dimensions (VAD) with multi-modal physiological sensors. The proposed study assesses the F-MERS framework in two scenarios: (1). Subject dependent and (2). Subject independent, making the framework more generalized and robust. The experimental outcomes indicate that the F-MERS framework is scalable, efficient in communication, and offers privacy preservation over the baseline Non-FL MLP model. [ABSTRACT FROM AUTHOR]

A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient's relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data. [ABSTRACT FROM AUTHOR]

Citizens' daily appeals are generally accompanied by negative sentiment, yet little is known about the impact of negative emotions on official response behaviors in a closed online environment. This study analyzed over 2.6 million environmental appeals and their handling records from China's closed complaint platform to explore how individual negative emotions affect department response behaviors. The results showed that negative emotions could cause departments to respond more rapidly and decrease the likelihood of the citizens receiving department assistance. Whether the appeal can be handled efficiently also depends on the oversight of the department and the respondent's implementation. Negative emotion towards the department is more likely to lead to a failed handling of the appeal. In addition, when citizens face serious hazards, such as health risks, negative emotions are understandable. Negative emotional appeals concerning health risks receive more time and effective intervention by departments. This paper sheds light on the role of negative emotions in shaping feedback and provides suggestions for improving individual appeal expression and departmental response behavior. [ABSTRACT FROM AUTHOR]

Understanding how outdoor environments affect mental health outcomes is vital in today's fast-paced and urbanized society. Recently, advancements in data-gathering technologies and deep learning have facilitated the study of the relationship between the outdoor environment and human perception. In a systematic review, we investigate how deep learning techniques can shed light on a better understanding of the influence of outdoor environments on human perceptions and emotions, with an emphasis on mental health outcomes. We have systematically reviewed 40 articles published in SCOPUS and the Web of Science databases which were the published papers between 2016 and 2023. The study presents and utilizes a novel topic modeling method to identify coherent keywords. By extracting the top words of each research topic, and identifying the current topics, we indicate that current studies are classified into three areas. The first topic was "Urban Perception and Environmental Factors" where the studies aimed to evaluate perceptions and mental health outcomes. Within this topic, the studies were divided based on human emotions, mood, stress, and urban features impacts. The second topic was titled "Data Analysis and Urban Imagery in Modeling" which focused on refining deep learning techniques, data collection methods, and participants' variability to understand human perceptions more accurately. The last topic was named "Greenery and visual exposure in urban spaces" which focused on the impact of the amount and the exposure of green features on mental health and perceptions. Upon reviewing the papers, this study provides a guide for subsequent research to enhance the view of using deep learning techniques to understand how urban environments influence mental health. It also provides various suggestions that should be taken into account when planning outdoor spaces. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose a preferences representation model where ambiguity attitudes can be exogenous events or past experience-dependent. We adapt the Recursive Smooth Ambiguity model proposed by Klibanoff (Journal of Economic Theory 144:930-976, 2009) by introducing past experience described by a sequence of neutral events occurring up to the moment of the decision. These neutral events do not provide any information on the true process, but are likely to strengthen or weaken the decision-maker's ambiguity aversion degree by modifying emotions. Our model can explain some observed behaviors and market inefficiencies. We propose two illustrations. First, we provide a behavioral explanation for the decrease in influenza vaccination in France that followed the H1N1 crisis in 2009–2010. Second, we contribute to the broad literature on the annuity puzzle by introducing the impact of emotions on ambiguity attitudes. [ABSTRACT FROM AUTHOR]

After introducing some of the many issues raised by intergenerational justice, the paper will focus in particular on the motivational problem: Why should we be motivated to act in favor of others when sacrifices on our behalf are required? And more specifically, how can such sacrifices be justified when those we act for are neither born nor easily unidentifiable? While many accounts of moral motivation exist, most scholars will grant that emotional engagement is a strong motivational drive. Hence, the paper will focus on such a drive. I will, first, argue that immediate emotions and empathy – understood uniquely as a form of emotional attunement – are insufficient to grant that the acts they motivate are morally acceptable. The case of future generations is a perfect example of such insufficiency. Second, I will discuss the possibility of regulated emotions and sympathy playing such a role. In fact, by regulating and educating emotions, a conscious or "rational" component is added, which could help avoid the biases and limitations immediate affective phenomena show. Such a "rational" component would also enable us to provide a criterion to distinguish cases in which emotions drive us in morally acceptable or unacceptable directions. In the second part of the paper, the sentimentalist tradition will be reconsidered, with particular attention to Adam Smith's moral proposal – since the sympathetic engagement of an impartial spectator could be an excellent example of a regulated and educated emotional attunement of the kind required to deal with some of the many moral issues future generations raise and, in particular, with the motivational problem itself. [ABSTRACT FROM AUTHOR]

