We know too that guilt may be alleviated by private confession Theorists conceive of shame and guilt as belonging to the (namely to a priest or a psychotherapist) plus the family of self-conscious emotions (Lewis 1990) (Fischer renouncing of past failings in future. Because of their private and Tangney 1995) (Tangney and Dearing 2002), invoked character, such confessions and atonements, given through self-reflection and self-evaluation. Though both their cost (prayers or fees), render temptation defecting less have evolved to promote cooperation, guilt and shame can probable. Public or open confession of guilt can be coordinated be treated separately. Guilt is an inward private phenomenon, with apology for better effect, and the cost appertained though it can promote apology, and even spontaneous to some common good (like charity), or as individual public confession. Shame is inherently public, though it compensation to injured parties.

We study the complexity of a combinatorial variant of the Shift Bribery problem in elections. In the standard Shift Bribery problem, we are given an election where each voter has a preference order over the set of candidates and where an outside agent, the briber, can pay each voter to rank the briber's favorite candidate a given number of positions higher. The goal is to ensure the victory of the briber's preferred candidate. The combinatorial variant of the problem, introduced in this paper, models settings where it is possible to affect the position of the preferred candidate in multiple votes, either positively or negatively, with a single bribery action. This variant of the problem is particularly interesting in the context of large-scale campaign management problems (which, from the technical side, are modeled as bribery problems). We show that, in general, the combinatorial variant of the problem is highly intractable; specifically, NP-hard, hard in the parameterized sense, and hard to approximate. Nevertheless, we provide parameterized algorithms and approximation algorithms for natural restricted cases.

Human conscious flows can alter brain states. Such brain activities modulate energy consumptions, which can be manifest in the BOLD effect in fMRI experiment. The goal of this study is to identify whether there is difference in such BOLD effects between experienced Tai Chi master in meditation state and normal control subjects. In this experiment, both the meditator and the controls using their conscious to lead a flow periodically circling in their brain in axial, sagittal, and coronal orientations inside a MRI scanner. The experimental results showed significant differences between the meditator and the controls. The most important one is that the meditator activates frontal medial cortex and precuneous regions without any visual excitation, while the controls only utilize visual cortex and precuneous regions without any frontal medial excitation. These seems suggest that for performing the same tasks, the meditator is in cognitive control state, while the controls are in spatial imagination state.

This paper describes one of the future images of the sleep monitoring system. The new technology should satisfy the following requirements: (1) noninvasive, (2) low cost and (3) long-term monitoring. What we propose here is the sleep monitoring system based on the human body vibrations sensed by the mattress type pressure sensors that gradually improves its estimation performance to the particular user by learning collected data and reconstructing its classifier.%In order to learn the data, however, the system needs the vibration data mapped to the appropriate sleep stages. As the solution to the problem, we use the existing approximate sleep stage estimation method. The experimental results reveal that (1)there is only a slightly difference between the accuracies of the two classifiers; the one trained the original dataset plus PSG based sleep stage labeled data; the other one trained the original dataset plus approximate sleep stage labeled data; (2 )Adding a particular user's several days data to the training data improves the accuracy of the original classifiers. The REM estimation accuracy is 87% in maximum. From those results, the contribution of this research is suggesting the way to personalize sleep estimation, and proving the effectiveness.

Two big recent revolutions: machine learning technologies; such as “deep learning” in Artificial Intelligence (AI), and personal genome informatics in biomedical science, provide us with new opportunities for understanding human happiness. Our ongoing important challenges are to discover our own truly meaningful personal happiness with the aid of AI and personal genome technologies. We have been developing a personal genome information agent entitled MyFinder, which supports searching for our inherited talents and maximizes our potential for a meaningful life. In the MyFinder project, we have provided a crowd-sourced DIY (Do it yourself) genomics research platform and conducted various “citizen science” projects in health and wellness. In this paper, we discuss how machine learning technologies and personal genome informat-ics might contribute to happiness sciences. We introduce the “Social Intelligence Genomics and Empathy-Building Study” and report the preliminary results of applying deep learning and six other machine learning algorithms for predicting social intelligence levels from nine SNPs genetic profiles. We dis-cuss the possibilities and limitations of applying machine learning technologies for personal happiness trait prediction. We also discuss future AI challenges in the context of wellbeing computing.

Artificial intelligence (AI) systems are increasingly capable of analyzing health data such as medical images (e.g., skin lesions) and test results (e.g., ECGs). However, because it can be difficult to determine when an AI-generated diagnosis should be trusted and acted upon—especially when it conflicts with a human-generated one—many AI systems are not utilized effectively, if at all. Similarly, advances in information technology have made it possible to quickly solicit multiple diagnoses from diverse groups of people throughout the world, but these technologies are underutilized because it is difficult to determine which of multiple diagnoses should be trusted and acted upon. Here, I propose a method of soliciting and combining multiple diagnoses that will harness the collective intelligence of both human and artificial intelligence for analyzing health data.

Well-being and mental health are directly associated with relationship status particularly in the context of relatedness and support. A key factor in relationship functioning is emotional arousal. We examine the interplay between emotional arousal manifested through acoustic and physiological cues and its association to relationship satisfaction. We propose a dynamical systems model to infer the within- and across-modality as well as the between-partner relations. Our results suggest that increased emotional regulation is negatively associated with relationship satisfaction and indicate that the proposed system consists a viable framework for analyzing such multimodal interrelations within romantic partners.

This paper outlines three projects that lay the foundation for a trans-disciplinary approach to the creation of interactive, multi-sensory devices combining biofeedback, virtual reality, and physical/virtual human-machine interactions. We explore new possibilities for interoperability and enhancing interoception and mindfulness with potential research contributions for novel personal, professional and medical applications.

We have developed a visualization system of dementia care skills based on multimodal communication features. The purpose of our system is to provide effective learning of dementia care to trainees. As dementia care skills are difficult to visualize and describe, they are hard to acquire for trainees. We focus on HumanitudeR; a non-pharmacological comprehensive intervention with verbal and non-verbal communication, which is a care methodology of French-origin for the vulnerable elderlies. The multimodal methodology utilizes four techniques to relate to elderly with dementia (i.e., gaze, speak, touch, opportunities to stand on their feet). We analyzed the care videos of Humanitude instructors to extract multimodal communication features. We designed and filmed video contents demonstrating the extracted features. These have shown to be effective, in combination with practice and reflection, to acquire dementia care skills. The trainees could use the system for self-reflection and teaching.

Conference calls represent a natural but limited communication channel between people. Lack of visual contact and limited bandwidth impoverish social cues people typically use to moderate their behavior. This paper presents a system capable of providing timely aural feedback enabling meeting participants to check themselves. The system is able to sense and recognize problems, reason about them, and make decisions on how and when to provide feedback based on an interaction policy. While a hand-crafted policy based on expert insight can be used, it is non-optimal and can be brittle. Instead, we use reinforcement learning to build a system that can adapt to users by interacting with them. To evaluate the system, we first conduct a user study and demonstrate its utility in getting meeting participants to contribute more equally. We then validate the adaptive feedback policy by demonstrating the agent's ability to adapt its action choices to different types of users.