scholarly journals Neural Correlates of Trust in Automation: Considerations and Generalizability Between Technology Domains

2021 ◽  
Vol 2 ◽  
Author(s):  
Sarah K. Hopko ◽  
Ranjana K. Mehta
Keyword(s):  
Intrusion Detection ◽  
Adaptive Systems ◽  
Decision Aid ◽  
Physiological Responses ◽  
Neural Correlates ◽  
Use Cases ◽  
Continuous Measure ◽  
Human In The Loop ◽  
Current State ◽  
Trust In Automation

Investigations into physiological or neurological correlates of trust has increased in popularity due to the need for a continuous measure of trust, including for trust-sensitive or adaptive systems, measurements of trustworthiness or pain points of technology, or for human-in-the-loop cyber intrusion detection. Understanding the limitations and generalizability of the physiological responses between technology domains is important as the usefulness and relevance of results is impacted by fundamental characteristics of the technology domains, corresponding use cases, and socially acceptable behaviors of the technologies. While investigations into the neural correlates of trust in automation has grown in popularity, there is limited understanding of the neural correlates of trust, where the vast majority of current investigations are in cyber or decision aid technologies. Thus, the relevance of these correlates as a deployable measure for other domains and the robustness of the measures to varying use cases is unknown. As such, this manuscript discusses the current-state-of-knowledge in trust perceptions, factors that influence trust, and corresponding neural correlates of trust as generalizable between domains.

scholarly journals Colvis—A Structured Annotation Acquisition System for Data Visualization

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 158
Author(s):  
Pierre Vanhulst ◽  
Raphaël Tuor ◽  
Florian Évéquoz ◽  
Denis Lalanne
Keyword(s):  
Literature Review ◽  
Data Visualization ◽  
Use Cases ◽  
Acquisition System ◽  
User Engagement ◽  
Current State ◽  
Classification Framework ◽  
Technical Considerations

Annotations produced by analysts during the exploration of a data visualization are a precious source of knowledge. Harnessing this knowledge requires a thorough structure of annotations, but also a means to acquire them without harming user engagement. The main contribution of this article is a method, taking the form of an interface, that offers a comprehensive “subject-verb-complement” set of steps for analysts to take annotations, and seamlessly translate these annotations within a prior classification framework. Technical considerations are also an integral part of this study: through a concrete web implementation, we prove the feasibility of our method, but also highlight some of the unresolved challenges that remain to be addressed. After explaining all concepts related to our work, from a literature review to JSON Specifications, we follow by showing two use cases that illustrate how the interface can work in concrete situations. We conclude with a substantial discussion of the limitations, the current state of the method and the upcoming steps for this annotation interface.

The Advancement of Neuroimaging Research Investigating Developmental Stuttering

2011 ◽  
Vol 21 (3) ◽  
pp. 88-95 ◽  
Author(s):  
Deryk S. Beal
Keyword(s):  
Speech Production ◽  
Fast Rate ◽  
Neural Correlates ◽  
Future Research ◽  
Current State ◽  
Developmental Stuttering ◽  
The Brain ◽  
State Of The Field

We are amassing information about the role of the brain in speech production and the potential neural limitations that coincide with developmental stuttering at a fast rate. As such, it is difficult for many clinician-scientists who are interested in the neural correlates of stuttering to stay informed of the current state of the field. In this paper, I aim to inspire clinician-scientists to tackle hypothesis-driven research that is grounded in neurobiological theory. To this end, I will review the neuroanatomical structures, and their functions, which are implicated in speech production and then describe the relevant differences identified in these structures in people who stutter relative to their fluently speaking peers. I will conclude the paper with suggestions on directions of future research to facilitate the evolution of the field of neuroimaging of stuttering.

scholarly journals Industry quantum computing applications

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
◽  
Andreas Bayerstadler ◽  
Guillaume Becquin ◽  
Julia Binder ◽  
Thierry Botter ◽  
...  
Keyword(s):  
Quantum Computing ◽  
Use Cases ◽  
Automotive Manufacturing ◽  
Current State ◽  
Funding Program ◽  
Optimization Simulation ◽  
Funding Programs ◽  
Quantum Technology ◽  
Pharmaceutical Production ◽  
Start Ups

