Academic literature on the topic 'Physical human-machine coordination'

Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after a surprise, or unexpected event. These biases further influence people’s trust in their partners, including machine partners (Muir, 1987; Madhavan & Wiegmann, 2004). Advances in robotics have allowed for robots to partner with people at work and be treated socially (Young, Hawkins, Sharlin & Igarashi, 2009). However, these advances may interfere with a person’s appropriate calibration of trust in robots (Parasuraman & Miller, 2004). A better understanding of attribution biases in the wake of an unexpected event may shed light on how trust develops in a robot partner. This study was built on a human coordination example to serve as a reference for future human-robot interactions. We posit that attribution biases lead people to blame their partner after experiencing a negative performance outcome, thus lowering their trust in the partner. Sixty participants (30 pairs) were tasked to coordinate with an unfamiliar human partner, to lift a 17.5 lb. box containing a 200ml cup of water filled to the brim, from the floor to a table, as quickly as possible without spilling water. Before the task, participants were told that the pair with the best performance would be rewarded; however, all pairs were told they did not achieve this. Participant pairs were randomly assigned to a surprise condition during which they heard a 250 Hz warning tone, or a baseline condition with no warning tone. Participants in both conditions were told to pause the task as quickly as possible if the warning tone was present. It was unknown to participants when or if a warning tone would occur. To assess participants’ trust in their partner, Muir’s (1987) trust questionnaire was administered twice, once after introducing the task to participants, and again after the coordination task was completed. To capture blame assignment, a scale based on Kim and Hinds (2006) was administered after participants were told they did not achieve the best performance. Results indicate participants were less likely to blame their partners for the negative outcome, compared to blaming themselves or the warning tone itself (in the surprise condition). Next, surprisingly, in the surprise condition, instead of experiencing a decrease of trust in a partner after the negative outcome, there was a significant increase in trust in their partners. No significant difference in trust was found in the baseline condition. Finally, results also indicate that initial trust in a partner is a significant predictor for how people assign blame. In general, the effects of attribution biases were not observed in the present study. Friendliness may be a factor in people’s assignment of blame; although participants were unfamiliar with one another, all participants were students at the same university. Second, shared experience during the surprise condition, including the chance to assess their partner’s behaviors in response to the warning tone, may have been a catalyst for increased trust in a partner. It is important to note that although physical differences between participants were not evaluated in this study, height may be a potential confounding factor in this task. These findings enlighten our understanding of physical human-robot coordination scenarios and trust in a partner.

Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

As manufacturing industry heads toward the socialization and mass individualization, customer requirements have become personalized and dynamic. Socialized manufacturing resources spring up in different finer-grained market segments to provide various manufacturing services for customers. To facilitate the mass business collaboration, these resources and customers need to be cyber-physical-social interconnected. This article proposes a cyber-physical-social system for the personalized product production in a social manufacturing environment, which incorporates social sensors in the human end, cyber-physical system nodes in the machine end, and smart products in the product end for social interaction and distributed production control. The three-layer framework of cyber-physical-social system and three-stage interaction scenarios are discussed. The multi-role distributed production control mechanism is studied to enhance the agility, responsiveness, flexibility, and coordination capability of the cyber-physical-social system–enabled personalized product production system. Cyber-physical-social system leverages the global cyber-physical-social convergence and the local regional autonomy for the personalized product production. It is expected that this article will contribute to the research areas of industry 4.0-based manufacturing mode innovation and intelligent production process control.

Human operated, hydraulic actuated machines are widely used in many high-power applications. Improving productivity, safety and task quality (e.g., haptic feedback in a teleoperated scenario) has been the focus of past research. For robotic systems that interact with the physical environments, passivity is a useful property for ensuring safety and interaction stability. While passivity is a well utilized concept in electromechanical robotic systems, investigation of electrohydraulic control systems that enforce this passivity property are rare. This paper proposes and experimentally demonstrates a teleoperation control algorithm that renders a hydraulic backhoe/force feedback joystick system as a two-port, coordinated, passive machine. By fully accounting for the fluid compressibility, inertia dynamics and nonlinearity, coordination performance is much improved over a previous scheme in which the coordination control approximates the hydraulic system by its kinematic behavior. This is accomplished by a novel bond graph based three step design methodology: (1) energetically invariant transformation of the system into a pair of “shape” and “locked” subsystems; (2) inversion of the shape system bond graph to derive the coordination control law; (3) use of the locked system bond graph to derive an appropriate control law to achieve a target locked system dynamics while ensuring the passivity property of the coordinated system. The proposed passive control law has been experimentally verified for its bilateral energy transfer ability and performance enhancements.