This paper argues that causing beings to exist can benefit them. It is sketched how this view avoids Derek Parfit's repugnant conclusion by rejecting the transitivity of the relation better/worse than. It handles Jeff McMahan's asymmetry consisting in that reasons against letting beings with bad lives exist are significantly stronger than reasons for letting beings with good lives exist by putting it down to the conditions making lives bad being more potent than those making them good. The latter asymmetry is reflected in negative feelings being stronger than positive, including compassion being stronger than positive sympathy. Compassion is a chief source of benevolence. Neither the absence of consciousness nor the physical non-existence of possible beings hinders compassion for them. What hinders it is the same as what hinders compassion for existing beings unknown to us: lack of the detailed information about what their lives would be like which facilitates empathy. [ABSTRACT FROM AUTHOR]

This paper proposes an explanatory model of the intention to pay for fair trade food products based on the stimulus-organism-response (S-O-R) theory. The research aims are to test the effects of general attitudes toward sustainable food products on the valuation of fair-trade food products, to study the effects of emotions on willingness to pay for fair trade food products and to analyse the mediating role of product evaluation and emotions. The proposed model was tested using an ordinary least squares estimation and an analysis combining the properties of parallel and serial mediation on a representative sample of 305 consumers in the Basque Country (Spain). Descriptive analyses indicate that respondents had a favourable attitude toward sustainable products and positively valued fair trade products. The willingness to pay was moderate. The results show the importance of emotions, especially pride, in transforming attitudes toward sustainable food products into a willingness to pay for fair trade food products. This study links the more general literature on sustainable products to that of fair-trade products, broadening the focus of the S-O-R theory. In addition, it helps fair trade organisations understand consumer behaviour influenced by positive emotions and attitudes towards sustainable products, including fair trade products. [ABSTRACT FROM AUTHOR]

Creating impressive video content such as movies and advertisements is a very important yet challenging task in business that requires both a sense of creativity and a lot of experience. Even professionals cannot necessarily invoke the impressions and emotions that they have aimed at. Many video advertisements are created and then disappear without giving a large impact on viewers. This paper presents a large-scale dataset of television (TV) advertisements that consists of 14,490 videos. The impressions of each video such as the recognition rate and interestingness rate are from the results of questionnaires answered by 620 participants. We also present a baseline for predicting the impression effects of TV advertisements by using visual and audio information, metadata such as broadcasting pattern, business category, the popularity of the casts, and text information including texts appearing on videos and narrations in audios. We predict four impressions of the viewers: 1) how much participants remember the video afterward, 2) how much they feel like buying the product/service, 3) how much they become interested in the product/service, and 4) how much they like the content of the advertisement itself. By combining images, audio, metadata, cast data, and text data, our baseline method is able to predict such impressions with a correlation of 0.69-0.82, much better than using a single-modal feature such as visual data or audio data only. This paper also gives some possible applications such as estimating the importance scores of each key frame, which gives us informative insights about how to make the advertisement content more impressive. [ABSTRACT FROM AUTHOR]

China communicates strategic narratives about artificial intelligence (AI) in digital media productions to create a shared meaning about its actions and its image in the global race to develop AI. Building upon the literature in emotions and strategic narratives, this study seeks to clarify which emotions are discursively used in China's international AI narratives, and their function and significance. Specifically, the study investigates emotion discourses in AI-focused videos disseminated on China's international broadcasting (CGTN YouTube channel). The analysis reveals that pride, hope and fear discourses have multiple functions in China's international AI narratives on CGTN. Hope is used to represent China as a key competitor in the global AI race, who is catching up to the US. China uses pride to showcase its advances in AI applications, to highlight its transformation of traditional industries, and to identity itself as equal to the US. Fear is used to frame US's perception of China's AI developments, to suggest their crisis of confidence, but also to refute the "China threat". Additionally, the fear discourse is used to deliberate anticipated risks, particularly on job loss and data privacy. These emotion discourses forge China's identity as a future global AI power. The paper then discusses how these findings contribute to key debates about Chinese discourse and media strategy. [ABSTRACT FROM AUTHOR]

