To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

A First Step toward the Automatic Understanding of Social Touch for Naturalistic Human–Robot Interaction

Social robots should be able to automatically understand and respond to human touch. The meaning of touch does not only depend on the form of touch but also on the context in which the touch takes place. To gain more insight into the factors that are relevant to interpret the meaning of touch within a social context we elicited touch behaviors by letting participants interact with a robot pet companion in the context of different affective scenarios. In a contextualized lab setting, participants (n = 31) acted as if they were coming home in different emotional states (i.e., stressed, depressed, relaxed, and excited) without being given specific instructions on the kinds of behaviors that they should display. Based on video footage of the interactions and interviews we explored the use of touch behaviors, the expressed social messages, and the expected robot pet responses. Results show that emotional state influenced the social messages that were communicated to the robot pet as well as the expected responses. Furthermore, it was found that multimodal cues were used to communicate with the robot pet, that is, participants often talked to the robot pet while touching it and making eye contact. Additionally, the findings of this study indicate that the categorization of touch behaviors into discrete touch gesture categories based on dictionary definitions is not a suitable approach to capture the complex nature of touch behaviors in less controlled settings. These findings can inform the design of a behavioral model for robot pet companions and future directions to interpret touch behaviors in less controlled settings are discussed

Toolkit for exploring ethical aspects of digital social and affective touch interactions

In this hands-on demonstration, people are invited to use a newly developed toolkit to scrutinize their attitude and preferences towards digital social and affective touch interactions

Describing complex interactions of social-ecological systems for tipping point assessments: an analytical framework

Humans play an interconnecting role in social-ecological systems (SES), they are part of these systems and act as agents of their destruction and regulation. This study aims to provide an analytical framework, which combines the concept of SES with the concept of tipping dynamics. As a result, we propose an analytical framework describing relevant dynamics and feedbacks within SES based on two matrixes: the “tipping matrix” and the “cross-impact matrix.” We take the Southwestern Amazon as an example for tropical regions at large and apply the proposed analytical framework to identify key underlying sub-systems within the study region: the soil ecosystem, the household livelihood system, the regional social system, and the regional climate system, which are interconnected through a network of feedbacks. We consider these sub-systems as tipping elements (TE), which when put under stress, can cross a tipping point (TP), resulting in a qualitative and potentially irreversible change of the respective TE. By systematically assessing linkages and feedbacks within and between TEs, our proposed analytical framework can provide an entry point for empirically assessing tipping point dynamics such as “tipping cascades,” which means that the crossing of a TP in one TE may force the tipping of another TE. Policy implications: The proposed joint description of the structure and dynamics within and across SES in respect to characteristics of tipping point dynamics promotes a better understanding of human-nature interactions and critical linkages within regional SES that may be used for effectively informing and directing empirical tipping point assessments, monitoring or intervention purposes. Thereby, the framework can inform policy-making for enhancing the resilience of regional SES

Multiancestry analysis of the HLA locus in Alzheimer’s and Parkinson’s diseases uncovers a shared adaptive immune response mediated by HLA-DRB1*04 subtypes

Across multiancestry groups, we analyzed Human Leukocyte Antigen (HLA) associations in over 176,000 individuals with Parkinson’s disease (PD) and Alzheimer’s disease (AD) versus controls. We demonstrate that the two diseases share the same protective association at the HLA locus. HLA-specific fine-mapping showed that hierarchical protective effects of HLA-DRB1*04 subtypes best accounted for the association, strongest with HLA-DRB1*04:04 and HLA-DRB1*04:07, and intermediary with HLA-DRB1*04:01 and HLA-DRB1*04:03. The same signal was associated with decreased neurofibrillary tangles in postmortem brains and was associated with reduced tau levels in cerebrospinal fluid and to a lower extent with increased Aβ42. Protective HLA-DRB1*04 subtypes strongly bound the aggregation-prone tau PHF6 sequence, however only when acetylated at a lysine (K311), a common posttranslational modification central to tau aggregation. An HLA-DRB1*04-mediated adaptive immune response decreases PD and AD risks, potentially by acting against tau, offering the possibility of therapeutic avenues