In recent years collaborative robots have become major market drivers in industry 5.0, which aims to incorporate them alongside humans in a wide array of settings ranging from welding to rehabilitation. Improving human–machine collaboration entails using computational algorithms that will save processing as well as communication cost. In this study we have constructed an agent that can choose when to cooperate using an optimal strategy. The agent was designed to operate in the context of divergent interest tacit coordination games in which communication between the players is not possible and the payoff is not symmetric. The agent’s model was based on a behavioral model that can predict the probability of a player converging on prominent solutions with salient features (e.g., focal points) based on the player’s Social Value Orientation (SVO) and the specific game features. The SVO theory pertains to the preferences of decision makers when allocating joint resources between themselves and another player in the context of behavioral game theory. The agent selected stochastically between one of two possible policies, a greedy or a cooperative policy, based on the probability of a player to converge on a focal point. The distribution of the number of points obtained by the autonomous agent incorporating the SVO in the model was better than the results obtained by the human players who played against each other (i.e., the distribution associated with the agent had a higher mean value). Moreover, the distribution of points gained by the agent was better than any of the separate strategies the agent could choose from, namely, always choosing a greedy or a focal point solution. To the best of our knowledge, this is the first attempt to construct an intelligent agent that maximizes its utility by incorporating the belief system of the player in the context of tacit bargaining. This reward-maximizing strategy selection process based on the SVO can also be potentially applied in other human–machine contexts, including multiagent systems.

The coordination of humanitarian relief, e.g. in a natural disaster or a conflict situation, is often complicated by a scarcity of data to inform planning. Remote sensing imagery, from satellites or drones, can give important insights into conditions on the ground, including in areas which are difficult to access. Applications include situation awareness after natural disasters, structural damage assessment in conflict, monitoring human rights violations or population estimation in settlements. We review machine learning approaches for automating these problems, and discuss their potential and limitations. We also provide a case study of experiments using deep learning methods to count the numbers of structures in multiple refugee settlements in Africa and the Middle East. We find that while high levels of accuracy are possible, there is considerable variation in the characteristics of imagery collected from different sensors and regions. In this, as in the other applications discussed in the paper, critical inferences must be made from a relatively small amount of pixel data. We, therefore, consider that using machine learning systems as an augmentation of human analysts is a reasonable strategy to transition from current fully manual operational pipelines to ones which are both more efficient and have the necessary levels of quality control. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations’.

The rehabilitation robot is an application of robotic technology for people with limb disabilities. This paper investigates a new applicable and effective sitting/lying lower limb rehabilitation robot (the LLR-Ro). In order to improve the patient’s training initiative and accelerate the rehabilitation process, a new motion intention acquisition method based on static torque sensors is proposed. This motion intention acquisition method is established through the dynamics modeling of human–machine coordination, which is built on the basis of Lagrangian equations. Combined with the static torque sensors installed on the mechanism leg joint axis, the LLR-Ro can obtain the active force from the patient’s leg. Based on the variation of the patient’s active force and the kinematic functional relationship of the patient’s leg end point, the patient motion intention is obtained and used in the proposed active rehabilitation training method. The simulation experiment demonstrates the correctness of mechanism leg dynamics equations through ADAMS software and MATLAB software. The calibration experiment of the joint torque sensors’ combining limit range filter with an average value filter provides the hardware support for active rehabilitation training. The consecutive variation of the torque sensors from just the mechanism leg weight, as well as both the mechanism leg and the patient leg weights, obtains the feasibility of lower limb motion intention acquisition.