Recognizing emotions from physiological signals has proven to be important in various scenarios. To assist in developing emotion recognizers, software frameworks and toolboxes have emerged, offering ready-to-use components. However,these have limitations regarding the type of physiological signals supported, the recognition steps covered, or the acquisition of multiple physiological signals. This paper presents metaFERA, an architectural meta-framework for creating software frameworks for end-to-end emotion recognition from physiological signals. The modularity and flexibility of the meta-framework and the resulting frameworks allow the fast prototyping of emotion recognition systems and experiments to test and validate new algorithms. To that end, metaFERA offers: (i) a set of pre-configured blocks to which we can add behavior to create framework components; (ii) an easy way to add behavior to the pre-configured blocks; (iii) a channel-based communication mechanism that transparently and efficiently supports the exchange of information between components; (iv) a simple and easy way to use and link components from a resulting framework to create applications. Additionally, we provide a set of Web services, already configured, to make the resulting recognition systems available as a service. To validate metaFERA, we created a framework for Electrodermal Activity, an emotion recognizer to identify high/low arousal using the aforementioned framework, and a layer to offer the recognizer as a service. [ABSTRACT FROM AUTHOR]

The negative impact of used batteries of new energy vehicles on the environment has attracted global attention, and how to effectively deal with used batteries of new energy vehicles has become a hot issue. This paper combines the rank-dependent expected utility with the evolutionary game theory, constructs an evolutionary game model based on the interaction mechanism between decision makers' emotions and decision making, and studies the recycling strategy of new energy automobile trams under the heterogeneous combination of emotions. The study shows that: (1) In addition to the establishment of effective external norms, the subjective preference of decision makers can also positively affect the recycling strategy of new energy vehicle batteries. (2) Fairness preferences can have a significant nonlinear effect on new energy vehicle battery recycling strategies by changing the utility function of decision makers. (3) When new energy vehicle manufacturers remain optimistic and new energy vehicle demanders remain rational or pessimistic, the new energy vehicle battery recycling strategy can reach the optimal steady state. [ABSTRACT FROM AUTHOR]

In this paper, we contrast the different ways in which the representationalist and the attitudinalist in the theory of emotions account for the fact that emotions have evaluative correctness conditions. We argue that the attitudinalist has the resources to defend her view against recent attacks from the representationalist. To this end, we elaborate on the idea that emotional attitudes have a rich profile and explain how it supports the claim that these attitudes generate the wished-for evaluative correctness conditions. Our argument rests on the idea that emotional attitudes manifest a sensitivity to evaluative evidence and that this sensitivity secures the kind of normativity we expect of the emotions. We bring our discussion to a close by assessing whether the psychological underpinnings of this sensitivity to evaluative evidence are such as to threaten the foundation of attitudinalism: the idea that emotions do not represent values. Given the available models of how we might access values prior to emotional experience, we conclude that the attitudinalist is still in the game. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the intricate relationship between physical function factors and each subcategory score of the Anterior Cruciate Ligament‐Return to Sport after Injury (ACL‐RSI) scale among patients following ACL reconstruction. Methods: Participants comprised 59 patients who had undergone primary ACL reconstruction using hamstring tendon. The ACL‐RSI was completed 6 months after reconstruction and five physical functions were measured in patients on the same day. Simple linear regression was performed multiple times to investigate the relationship between ACL‐RSI subcategory scores as a dependent variable and each independent variable (knee strength, leg anterior reach distance, single‐leg hop [SLH] distances, side bridge endurance, and subjective running ability). Multiple regression analysis was performed using a stepwise method, with factors showing a risk rate <0.05 in simple linear regression analyses as independent variables and the ACL‐RSI in each subcategory score as the dependent variable. Results: Multiple regression analysis showed that subjective running ability affected all subcategories (p ≤ 0.001), and that the limb symmetry index of medial SLH distance affected both the Emotions (p = 0.047) and Confidence (p = 0.009) subcategories. Higher subjective running ability and greater limb symmetry in the medial SLH were thus positively associated with each dimension of psychological readiness. Conclusions: This study highlights the differential impact of physical function factors on specific subcategories of the ACL‐RSI scale, providing clinicians with insights for designing targeted rehabilitation strategies. This original paper suggests the importance of analysing factors related to subcategory scores in addition to total ACL‐RSI score, and could contribute to the understanding of determinants for a successful return to sport following ACL reconstruction. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]

