Personalisation and profiling using algorithms and not-so-popular Colombian music: goal-directed mechanisms in music emotion recognition

The following article discusses the development of Colombia’s paramilitary army, the Autodefensas Unidas de Colombia (AUC), beginning in the 1990s and ending with the destruction of the organisation in the late 2000s. The AUC was originally founded by three brothers surnamed Castaño as a private army designed to combat the Fuerzas Armadas Revolucionarias de Colombia (FARC) and other Columbian revolutionary guerrilla groups. The main argument put forward in the article is that when the AUC was initially founded, the primary goal of its leaders, the Castaño brothers, was a sincere desire to check and, if possible, destroy the power of the FARC. In the process of its development however, the AUC came to depend on the taxation of cocaine to fund its war against the guerrillas. When the Colombian state, which had been too weak to prevent the development of either the AUC or the FARC in the 1990s, strengthened its military power in the 2000s, it demanded the AUC cease its operations, demobilise its military forces, and aid the state in destroying the cocaine industry’s infrastructure in southern Colombia. The Castaño brother who had become the organisation’s sole leader, Carlos, was willing to comply, but his move to end the AUC’s association with the cocaine industry invoked the wrath of his subordinate commanders, resulting in his brutal murder. This event revealed that the AUC had gradually developed into a cocaine cartel in the guise of a paramilitary army despite the intentions of its leader, who was killed because his leadership became a threat to the profitable taxation of cocaine that his former subordinate commanders enjoyed.

Music is one of the primary ways to evoke human emotions. However, the feeling of music is subjective, making it difficult to determine which emotions music triggers in a given individual. In order to correctly identify emotional problems caused by different types of music, we first created an electroencephalogram (EEG) data set stimulated by four different types of music (fear, happiness, calm, and sadness). Secondly, the differential entropy features of EEG were extracted, and then the emotion recognition model CNN-SA-BiLSTM was established to extract the temporal features of EEG, and the recognition performance of the model was improved by using the global perception ability of the self-attention mechanism. The effectiveness of the model was further verified by the ablation experiment. The classification accuracy of this method in the valence and arousal dimensions is 93.45% and 96.36%, respectively. By applying our method to a publicly available EEG dataset DEAP, we evaluated the generalization and reliability of our method. In addition, we further investigate the effects of different EEG bands and multi-band combinations on music emotion recognition, and the results confirm relevant neuroscience studies. Compared with other representative music emotion recognition works, this method has better classification performance, and provides a promising framework for the future research of emotion recognition system based on brain computer interface.

Electroencephalography (EEG) can capture electrical activity associated with human emotion processing from the scalp. The electrical activity can be processed using deep learning models to predict emotional states. Two approaches can be employed to develop these deep learning models: subject-dependent and subject-independent. The subject-independent approach is more practical as it trains the model on data from some individuals and tests it on entirely different individuals, ensuring it generalizes well to new users. However, because of the high variability of EEG across individuals, the subject-independent approach tends to yield low performance. Recent studies suggest incorporating demographic information along with EEG signals is one way to overcome this issue. By using the subject-independent approach, this study investigates different demographics factors such as age, biological sex and cultural factors impact emotion prediction. Moreover, this thesis delineates the development of a deep learning models dedicated to emotion recognition on five different datasets. To find the impact of age and biological sex a logistic regression model was used to correlate the output of a deep learning model with subjects’ age and sex, thereby evaluating whether these factors impact emotion prediction. Our analysis indicates that the ‘sex’ variable significantly influenced the predictions of the deep learning model in three out of five emotions, whereas ‘age’ does not have any effect. These findings suggest that sex is a factor that needs to be considered when designing EEG-based emotion recognition models. Furthermore, attention network layers were used to identify brain areas more involved in predicting emotions. Additionally, an odds ratio analysis was conducted using logistic regression to evaluate the impact of sex on emotion prediction. Our findings reveal that cortical activation patterns elicited by emotional audio-visual stimuli differ between females and males, with females showing more neural activation in the left hemisphere and males showing more in the right hemisphere. Moreover, when the output probabilities of the deep learning models are further postprocessing with the subject’s sex, the odds of correctly predicting emotions increase. These findings suggest that sex differences can lead to more robust subject-independent emotion recognition models. Additionally, this study also investigates how cultural factors impact emotion prediction. Specifically, we used a stacking model that combines deep learning with multinomial logistic regression to predict positive, neutral, and negative emotions among 15 Chinese, 8 French, and 8 German subjects. Our approach achieved accuracies of 77.3% for Chinese subjects, 73% for French subjects, and 65% for German subjects, which are comparable to or exceed accuracies reported by previous studies. Our approach highlighted that incorporating cultural information increases the likelihood of predicting positive emotions for Chinese participants and negative emotions for Europeans. Moreover, French and German subjects exhibited similar neural patterns across all emotions, suggesting a more common cultural sharing between those subjects. Overall, our findings emphasize the importance of integrating demographics information considerations into emotion recognition models. This inclusion not only improves emotion prediction accuracy for subject-independent approaches but also promotes inclusivity and ethical practices in emotion recognition systems. Which could lead to more robust subject- independent models with potential applications in areas such as healthcare, education, and marketing.