Functional safety networks are becoming of paramount importance in industrial systems, due to the progressive innovation introduced by the Industry 4.0 paradigm, characterized by high production flexibility, reliability and scalability. In this context, new and challenging applications have emerged such as hyperautomation, which refers to the combination of machine vision, robotics, communication, and learning, with the explicit involvement of humans. This requires the pervasive and ubiquitous connectivity encompassed by the Industrial Internet of Things, typically achieved via wireless systems. As an example, wireless communications are today fundamental to open up to new categories of autonomous devices that can actively collaborate with human personnel in the production process. This challenging scenario has important implications for safety. Indeed, a reliable coordination among sensors, actuators and computing systems is required to provide satisfactory levels of safety, especially in the case of innovative processes and technologies, such as mobile and collaborative robotics. Hence, it becomes imperative to ensure the correct transfer of safety-critical data via communication networks. In this paper, we address the challenges concerned with functional safety networks and protocols in Industrial Internet of Things ecosystems. We first introduce the design characteristics of functional safety networks and discuss the adoption of safety protocols over wireless networks. Then, we specifically address one of such protocols, namely Fail Safety over EtherCAT (FSoE), and provide the results of an extensive experimental session carried out exploiting a prototype system, implemented using commercial devices based on a WiFi network. Finally, the outcomes of the experiments are used as a basis for a discussion about future trends of functional safety in the Industrial Internet of Things era.