AbstractQuantum computing promises to overcome computational limitations with better and faster solutions for optimization, simulation, and machine learning problems. Europe and Germany are in the process of successfully establishing research and funding programs with the objective to advance the technology’s ecosystem and industrialization, thereby ensuring digital sovereignty, security, and competitiveness. Such an ecosystem comprises hardware/software solution providers, system integrators, and users from research institutions, start-ups, and industry. The vision of the Quantum Technology and Application Consortium (QUTAC) is to establish and advance the quantum computing ecosystem, supporting the ambitious goals of the German government and various research programs. QUTAC is comprised of ten members representing different industries, in particular automotive manufacturing, chemical and pharmaceutical production, insurance, and technology. In this paper, we survey the current state of quantum computing in these sectors as well as the aerospace industry and identify the contributions of QUTAC to the ecosystem. We propose an application-centric approach for the industrialization of the technology based on proven business impact. This paper identifies 24 different use cases. By formalizing high-value use cases into well-described reference problems and benchmarks, we will guide technological progress and eventually commercialization. Our results will be beneficial to all ecosystem participants, including suppliers, system integrators, software developers, users, policymakers, funding program managers, and investors.

scholarly journals Query Processing in Blockchain Systems: Current State and Future Challenges

2021 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Dennis Przytarski ◽  
Christoph Stach ◽  
Clémentine Gritti ◽  
Bernhard Mitschang
Keyword(s):  
Query Processing ◽  
Distributed Database ◽  
Database Management System ◽  
Use Cases ◽  
Healthcare Sector ◽  
Future Research ◽  
Use Case ◽  
Secure Storage ◽  
Current State ◽  
Future Challenges

When, in 2008, Satoshi Nakamoto envisioned the first distributed database management system that relied on cryptographically secured chain of blocks to store data in an immutable and tamper-resistant manner, his primary use case was the introduction of a digital currency. Owing to this use case, the blockchain system was geared towards efficient storage of data, whereas the processing of complex queries, such as provenance analyses of data history, is out of focus. The increasing use of Internet of Things technologies and the resulting digitization in many domains, however, have led to a plethora of novel use cases for a secure digital ledger. For instance, in the healthcare sector, blockchain systems are used for the secure storage and sharing of electronic health records, while the food industry applies such systems to enable a reliable food-chain traceability, e.g., to prove compliance with cold chains. In these application domains, however, querying the current state is not sufficient—comprehensive history queries are required instead. Due to these altered usage modes involving more complex query types, it is questionable whether today’s blockchain systems are prepared for this type of usage and whether such queries can be processed efficiently by them. In our paper, we therefore investigate novel use cases for blockchain systems and elicit their requirements towards a data store in terms of query capabilities. We reflect the state of the art in terms of query support in blockchain systems and assess whether it is capable of meeting the requirements of such more sophisticated use cases. As a result, we identify future research challenges with regard to query processing in blockchain systems.

scholarly journals Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

2020 ◽  
Vol 2020 (2) ◽  
pp. 5-23
Author(s):  
Sergiu Carpov ◽  
Caroline Fontaine ◽  
Damien Ligier ◽  
Renaud Sirdey
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Use Cases ◽  
Security And Privacy ◽  
Functional Encryption ◽  
Cryptographic Primitives ◽  
Encrypted Data ◽  
Current State ◽  
Encryption Schemes ◽  
Privacy Issues

AbstractClassification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear.In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.

scholarly journals Use cases for obstacle detection and track intrusion detection systems in the context of new generation of railway traffic management systems

2021 ◽  
pp. 095440972110410
Author(s):  
Paul Hyde ◽  
Cristian Ulianov ◽  
Jin Liu ◽  
Milan Banic ◽  
Milos Simonovic ◽  
...  
Keyword(s):  
Intrusion Detection ◽  
Traffic Management ◽  
Obstacle Detection ◽  
Operating Conditions ◽  
Use Cases ◽  
System Response ◽  
Detection Systems ◽  
Railway Traffic ◽  
Detailed Classification ◽  
New Generation

In this paper, the concept of Obstacle Detection and Track Intrusion Detection (OD&TID) systems related to the operation of trains is introduced, along with a potential concept for such a system. The main focus of the work presented here is the identification and description of system requirements and Use Cases (UC), their detailed classification, including general UCs for mainline railway and UCs specific to freight, as well as an analysis of the UCs and of the method used. The identified UCs have been organised with respect to the mode of operation, Grade of Automation (GoA), and operating conditions. The UCs were further analysed in different UC scenarios, including the pre-conditions, system response, actions made by OD&TID and associated systems and the post use conditions of the scenarios. The priority for implementation and complexity of each UC are discussed with respect to the probability of scenario occurrence and required interfaces. This work has been carried out as part of the process to evaluate implementation constraints, risks and requirements, and the operational scenarios of the OD&TID developed within the EU-funded Shift2Rail project SMART2, which aims to design and develop a prototype OD&TID system.