Exploring emotional intelligence in artificial intelligence systems: a comprehensive analysis of emotion recognition and response mechanisms.

Objective.Emotional states and mood disorders are closely interconnected, and their joint recognition serves as a critical pathway to uncovering their intrinsic relationship. Currently, deep learning (DL) models based on electroencephalogram (EEG) have achieved significant progress in single tasks such as emotion recognition or mood disorder (MD) recognition. However, most existing models are limited to handling only one of these tasks independently and fail to effectively leverage the shared features in EEG data related to both emotions and mood disorders. This limitation hinders the in-depth exploration of the complex interplay between emotions and mood disorders. Therefore, this study aims to develop an EEG-based DL framework for the joint recognition of emotions and mood disorders, thereby providing a foundation for further investigation into their interaction.Approach.We design a multi-gate mixture-of-experts graph convolutional network model(MMoGCN) for joint emotion and MD recognition. MMoGCN comprises three key modules: (1) a feature extraction module based on differential entropy to robustly represent EEG signals; (2) a Multi-gated shared experts module, which integrates two experts, and combines them through a gating mechanism to extract shared representations across tasks; and (3) adaptive task-specific towers, which consist of individual classification towers for each task and incorporate an adaptive weighting loss function to dynamically adjust task contributions. MMoGCN is evaluated on a self-collected dataset and further validated on the public DEAP dataset.Main results.MMoGCN achieves superior performance compared with state-of-the-art single-task and multi-task baselines in both emotion and MD recognition. Validation experiments on DEAP further demonstrate the scalability and generalization of MMoGCN.Significance.An effective multi-task learning model is proposed for joint emotion and MD recognition based on EEG. Additionally, the cognitive differences are also analyzed in emotional responses between healthy controls and subjects with mood disorders, providing methodological insights and potential assistance for cognitive rehabilitation from both cognitive and emotional perspectives.

Subjective and physiological emotional response in euthymic bipolar patients: A pilot study

This chapter considers the multiple efforts at engaging three armed groups in Colombia: the Revolutionary Armed Forces of Colombia (Fuerzas Armadas Revolucionarias de Colombia [FARC]), the National Liberation Army (Ejercito Liberacion Nacional [ELN]), and the United Self-Defense Forces of Colombia (Autodefensas Unidas de Colombia [AUC]). I address the various demands for territorial and political control put forth by each of the two key rebel armed groups — the FARC and the ELN — as well as the efforts to either address those demands or simply recognize the security situation on the ground, and to reach cease-fires and engage in demobilization. This engagement appears to have failed, for various reasons, including the possibility that the incentives offered are not of significant interest to groups also engaged in the lucrative narcotics industry, or fearful of the heavy presence of the United States through Plan Colombia. Based on my fieldwork, carried out in summer 2006, I address the key concerns and objections of each armed group. Finally, although the umbrella group of right-wing paramilitaries, the AUC, has distinct historical origins and relations with the government, I address the demobilization of the AUC undertaken with the government’s guarantee of amnesty and a ‘concentration’ zone.

Nationalism, racism, violence, and militarism are incarnate in football itself, as indicated through an engagement with the history and theory of the sport. This chapter considers this context to question the identity of the state and who holds a monopoly on legitimate violence, through a case study of Colombia. It focuses on the 1980s and 1990s, an era dominated by putatively progressive guerrilla movements (the Fuerzas Armadas Revolucionarias de Colombia, or FARC), putatively unofficial right-wing paramilitares (the Autodefensas Unidas de Colombia, or AUC), and putatively populist narcotraficantes/Mafiosi. One aspect was not entirely shared in their tripartite struggle against each other and the state over who could terrorize the population most—the narcos' involvement in football. For while state militarism occupies an important role in the mental map of Colombians, especially since US intervention from Bill Clinton to Barack Obama via "Plan Colombia", it has been largely absent from football, though institutional violence and its symbolism have not.

Las víctimas han adquirido una legitimidad social y política inédita en el mundo contemporáneo. A la luz de dicho fenómeno y los debates que ha generado en las ciencias sociales y humanas, en este artículo analizamos cómo se construye la identidad de víctima en Bojayá, un municipio del litoral pacífico colombiano donde fueron masacradas más de ochenta personas en 2002 durante un combate entre las FARC-EP (Fuerzas Armadas Revolucionarias de Colombia-Ejército del Pueblo) y las AUC (Autodefensas Unidas de Colombia). Apoyados en entrevistas y datos de campo, argumentamos que el trauma social de la masacre y el sufrimiento son el motor de la agencia política de las víctimas y el medio que les permite devenir sujetos políticos. A partir de conceptos del psicoanálisis lacaniano, concluimos que la nominación de víctima en Bojayá posibilita que sus habitantes —antes invisibilizados— existan para el Otro de la nación y del escenario internacional.