OBJECTIVES/SPECIFIC AIMS: Electronic health records have become the fulcrum for efforts by institutions to reduce errors, improve safety, reduce cost, and improve compliance with recommended guidelines. In recent times they are also being considered as a potential game changer for improving patient recruitment for clinical trials (CT). Although the use of CDS for clinical care is partially understood, its use for CT patient identification and recruitment is young and a great deal of experimental and theoretical research is needed in this area to optimize the use of CDS tools that personalize patient care by identifying relevant clinical trials and other research interventions. The use of CDS tools for CT recruitment offers a great deal of possibilities, but some initial usage has been disappointing. This may not be surprising because, while the implementation of these interventions is somewhat simple, ensuring that they are embedded into the right point of the care providers workflow is highly complex and may affect many actors in a clinical care setting, including patients, nurses, physicians, clinical coordinators, and investigators. Overcoming the challenges of alerting providers regarding their patient’s eligibility for clinical trials is an important and difficult challenge. Translating that effort into effective recruitment will require understanding of the psychological and workflow barriers and facilitators for how providers respond to automated alerts requesting patient referrals. Evidence from using CDS for clinical care that shows alerts become increasingly ignored over time or with more exposure (1, 2). The features, timing, and method of these alerts are important usability factors that may influence effectiveness of the referral process. Focus group methods capture the shared perspectives of a phenomenon and have been shown to be an effective method for identifying perceptions, attitudes, information needs, and other human factors effecting workflow (3, 4). Our objective was to develop a generalizable method for measuring physician and clinic level factors defining a successful point of care recruitment program in an outpatient care setting. To achieve this we attempted to (a) Characterize provider’s attitudes regarding CTs referrals and research. (b) Identify perceived workflow strategies and facilitators relevant to CT recruitment in primary care. (c) Develop and test a pilot instrument. METHODS/STUDY POPULATION: The methods had 3 phases: focus groups, development of item pool, and tool development. Focus group topics were developed by 4 experienced investigators, with training in biomedical informatics, cognitive psychology, human factors, and workflow analysis, based upon a knowledge of the literature. A script was developed and the methods were piloted with a group of 4 clinicians. In all, 16 primary care providers, 5 clinic directors, and 6 staff supervisors participated in 6 focus groups, with an average of 5 participants each, to discuss clinical trial recruitment at the point of care. Focus groups were conducted by the development team. Audio recording were content coded and analyzed to identify themes by consensus of 3 authors. Item Pool generation involved extracting items identified in the focus group analysis, selecting a subset deemed most interesting based on knowledge of the recruitment literature and iteratively writing and refining questions. Instrument development consisted of piloting an initial 7-item questionnaire with a local primary provider sample. Questions were correlated with the item pool and limited to reduce provider burden, based on those that the study team deemed most applicable to information technology supported recruitment. Descriptive statistical analysis was performed on the pilot survey results. An online survey was developed based on the findings of the focus groups and emailed to 127 primary care providers who were invited to participate. In total, 36 questionnaires were completed. This study was approved by the University of Utah Institutional Review Board. RESULTS/ANTICIPATED RESULTS: The results section is organized into 3 sections: (a) Focus groups, (b) Item generation; and (c) Questionnaire pilot. (I) (1) Focus Groups. Themes identified through a qualitative review are presented below with illustrative comments of participants. The diversity of attitudes and willingness to support clinical trial recruitment varied so substantially that no single pattern emerged. Attitudes ranged from enthusiastic support, to interest in some trials to disinterest or distrust in trials in general. Compensation for time spent, which could be monetary, informational, or through professional recognition; and provider relationship with the study team or pre-selection of specific trials by a clinic oversight committee, and importance to providers practice positively affected willingness to help recruit. “I would love to get people into clinical trials as much as possible... If it works for them you are going to help a whole lot of other people.” If we felt like we have done every possible thing that was already established as evidence-based and it didn’t work out, then we would consider the trials. I think that studies are more beneficial for specific specialists... There might be a whole slew of things that I never deal with or don’t care about because it’s not prevalent for my patient population. Local and reputable... A long distance someone asking to do something is just not the same as someone in the trenches with you. The bottom line is how much work is involved at our end and if there is going to be any compensation for that. I think also the providers would like have feedback on what they referred them to. And how did it go? So did we pick the right patient? ... It helps us to know, did they even sign up for the study? Getting your name on a research paper would be nice too. Lack of information regarding trials reduced support for recruitment of patients. Providers stated that they do not know how to quickly find information about studies, nor do they have time to find the information, and therefore cannot efficiently council patients regarding trial participation. Notifications regarding clinical trials that were deemed to be important included: Trial coordinator intention to recruit patients, enrollment of a patient in a clinical drug trial, trial progress and result updates, and reports of effectiveness of provider recruitment efforts. Perceived information needs regarding trials that providers are referring patients to included: trial purpose, design, benefits and risks, potential side effects, intervention details, medication class (mechanism of action), drug interactions with study drug, study timeline, coordinator contact information, link to print off patient handouts, enrollment instructions, and a link to study website. (2) It’s just we don’t know any of the information ... and it can’t take any of our time. ... I don’t have time to research it. Sometimes the patients ask me questions about it and I would like to be in a position where I have some information about it before I am asked. It would be nice to be notified if they [my patients] are enrolled in the trial, when it turns into actual recruitment. I do like to know if they’re in [a trial] so that when they come in for problems, I at least know that they might be on a study medication so I can be safe. I’ll get an ER message, “The patient got admitted. There blood pressure’s, you know, tanked, because they’re on a study drug I didn’t know anything about.” if there’s certain side effects that I need to be watching out for. It would also be good to have a contact person from the study in case we need to notify them of. “this person’s possible having an adverse event. Look into it more.” (3) Provider burden associated with patient recruitment appeared to be a deterrent. These burdens included adding to the providers task list, increasing the time required to complete a visit, and usurpation of control over the patients care plan with the associated effect on provider quality scores. We don’t have time. I mean, we don’t even take a lunch break. I have 15 minutes and now this is taking this many minutes away from my 15 minutes. I am just sick of extra work. We already have so much extra work. It’s just more stuff to do. We are maxed out on stuff to do. Right now, part of our compensation depends on having our patients A1Cs controlled. And so if we’re taking a chance that maybe they’re getting a medicine, maybe they’re not, maybe it’ll help, maybe it won’t, its gonna further delay our ability to get paid. Cause they’re like “I’m not going to let you go mess up my patient and I’m going to have to deal with the consequences is kind of the way they think. If you’re going to put the patient in a study, being able drop them from our registry so we don’t get penalized for a negative