Towards Social Touch Intelligence

Touch behavior is of great importance during social interaction. Automatic recognition of social touch is necessary to transfer the touch modality from interpersonal interaction to other areas such as Human-Robot Interaction (HRI). This paper describes a PhD research program on the automatic detection, classification and interpretation of touch in social interaction between humans and artifacts. Progress thus far includes the recording of a Corpus of Social Touch (CoST) consisting of pressure sensor data of 14 different touch gestures and first classification results. Classification of these 14 gestures resulted in an overall accuracy of 53% using Bayesian classifiers. Further work includes the enhancement of the gesture recognition, building an embodied system for real-time classification and testing this system in a possible application scenario

Human perception baseline for anticipating haptic greetings in human-robot interaction

Greeting is the hallmark example of social behavior inwhich touch occurs frequently and serves an importantfunction in managing interpersonal relations by welcominga guest or acknowledging a friend [1]. Socially intelligentrobots should therefore be able to initiate and respond tohaptic greetings such as a handshake or a hug. While anticipating touch interactions from an exocentric perspective [2]is useful for some applications, social robots would requireanticipation of human actions from an egocentric perspective.To establish a baseline, a perception study was conductedto compare how accurately humans can anticipate differenttypes of haptic greetings from an egocentric and exocentricvie

What Do Older Adults and Clinicians Think About Traditional Mobility Aids and Exoskeleton Technology?

Mobility impairments can prevent older adults from performing their daily activities, which highly impacts quality of life. Powered exoskeletons, which are wearable robotic devices, can assist older adults by providing additional support to compensate for age-related decline in muscle strength. To date, little is known about the opinions and needs of older adults regarding exoskeletons, as current research primarily focuses on the technical development of exoskeleton devices and on groups with more severe mobility impairments such as people with spinal cord injuries. Therefore, the aim of this article is to inform the design of exoskeletons for older adults from a person-centered perspective. Interviews were conducted with seven older adults and six clinicians. Results indicated that exoskeletons can be a valuable addition to existing mobility devices to support older adults during walking and other actions necessary to perform their daily activities. Although reactions towards the concept of exoskeleton technology were positive, older adults did not currently perceive the need for an exoskeleton device. Exoskeletons for older adults should be easy to use; preferably, users should be able to put the device on and take it off independently. Moreover, the appearance of the exoskeleton should be as inconspicuous as possible, as most older adults do not like to advertise their need for assistive devices. At this point in time, the willingness to use exoskeleton technology will depend on personal needs and preferences

What Do Older Adults and Clinicians Think About Traditional Mobility Aids and Exoskeleton Technology?

Mobility impairments can prevent older adults from performing their daily activities, which highly impacts quality of life. Powered exoskeletons, which are wearable robotic devices, can assist older adults by providing additional support to compensate for age-related decline in muscle strength. To date, little is known about the opinions and needs of older adults regarding exoskeletons, as current research primarily focuses on the technical development of exoskeleton devices and on groups with more severe mobility impairments such as people with spinal cord injuries. Therefore, the aim of this article is to inform the design of exoskeletons for older adults from a person-centered perspective. Interviews were conducted with seven older adults and six clinicians. Results indicated that exoskeletons can be a valuable addition to existing mobility devices to support older adults during walking and other actions necessary to perform their daily activities. Although reactions towards the concept of exoskeleton technology were positive, older adults did not currently perceive the need for an exoskeleton device. Exoskeletons for older adults should be easy to use; preferably, users should be able to put the device on and take it off independently. Moreover, the appearance of the exoskeleton should be as inconspicuous as possible, as most older adults do not like to advertise their need for assistive devices. At this point in time, the willingness to use exoskeleton technology will depend on personal needs and preferences