Despite the increasing use of robots in a variety of applications, little is known about the emotional responses humans experience when a robot or a human commits a trust violation. The current paper compares the affective responses of humans paired with either a human or a robot confederate who committed trust violations. Additionally, the current paper utilizes new manipulations in the literature to experimentally manipulate the type of trust violation, namely ability, benevolence, and integrity violations. As expected, when a robot committed an ability violation participants' positive affect decreased more than if the violation was performed by a human. When an integrity or benevolence violation occurred, participants had a greater decrease in positive affect when a human performed the violations than when a robot violated trust. Overall, participants experienced more negative affect with a human partner than a robot partner. Also, ability violations had stronger effects on negative affect than integrity violations. Results indicate humans do have different affective responses when trust is violated, depending on the type of violation as well as the partner performing the violation. Implications are discussed. [ABSTRACT FROM AUTHOR]

Archaeologists are increasingly interested in studying the role emotions have played in past human decision making. This paper demonstrates how awe is under-appreciated within archaeology despite it being uniquely available to archaeological research given its connection to monumental architecture and communal rituals. Archaeological engagement with awe is particularly important as psychological research has demonstrated that it is a prosocial emotion that leads to the creation of more extensive and stronger social bonds between individuals. A novel interpretation of Poverty Point (USA) is provided to illustrate the importance of studying awe, as this massive earthwork site was built more than 3000 years ago through large-scale gatherings. Reconsidered as a place of awe, Poverty Point is recast as an emotional locale where larger social and cultural identities and relationships were formed. [ABSTRACT FROM AUTHOR]

Music has always been the most powerful medium to express human emotion and feeling which sometimes mere words cannot express. As a result generating music using machine and deep learning approaches have been quite popular for some time now. It is a very challenging and interesting task to do as imitating human creativity is not easy. This paper attempted to perform effective melody generation using sequential deep learning models particularly LSTMs (Long short-term memory). In this context, note that the previous works exhibit two principal limitations. Firstly, a significant majority of the studies rely on RNN variants that cannot effectively remember long past sequences. Secondly, they often don't consider the varying temporal context lengths in melody generation during data modeling. In this work, experiments have been performed with different LSTM variants namely Vanilla LSTM, Multi-Layer LSTM, Bidirectional LSTM and different temporal context lengths for each of them to find out the optimal LSTM model and the optimal timestep for efficient melody generation. Moreover, ensembles of the best-performing techniques for each genre (e.g., classical, country, jazz, and pop) are implemented to see if we can generate even better melodies than the corresponding individual models. Finally, a qualitative evaluation is carried out for the generated melodies by conducting a survey that we circulated among fellow colleagues and within the ISMIR community and asked the participants to rate each audio on a scale of 1-5 which helped us in assessing the quality of our generated music samples. All the models have been validated on four datasets that we manually prepared based on their genres namely Classical, Jazz, Country and Pop. [ABSTRACT FROM AUTHOR]

This paper examines the therapeutic potential of twenty-first century music as a means of supplementary therapeutic care for cancer survivorship. It presents a study of songs by Rihanna, Beyoncé, Adele, Coldplay, and Imagine Dragons, which combines the analysis of relevant music features and conceptual metaphors in the lyrics to examine the effect of the songs on the audience. The main aim of this study was to highlight the emotional and cognitive impact of these songs on listeners and identify their potential role in improving the psychological condition of patients with cancer who are downtrodden or reeling from the pain of surgery, chemotherapy, and side effects of treatment. This article adopts the conceptual metaphorical framework proposed by Lakoff and Johnson (1980) and the metaphor identification procedure (MIP) (Pragglejazz group, 2007) to examine the targeted use of metaphors features in the lyrics of the selected songs. The findings show that although there is a therapeutic potential associated with the songs analyzed, there are also potential risks for patients with cancer. “”. [ABSTRACT FROM AUTHOR]