outcome [is important]. (4) Patient’s needs were a priority among factors influencing likelihood to help recruitment patients. Providers considered perceived benefit or risk to the patient, such as additional healthcare services, increased monitoring, financial assistance, or access to new treatments when other options have been ineffective, important; as well as continuance of established care that has proven effective, and ethical recruitment that addresses language and mental health to ensure that patients can make decisions regarding study participation. If there’s something great that’s gonna benefit a patient, I would definitely wanna know about it to give them that option. You know that’s what we wanna try to do is make our patients better. Someone who is really well controlled and doing well, I would not tend to put them toward the study. Just keep going with what’s working right now. Sometimes there’s financial incentives for them to participate, so you know, if its a good fit its easy to at least offer that to the patient. They get treatment maybe that they can’t afford. You don’t want to be seen as somebody who's forcing a patient... if their provider is telling them this is a good idea you are more likely to get your patient to do it. I think they have to understand what a clinical trial is, first of all, in that it’s a trial. Right? We’re trying to figure out if a certain treatment is good or not. It may not work. It may work. With many patients, they don’t only have medical problems, but significant mental illness that sometimes interferes a lot with just our treatment of them here for their clinical problems. And so, that probably would interfere with someone’s ability to understand and consent to a trial. And the patients have the right to make that choice. I don’t need to be—I don’t mind influencing them on things I know about, I think are invaluable, but I don’t need to be a barrier to them. (5) Perceived responsibility in trial recruitment varied substantially, from no involvement at all, to prescreening, counseling, or recruiting patients. Some providers felt that they should have the right to say “no” to recruitment of their patients while others believed prescreening was an unnecessary burden, outside of their role as a primary care provider. if someone prescreens and thinks its appropriate and gives me that judgment call to say, do you think it would be a good fit? I think one of them, they sent, and I said, Oh, I don’t think it would be a good fit because of this...So that would be fine. I don’t think I need to be a gatekeeper for studies. I mean, if there’s people that qualify for a study, and there’s a great study that’s been approved, and they can recruit them without me knowing, that doesn’t bother me in the slightest. I liked how it was—I could do a simple referral ... someone else figured out the qualifications. if we knew of ongoing studies and if we thought a certain patient may qualify for a certain study, we just contact the coordinator, and then they just take care of the rest. I think that appropriate ... from our perspective, would be, “Are you interested?” “This is the number for a person who can sit with you, talk with you about a trial, tell you everything about it, answer your questions, and then you can make a decision.” I’m not going to let you go mess up my patient and I’m going to have to deal with the consequences. (6) A clinic-implementation approach that systemizes workflow, limits the number of trials providers are asked to recruit for, and minimizes provider time burden is needed. Suggested methods for informing providers of patient clinical trial eligibility included: email, alerts, in-basket messages, texts, phone-calls, and in-person contact. People are so sick of change, change, change, change ... if there’s no stability whatsoever, then people get frustrated and start to burn out. Having my staff remember how to do it correctly and I remember what studies we have going ... it becomes somewhat of a burden... it’s hard for us to remember as we are flying through our day. There just needs to be a clear understanding with those roles... Who does the patient call? We don’t want to look like we don’t know what we are doing. There probably should be a selection committee put together from various people who have stakes in the community, at least who can say, “This would be applicable for xx clinic.” (7) Provider Suggestions Providers had multiple suggestions regarding notification methods. (II) Development of item pool and construction of questionnaire The specific items were constructed from literature review on physician’s attitudes and results from the focus group. The overarching concern was on readability, brief questionnaire size, and relevance. A large item were constructed and then reduced through piloting. (III) Questionnaire Pilot Results: The 7-item pilot questionnaire was completed by 36 physicians (28% response rate). In this section, we report the empirical results. DISCUSSION/SIGNIFICANCE OF IMPACT: Discussion Relevance of Methods. Overall, the described methods for determining components for a recruitment program in primary care shows early promise. The focus groups that consisted of providers, staff and administrators resulted in insights as to workflows, attitudes, and clinical processes. These insights significantly varied across clinics. This variation supported the need for an individualized clinic-based approach that will meet local needs. During the course of the study, participants were willing to participate in all activities (although some requested payment). We were able to conduct the focus groups as scheduled and obtained the desired input. The analysis of the focus group transcripts was performed using iterative discussions and did not needed any special adaptation for this area of study. The pilot survey response rate was within the expected for this type of study. Focus groups can rapidly provide rich information regarding attitudes and other factors affecting provider participation at the point of care. However, findings from focus groups must always be confirmed through larger studies. It is important to keep the focus groups small and to hold multiple focus groups to offset the more vocal participants that may influence comments of others. This study shows that using our 3-step approach it is possible to gather important information on clinician’s and staff perceptions and needs to participate in point of care patient recruitment for CT. The focus groups also provide an important step for survey construction. Designing surveys empirically requires multiple validation efforts, which will be conducted in the future. However, we can draw preliminary conclusions from the results of the pilot study which are quite informative and they are discussed below. Near future work will be to expand the response rate through additional local survey and conduct formal psychometric testing and validation both locally and nationally. A final validation will be proposed through the CTSA consortiums. Variation in responses. There was a lack of normal curves in our survey results. This points to the need to target education and recruitment efforts by provider type (with similar perspectives). Identification of these types would be useful. Some specific points regarding variability that should be considered in program design. Preferences for trail recruitment methods. Many trial recruitment notification methods have the potential to be successful when used judiciously and done well, particularly if the trial coordinator/provider relationship is supported by reciprocal benefits to the provider. Consistency in workflow within seems paramount to success. Providers can pull some notifications at a time they choose, while other notifications interrupt and must be used sparingly. Some allow review of multiple patients at the same time, and some foster easy access to the patient’s medical record. Conclusions. The authors recommend that recruitment HIT be customizable at the clinic and provider level by responsibility and interest to allow selection of level of information, delivery method, that is, email, text, in-basket, alert, dashboard, mail; frequency of notification, and an opt out feature. These customizable options will allow for better support of clinic workflow or goals. There is the potential with machine learning technology to monitor provider interactions with trial notifications and for the system to automatically make adjustments to the method and level that best supports each physician. Limitations: The major limitation is the focus on one site only and one delivery system (university based). The low response makes generalization difficult. Efforts to improve the rate are underway. Many populations are under-represented in Utah. Full psychometric analysis was not conducted but will part of the final project.