Misophonia

2021 ◽  
pp. 87-102
Author(s):  
Lisa Clark ◽  
Dean McKay
Keyword(s):  
Adverse Reactions ◽  
Physiological Responses ◽  
Emotional Responses ◽  
Obsessive Compulsive ◽  
Research Directions ◽  
Treatment Approaches ◽  
Current State ◽  
Respiratory Noises ◽  
And Behaviors

Misophonia is a recently described condition that is marked by extreme adverse reactions to select classes of trigger sounds. It has recently received attention as a putative diagnosable condition, and specifically as a member of the class of obsessive-compulsive and related disorders. Adverse reactions may include physiological responses, emotional responses, and behaviors. Common trigger sounds include gustatory and respiratory noises and other sounds created by humans but vary widely among affected individuals. This chapter describes misophonia, including coverage of other audiological conditions that need to be ruled out in potential cases, the current state of assessment, and brief consideration of treatment approaches. Future clinical and research directions are highlighted.

Human trust model of collision warning through pupillary and electromyography responses

2019 ◽  
Author(s):  
◽  
Xiaonan Yang
Keyword(s):  
Linear Regression ◽  
Collision Avoidance ◽  
Regression Models ◽  
Phase 1 ◽  
Physiological Responses ◽  
Warning System ◽  
Linear Regression Models ◽  
Fuzzy Linear Regression ◽  
Collision Warning ◽  
Trust In Automation

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] This pioneering work on human trust in automation was modeled by two main physiological measurements responses to collision avoidance warning as observed by pupil and electromyography (EMG) signals long regarded as meaningful physiological responses to danger. As an advanced driver assistance system (ADAS) becomes popular, distraction-related crashes caused by frequent false warnings make drivers' trust in ADAS is likely to deteriorate. In particular, trust is one of the most important driver cognitive characteristics that can determine the willingness to rely on and use the ADAS. Hence, it is important to investigate the driver's trust changes related to the collision warning. Previous research was limited to a single physiological response, or survey responses, and ocused on measurements of simple physical reaction instead of on human trust in automation. Accordingly, the driver's trust in a collision avoidance warning system under complex driving circumstances was not well studied. This study extended and enhanced past studies to multiple physiological responses to explore driver trust in collision warning and the role trust plays in avoidance of potential hazards and vulnerability. The purpose of this research was to assess drivers' dynamic learned trust of a collision avoidance warning system through physiological responses. In this multi-phase study, the Tobii eye-tracking device and Myo armbands were used to collect pupillary and EMG responses. During phase 1 study, aftermarket ADAS devices were used to collect drivers' natural responses to the collision warnings under open road real driving. A significant pattern changing of pupil EMG data only exits when drivers responded to warning. The findings of phase 1 demonstrated that pupillary and electromyography responses could be used together as effective indicators when drivers received valuable information and chose to make a physical response to the warning. The study noted that drivers often responded only to a warning in which they identified a potential hazard in situations characterized by uncertainty and vulnerability. As the lab offering an opportunity for simulated danger while studies in natural environments occur under conditions that are largely safe, the phase 2 study was designed as laboratory-based with under controlled environmental factors, to reveal the underlying pupillary and electromyography responses under potential hazards. For the model development, the time series features of pupil dilation and EMG data were extracted as independent variables, while the frustration based trust level was set as a dependent variable. Fuzzy linear regression models were built as quantitative measures of drivers' trust in the collision warning by using pupillary and EMG data. Classification rates of different fuzzy linear regression models were compared to the traditional linear regression model in both development and validation scenarios. Results indicate that the prediction models of drivers' trust, is improved upon by this study's possibility linear regression method (PLR) with waveform length time-series feature of pupil and EMG data as inputs, to more effectively predict drivers' trust in their collision warning system. New understanding of human dynamic learned trust in collision warning systems may provide benefits by improving driving safety and the usability of ADAS. Results from this study could contribute to future software algorithm development in a next-generation smart vehicle that can identify not only potential surrounding hazards, but also drivers' trust status, in order to provide a safer driving experience. Additionally, the findings of this study are anticipated to lead to the improvement of collision warning system development to enhance safety and improved device-user interaction.

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