The aim of our paper is to explore the predictor variables of autonomous use of technology (Benson, 2011) in relation to second language learning. To this end, we explored motivated learning behavior (Dörnyei & Ushioda, 2021), self-efficacy (Bandura, 1986) and several language learning-related emotions, such as enjoyment, pride, hope, curiosity, confusion, anxiety, shame, boredom, and apathy (Albert et al., 2021) in our quantitative study. Our research questions concerned the investigation of gender differences in addition to exploring relationships between the constructs analyzed. Our standardized questionnaire was completed by 1,152 secondary school students, 467 boys and 682 girls. Regression analysis results suggest that, in addition to motivation and self-efficacy, there are several emotions which significantly shape the autonomous use of technology related to language learning. Out of these, confusion (with a negative effect) and boredom (surprisingly with a positive effect) influence autonomy for all students, while pride and shame are significant only for boys and anxiety exclusively for girls. Our results have both theoretical and pedagogical implications. The findings concerning the individual difference variables tested contribute to the study of the complex interplay of these variables, and the pedagogical implications draw our attention to the important role of teachers in shaping emotions. [ABSTRACT FROM AUTHOR]

Contemporary and emerging chatbots can be fine-tuned to imitate the style, tenor, and knowledge of a corpus, including the corpus of a particular individual. This makes it possible to build chatbots that imitate people who are no longer alive — deathbots. Such deathbots can be used in many ways, but one prominent way is to facilitate the process of grieving. In this paper, we present a framework that helps make sense of this process. In particular, we argue that deathbots can serve as affective scaffolds, modulating and shaping the emotions of the bereaved. We contextualize this affective scaffolding by comparing it to earlier technologies that have also been used to scaffold the process of grieving, arguing that deathbots offer some interesting novelties that may transform the continuing bonds of intimacy that the bereaved can have with the dead. We conclude with some ethical reflections on the promises and perils of this new technology. [ABSTRACT FROM AUTHOR]

The skill model of wisdom argues that practical wisdom can be best understood in terms of practical skill or expertise, and the model is thought to have the characteristic of focusing on how wise people think rather than how wise people feel. However, from the perspective of Kunzmann and Glück, "it is time for an 'emotional revolution' in wisdom research, which will contribute to a more balanced view on wisdom that considers emotional factors and processes as equally typical of wisdom as are cognitive and reflective factors" (Kunzmann U, Gluck J [2019] Wisdom and emotion. In: Sternberg R, Gluck J (eds) The cambridge handbook of wisdom. Cambridge University Press, Cambridge, pp 575–601). Kunzmann and Glück's appeal for this emotional revolution is significant and intriguing because the inclusion of an explanation of the role of emotion in wisdom can contribute to developing the skill model of wisdom into a more comprehensive framework that recognizes the intricate relation between wisdom and emotions. In this paper, I shall propose and develop a skill account of the emotions of the wise to serve as a supplement to the skill model of wisdom. According to this account, first, at the critical level, having emotions and emotional competence as elements is not essential to wisdom; second, at the theoretical level, a wise person feels an emotion E if and only if E is strategically rational for the wise person; and third, at the metatheoretical level, the primary aim of the skill account is to explain why wise people feel in a particular way rather than to describe how wise people feel. [ABSTRACT FROM AUTHOR]

The principles of CBT emphasize the interactions among cognition, emotion, and behavior. CBT's origins lie in Stoicism philosophy and share similarities with the ideas of Confucianism, Taoism, and Traditional Chinese Medicine (TCM) in China. The idea of mind–body regulation in traditional Chinese culture has laid a cultural foundation for the promotion of CBT in China. This paper analyzes the applicability and challenges of CBT in China based on the current development of CBT in mainland China and the learning and practice of CBT therapists. It also explores the potential further development of CBT in China and the integration of CBT with the Chinese culture. [ABSTRACT FROM AUTHOR]