abstract: Reading partners’ actions correctly is essential for successful coordination, but interpretation does not always reflect reality. Attribution biases, such as self-serving and correspondence biases, lead people to misinterpret their partners’ actions and falsely assign blame after an unexpected event. These biases thus further influence people’s trust in their partners, including machine partners. The increasing capabilities and complexity of machines allow them to work physically with humans. However, their improvements may interfere with the accuracy for people to calibrate trust in machines and their capabilities, which requires an understanding of attribution biases’ effect on human-machine coordination. Specifically, the current thesis explores how the development of trust in a partner is influenced by attribution biases and people’s assignment of blame for a negative outcome. This study can also suggest how a machine partner should be designed to react to environmental disturbances and report the appropriate level of information about external conditions.
Dissertation/Thesis
Masters Thesis Human Systems Engineering 2019

abstract: Human-robot interaction has expanded immensely within dynamic environments. The goals of human-robot interaction are to increase productivity, efficiency and safety. In order for the integration of human-robot interaction to be seamless and effective humans must be willing to trust the capabilities of assistive robots. A major priority for human-robot interaction should be to understand how human dyads have been historically effective within a joint-task setting. This will ensure that all goals can be met in human robot settings. The aim of the present study was to examine human dyads and the effects of an unexpected interruption. Humans’ interpersonal and individual levels of trust were studied in order to draw appropriate conclusions. Seventeen undergraduate and graduate level dyads were collected from Arizona State University. Participants were broken up into either a surprise condition or a baseline condition. Participants individually took two surveys in order to have an accurate understanding of levels of dispositional and individual levels of trust. The findings showed that participant levels of interpersonal trust were average. Surprisingly, participants who participated in the surprise condition afterwards, showed moderate to high levels of dyad trust. This effect showed that participants became more reliant on their partners when interrupted by a surprising event. Future studies will take this knowledge and apply it to human-robot interaction, in order to mimic the seamless team-interaction shown in historically effective dyads, specifically human team interaction.
Dissertation/Thesis
Masters Thesis Engineering 2019

In three specific model instantiations, this chapter demonstrates how URANOS can be applied to other research domains. The first instantiation, referred to as the body-mind continuum, addresses humans as holistic and spiritual beings embedded in their natural, informational and socio-cultural environments. The second instantiation provides a framework for integral thinking and designing based on the AQAL-model from K. Wilber (2007). The third instantiation addresses holistic and cognitive coordination processes in the context of multi-agent and cyber-physical systems. These three instantiations together build the core of our human-centered modeling approach. Each of them holds our generic system model at its core, but at the same time has its own specific extensions.

The essence of cloud computing is moving out the processing from the local systems to remote systems. Cloud is an umbrella of physical/virtual services/resources easily accessible over the internet. With more companies adopting cloud either fully through public cloud or hybrid model, the challenges in maintaining a cloud capable infrastructure is also increasing. About 42% of CTOs say that security is their main concern for moving into cloud. Another problem, which is mainly problem with infrastructure, is the connectivity issue. The datacenter could be considered as the backbone of cloud computing architecture. Handling this new generation of requirements of volume, variety, and velocity in IoT data requires us to evaluate the tools and technologies. As the processing power and storage capabilities of the end devices like mobile phones, routers, sensor hubs improve, we can increase leverage these resources to improve your quality and reliability of services. Applications of fog computing is as diverse as IoT and cloud computing itself. What IoT and fog computing have in common is to monitor and analyse real-time data from network connected things and acting on them. Machine-to-machine coordination or human-machine interaction can be a part of this action. This chapter explores fog computing and virtualization.