In the field of self-help groups for recovering from developmental disorders or alcohol dependence or the other problem, a meeting of non-crosstalk style has been used. On the meeting style, participants speak about one topic without conversation with the other participants, and advising and asking to others are not recommended besides. We are proposing an online meeting system that is specialized to support non-crosstalk style meeting. Now, we would like to develop an online agent who can perform like a human participant or more useful for participants. Since the developments of above online meeting system and autonomous agents contribute to supporting many people in the self-help field, this study has an impact in the designing methods for making better well-being space environment. In order to inspire human behaviors to the agent, this paper shows analysis the results of online humans meetings using our proposed system. As the experimental results which were compared with a classical style system, it has revealed about proposed system as follows: (1) frustrating with rules of non-crosstalk is small, (2) conversational speech didn’t reduce, (3) conversational speeches were increasing along with the time but they are leading to prevent from decreasing the number of speeches, and (4) more sensitive to the speed of displaying speeches.

This paper proposes a novel method to estimate sleep stage in real-time with a non-contact device. The proposed method employs the trigonometric function regression model to estimate prospective heart rate from the partially obtained heart rate and calculates the sleep stage from the estimated heart rate. This paper conducts the subject experiment and it is revealed that the proposed method enables to estimate the sleep stage in real-time, in particular the proposed method has the equivalent estimation accuracy as the previous method that estimates the sleep stage according to the entire heart rate during sleeping.

Highly sensitive low noise electrodes, capability of fast processing of multivariate signals, low cost of hardware and wireless communication have widen the possibilities of the use of electroencephalogram (EEG) data for various applications. These applications are not only restricted to medical investigations (like epileptic seizures, monitoring anesthesia or brain functions etc.), but also for well-being of disabled patients, as well as for entertainments like playing games. Our focus in this study is BCI applications based on identification of Event Related Potential (ERP) P300. One such application is BCI speller, which is used in our experiments. BCI speller on the market use 8 probes and take 72 seconds to collect data to reliably spell a single character. The motivation of this work is to reduce the number of probes and the time needed to spell a letter. A commercial product should reliably work for every user (customer). Such a large number of probes and long time to spell a letter are necessary to ensure correct recognition. We have shown that if we identify the position of the probes appropriately for an individual, as few as two probes could give even better results. All experiments are conducted at our in-house facility, where most ot the subjects undergone no prior training.

Due to the advances in medical science, increasing health consciousness, improved quality of food, the average human life span has increased to a great extent. On the other hand, stresses of modern life, overwork and less sleep, increased usage of digital devices and internet, less exercise, are leading us to poor quality of life. Elderly people are more vulnerable to reduced life quality due to deterioration of both physical and mental health. People at any age need to maintain a minimum level of wellbeing to pursue his or her daily activities to lead a fulfilling life. Thus the need of assessing and restoring wellness is very important. Fortunately the progress of information and communication technologies provide use sensor devices and computing platform to feel, monitor and restore the wellness. In this work, a study has been done to define and determine wellness related to daily activities data obtained from various sensors and provide recommendation to the user regarding improvement of life style to achieve wellness. A small-scale experiment has been done using a simple lifelog device. The daily activities data including walking steps, sleep time, inactive period, calories burned are collected from 8 subjects. In addition food intake, eating times, cell phone usage, messaging time, time of interaction with other people and solo time are also manually collected. The correlation of physical activities (walking time, exercise time), mental activities (cell phone usage, study time, interaction with friends) and sleep patterns are studied. A simple parameter Tiredness Factor has been proposed to determine wellness and a recommendation system for improving wellness has been developed. Questionnaire from the subjects about the personal feelings of wellness has been noted and used to evaluate our proposal.