Young people that offend evidence higher rates of trauma and post-traumatic distress than non-offending peers. Effects of post-traumatic distress also parallel research on some young people that participate in restorative justice (RJ) meetings who struggle with communication, emotionally withdraw, become agitated or defiant, evidence poor understanding of harms they have caused, or fail to demonstrate empathy or remorse. In this paper I suggest post-traumatic distress may explain some variation in RJ process and outcomes hitherto ignored in existing research. I also suggest research on trauma in young people raises four areas of concern for thinking about RJ as a "trauma-informed" practice, including impacts of trauma and post-traumatic distress on (1) oral language proficiency and non-verbal communication; (2) the experience and expression of emotions; (3) offender perceptions of fairness and respect; and (4) difficulties in behavioural changes following participation in RJ meetings. I conclude with discussion of challenges to and suggestions for using RJ as a trauma-informed practice in youth justice settings. [ABSTRACT FROM AUTHOR]

Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of online platforms, the automatic detection of emotions forms an important application in monitoring customer satisfaction and preventing negative word-of-mouth. This paper introduces EmoTwiCS, a corpus of 9489 Dutch customer service dialogues on Twitter that are annotated for emotion trajectories. In our business-oriented corpus, we view emotions as dynamic attributes of the customer that can change at each utterance of the conversation. The term 'emotion trajectory' refers therefore not only to the fine-grained emotions experienced by customers (annotated with 28 labels and valence-arousal-dominance scores), but also to the event happening prior to the conversation and the responses made by the human operator (both annotated with 8 categories). Inter-annotator agreement (IAA) scores on the resulting dataset are substantial and comparable with related research, underscoring its high quality. Given the interplay between the different layers of annotated information, we perform several in-depth analyses to investigate (i) static emotions in isolated tweets, (ii) dynamic emotions and their shifts in trajectory, and (iii) the role of causes and response strategies in emotion trajectories. We conclude by listing the advantages and limitations of our dataset, after which we give some suggestions on the different types of predictive modelling tasks and open research questions to which EmoTwiCS can be applied. The dataset is made publicly available at https://lt3.ugent.be/resources/emotwics. [ABSTRACT FROM AUTHOR]

Mental health challenges have been recognized as one of the most prevalent issues impacting children and families within the United States. Children and families of color are disproportionately affected by this due to lack of access to preventative and ongoing supports, and programs that can help address their overall well-being. These issues are even more severe for those that are at risk or interact with the child welfare system. Social workers use various clinical modalities to assess and support them. In the recent years, child welfare agencies have turned to Evidence based programs (EBPs) as a best practice to meet the needs of children and families. The Title IV-E Prevention Services Clearinghouse, established by the Administration of Children and Families is a landmark effort to review and promote such EBP's that prevents foster care placements, including mental health programs. While the Clearinghouse has reviewed 141 programs and services, out of which 71 have been rated as promising, supported, or well-supported, there is a dearth of culturally responsive programs that have met the Clearinghouse criteria. Given the overrepresentation of African American and Native American Families in the child welfare system, and the critical role of the Clearinghouse in reviewing and promoting EBPs, this paper highlights the philosophical, administrative, cultural and logistical barriers for culturally relevant programs to be accepted into the Clearinghouse. It also provides specific recommendations on how the Clearinghouse and researchers can build EBPs that account for the intersection between mental health, social location and cultural identities of the children and families that social workers serve. [ABSTRACT FROM AUTHOR]

Children who have been removed from their homes as a result of maltreatment and abuse and have been placed in foster care or are in the process of adoption often continue to meet their birth parents by court decision. This contact is often held under supervision. Supervised contact is intended to provide children the opportunity to maintain the parent-child relationship in a safe and neutral setting. Findings have shown that in some cases supervised contact can be harmful, undermining the children's sense of security and placement stability. It has been suggested that agencies have limited practice skills to help build constructive relationships through contact visits, and may thereby be failing to offer sufficient support for supervised contact. While the literature highlights various aspects that need to be implemented to improve visits, there is a lack of a trauma-informed approach, whereby professionals supervising visits can address the traumatic experiences that led to the circumstances of supervised visitation and respond to the difficult emotions of all those involved. This paper provides practice guidelines for professionals accompanying supervised visits using principles of Child-Parent Psychotherapy, a trauma-informed dyadic intervention model for young children. Case vignettes illustrate how professionals supervising contact might address the child's trauma history and help birth parents and foster/adoptive parents respond to the child's attachment needs following trauma, and how professionals can support birth parents and foster/adoptive parents, thereby promoting safety and improving child-parent interactions. Recommendations are offered for attaining the best clinical practices in supervised contact, using seven practical guidelines. [ABSTRACT FROM AUTHOR]