People living with Human Immunodeficiency Virus (PLHIV) have been reported to show poor facial emotion recognition. However, these studies presented participants with facial emotion photographs whereas in real life facial emotion recognition hardly involves inferring emotions from static faces. Moreover, emotion recognition from other sensory modalities, such as auditory, has hardly been explored. There’s also a dearth of studies examining emotion regulation difficulties in this group. The present study, thus, explored facial (using facial emotion videos) and auditory emotion recognition as well as difficulties in emotion regulation (using the Hindi version of Difficulties in Emotion Regulation Scale) in 60 PLHIV and 60 people without HIV (PWoHIV). Additionally, the association of HIV duration (duration since diagnosis of HIV), viral load, and Clusters of differentiation 4 (CD4) count with emotion recognition and regulation difficulties in PLHIV was explored. Findings from one-way ANCOVA (with education and socioeconomic status as covariates) revealed significantly impaired auditory emotion recognition (particularly for fear) among PLHIV than PWoHIV. The former also showed significantly poorer facial emotion recognition for surprise. PLHIV also self-reported significantly more emotion regulation difficulties than PWoHIV, specifically Nonacceptance of their response to negative emotions and limited access to emotion regulation Strategies. CD4 count was negatively correlated with emotion regulation difficulties, particularly for accomplishing goal-directed behaviour when experiencing negative emotions (Goals) and Strategies. Besides the novel addition to the literature regarding impaired auditory emotion recognition in PLHIV, these findings can help develop targeted interventions to improve emotion recognition and emotion regulation for PLHIV.

El primer intento de justicia transicional en Colombia se materializó con la Ley de Justicia y Paz (Ley 975 de 2005), en virtud del Acuerdo de Ralito entre las Autodefensas Unidas de Colombia y el gobierno colombiano. Posteriormente, con la expedición de la Ley 1592 de 2012, se generó la posibilidad de que los miembros que no cumplieran las condiciones establecidas en la Ley de Justicia y Paz o que no cumplieran con los compromisos adquiridos al someterse a dicha legislación, pudieran ser excluidos de ella y continuar siendo investigados y juzgados en la jurisdicción ordinaria. Como resultado del Acuerdo de Paz entre las Fuerzas Armadas Revolucionarias de Colombia y el gobierno colombiano, nació la Jurisdicción Especial para la Paz, mediante la cual se juzgarán los delitos cometidos durante el conflicto armado. El presente artículo pretende evaluar la posibilidad que tienen los excombatientes de grupos paramilitares que, habiéndose postulado a la Ley de Justicia y Paz y que hayan sido excluidos por no cumplir con algunos de los requisitos o por no haber cumplido con sus compromisos, puedan someterse a la Jurisdicción Especial para la Paz. Lo anterior se realiza a partir de la construcción de escenarios sobre casos icónicos de ex paramilitares excluidos de la Ley de Justicia y Paz.

In this study, we address emotion recognition using unsupervised feature learning from speech data, and test its transferability to music. Our approach is to pre-train models using speech in English and Mandarin, and then fine-tune them with excerpts of music labeled with categories of emotion. Our initial hypothesis is that features automatically learned from speech should be transferable to music. Namely, we expect the intra-linguistic setting (e.g., pre-training on speech in English and fine-tuning on music in English) should result in improved performance over the cross-linguistic setting (e.g., pre-training on speech in English and fine-tuning on music in Mandarin). Our results confirm previous research on cross-domain transferability, and encourage research towards language-sensitive Music Emotion Recognition (MER) models.

Word recognition using deep learning is a simple approach to speech recognition in general. From this word-level recognition, the emotional expression recognition model. The emotion recognition model can be used to describe the important level of action on future planned hardware implementation. This research was conducted using MFCC as the feature extraction method from the audio data and using the CNN-LSTM approach for the emotional expression classifier. The model itself will be implemented into a humanoid robot to become a companion robot for the elderly. The model itself has 67% accuracy for emotion recognition and 97% accuracy for word recognition. However, the model only attained 20% accuracy in real-life testing using the humanoid robot as the model tends to overfitting as a result of the lack of data used in model training.

Currently the study of affective computing (AC) includes a focus on researching emotion regulation and recognition. Recent studies in this field have utilized deep learning architectures to enhance emotion recognition from EEG signals. An alternative approach to deep learning is to use feature engineering to extract relevant features to train supervised machine learning models. Current theories in the neuroscience field can guide this feature engineering process. Neuroscientists have suggested various models to clarify how emotions are processed. One of these models suggests that positive emotions are processed in the left hemisphere, while negative emotions are processed in the right hemisphere. This emotional processing model has inspired previous studies to propose asymmetrical features to predict emotions. However, none of these studies have statistically evaluated whether the inclusion of asymmetrical features could yield benefits such as increased accuracy or reduced training time. To address that direction, this research presents both statistical evaluations for emotion regulation and a comparable model for emotion recognition. The outcomes show that brain hemispheres and frequency bands participate differently in processing emotions and observed the presence of the two asymmetry emotion processing models but in different frequency ranges. Also, the results from this study imply that by using asymmetry EEG, emotion recognition approaches can use fewer features without significantly compromising performance.

What our eyes tell us about feelings: Tracking pupillary responses during emotion regulation processes.