Texts posted on the web by users from diverse cultures provide a nearly endless source of data that researchers can use to study human thoughts and language patterns. However, unless care is taken to avoid it, models may be developed in one cultural setting and deployed in another, leading to unforeseen consequences. We explore the effects of using models built from a corpus of texts from multiple cultures in order to learn about each represented people group separately. To do this, we employ a topic modeling approach to quantify open-ended writing responses describing personal values and everyday behaviors in two distinct cultures. We show that some topics are more prominent in one culture compared to the other, while other topics are mentioned to similar degrees. Furthermore, our results indicate that culture influences how value-behavior relationships are exhibited. While some relationships exist in both cultural groups, in most cases we see that the observed relations are dependent on the cultural background of the data set under examination.

Social media is increasingly populated with bots. To protect the authenticity of the user, experience machine learning algorithms are used to detect these bots. Ethical dimensions of these methods have not been thoroughly considered yet. Taking histogram analysis of Twitter users' profile images as example, the paper demonstrates the trade-offs of accuracy, transparency, and robustness. Because there is no general optimum in ethical considerations, these dimensions form a "devil's triangle".

Dialog between drivers and speech-based robot vehicle interfaces can be used as an instrument to find out what drivers might be concerned, confused or curious about in driving simulator studies. Eliciting ongoing conversation with drivers about topics that go beyond navigation, control of entertainment systems, or other traditional driving related tasks is important to getting drivers to engage with the activity in an open-ended fashion. In a structured improvisational Wizard of Oz study that took place in a highly immersive driving simulator, we engaged participant drivers (N=6) in an autonomous driving course where the vehicle spoke to drivers using computer-generated natural language speech. First, using microanalyses of drivers’ responses to the car’s utterances, we identify a set of topics that are expected and treated as appropriate by the participants in our study. Second, we identify a set of topics and conversational strategies that are treated as inappropriate. Third, we show that it is just these unexpected, inappropriate utterances that eventually increase users’ trust into the system, make them more at ease, and raise the system’s acceptability as a communication partner.

The increasing mere presence of robots in everyday life does not automatically result in gradual acceptance of these systems by human users. Over the past years, we have conducted several studies with the goal to provide insight into the long-term process of social robots in domestic environments. This paper presents our overall conclusions from the combined findings of our multiple studies on social robot acceptance. We will provide insights from a user’s perspective of what makes robots social, describe a phased framework of the long-term process of robot acceptance, present some key factors for social robot acceptance, offer guidelines to build better sociable robots, and provide some recommendations for conducting research in domestic environments. With sharing our experiences with conducting (long-term) user studies in domestic environments, we aim to serve to push this sub-field of HRI in real-world contexts forward and thereby the community at large.

We present a new algorithm called PIEM to approximately solve for the policy of an infinite-horizon decentralized partially observable Markov decision process (DEC-POMDP). The algorithm uses expectation maximization (EM) only in the step of policy improvement, with policy evaluation achieved by solving the Bellman's equation in terms of finite state controllers (FSCs). This marks a key distinction of PIEM from the previous EM algorithm of (Kumar and Zilberstein, 2010), i.e., PIEM directly operates on a DEC-POMDP without transforming it into a mixture of dynamic Bayes nets. Thus, PIEM precisely maximizes the value function, avoiding complicated forward/backward message passing and the corresponding computational and memory cost. To overcome local optima, we follow (Pajarinen and Peltonen, 2011) to solve the DEC-POMDP for a finite length horizon and use the resulting policy graph to initialize the FSCs. We solve the finite-horizon problem using a modified point-based policy generation (PBPG) algorithm, in which a closed-form solution is provided which was previously found by linear programming in the original PBPG. Experimental results on benchmark problems show that the proposed algorithms compare favorably to state-of-the-art methods.

In a time of pervasive and increasingly transparent computing, humans will interact with information objects and less and less with the computing devices that define them. Artificial Intelligence (AI) will be the proxy for humans’ interaction with information. Because interaction creates opportunities for error, the trend towards AI-augmented human information interaction (HII) will mandate an increased emphasis on cognition-oriented information science research and new ways of thinking about errors and error handling. A review of HII and its relationship to AI is presented, with a focus on errors in this context.