bluuebunny/crossref_metadata_2025 huggingface dataset:
Human Factors Hazards in aviation operating environment during COVID -19
Authors: Maria Arnaldo Valdés Rosa , Fernando Gómez Comendador Victor , Zamarreño Maria , Perez Moreno Francisco , Delgado-Aguilera Raquel
Published: June 2022

COVID 19 has become a mayor and without precedent disruption in aviation, with a big impact in aviation front line operators, such as crews, air traffic controller and aircraft dispatchers. This new reality has generated unexperienced human factors consequences and ups and downs in the lives of these professionals. They had to deal with new environments in their personal and professional dimensions and changes in both the physical and also psychosocial conditions as a consequence of COVID -19 impacts.The complexity of restating a complex system such as global air transport affects the staff of all aviation supply chain and has the potential to generate new and emergent hazards, a different risk landscape, and new operational and safety challenges.This paper focuses on COVID-related human factors challenges for aviation professionals, how those challenges can generate hazards that jeopardize aviation safety levels, and what can be done to mitigate the derived risks.The paper revises situations faced by operators and service providers during the COVID lockdown and the reanimation of the operations, with a focus on the best practices applied by different organisations to cope with the challenges human factors derived issues while assuring continued high levels of safety as operations ramp up.The paper combines an assessment of the best practices implemented by the industry during the last two years with a classical safety risk assessment approach. The paper revised also the safety risk assessments, about COVID 19 operations-related human factors challenges, conducted by main international aviation organisation such as as the Civil Air Navigation Services Organisation (CANSO), the International Federation of Air Traffic Controllers’ Associations (IFATCA), the International Federation of Air Line Pilots’ Associations (IFALPA) and the International Air Transport Association (IATA). Appropriate resources and tools to minimize the mental health impact of COVID-19 and ensure staff well-being throughout the lockdown and recovery phases are discussed.The paper offers a better understanding of the impact that post COVID-19 operations-related challenges could have on aviation human factors; outline new biological and psycho-social hazards and risks situation and their interactions with aviation safety; and finally identify and propose appropriate mitigation measurements for those negative consequences
Impact of Artificial Intelligence in the Certification of Human-Centered Aviation systems
Authors: Maria Arnaldo Valdés Rosa , Fernando Gómez Comendador Victor , Delgado-Aguilera Raquel , Perez Moreno Francisco , Zamarreño Maria
Published: June 2022

In recent years we have witnessed the emergence of applications based on artificial intelligence in the aviation industry. This technology is said to be promoting a new era or evolution, such as the introduction of jet engines in the 1950s and fly-by-wire in the 1980s. To maintain aviation safety standards in this transition, civil aviation authorities responsible for certifying aerospace systems must anticipate the unprecedented impact of AI on human-centric aerospace systems and answer a number of critical questions:•How to establish public trust in human- centric AI-based systems?•How to integrate the ethical dimension of human- centric AI (transparency, non-discrimination, fairness, etc.) in safety certification processes? •How to prepare for the certification of human- centric AI systems? •What standards, protocols, methods needs to be developed to ensure that human- centric AI further improves the current level of air transport safety? EASA, the European Aviation Safety Agency, has recently developed a roadmap for the certification of AI applications in aviation, which analyzes the involvement of human- centric AI in the aviation sector and identifies the objectives that must be met and the actions that must be taken to respond to the previous questions. This effort constitutes a starting point for the certification of human- centric AI in aerospace systems. It develops in particular the core notion of trustworthiness of human- centric AI in human centered systems and proposes a framework based on four human- centric AI trustworthiness building block:— trustworthiness analysis — learning assurance — explainability — safety risk mitigation The presented paper syntheses the concept of human- centric AI applications, it also discusses and revises the 4 elements of the trustworthiness of human- centric AI framework proposed by EASA, and based on this discussion anticipates the possible impacts of the introduction of human- centric AI in the different Implementation Rules (IR), Certification Specifications (CS), Acceptable Means of Compliance (AMC) and guidance material (GM) in the domains covered by the EASA Basic Regulation.
Visual attention, memory and persuasion of recycling advertisements among Chinese university students
Authors: Wai Sang Siu Betty , Xiao Bing
Published: June 2022

This research aims to use an eye-tracking methodology to explore the visual attention of recycling advertisements. The data obtained from the eye-tracking, unaided memory recall, and self-report on perceived persuasiveness can help advertisers and designers understand viewers' visual attention patterns and determine the best persuasion method when designing recycling-related advertisements. Findings showed the Pathos posters would result in long-term memory recall and high persuasiveness. However, more research is needed to see effects of using recycling relevant celebrities on the persuasiveness of Ethos posters. Central Slogans and Images were by far the most important aspect of the posters and what people spend the most time viewing. Keywords: Eye-tracking, recycling, public service advertisement, memory recall, persuasion
The Effectiveness of Amazon Headphone Video Advertising Based on Time Series Analysis
Authors: Cai Yuting , Jiang Zhengqing
Published: June 2022

Nowadays, both academia and the industry have discussed e-commerce video advertisements. However, the advertising effects and effective duration of e-commerce video advertisements mostly remain in qualitative analysis and lack quantitative analysis. In this article, using the Amazon platform, two sets of the same headset with different brand names are launched at the same time, and only one set of headsets is placed with video ads to carry out the experiment. After ten months, complete e-commerce data was obtained. Data analysis proved that: video advertising attracted a large number of consumers, which greatly increased the sales of headphones; the effective duration of video advertising was 15 weeks. This research provides an experimental method that can be quantified and analyzed for the research on the effect of video advertising on e-commerce platforms.
The future prospect of dynamic poster design in the context of new media
Authors: Wei Ding , Fan Wenqin , Liao Zhenhai
Published: June 2022

In order to explore the development and research status of dynamic posters in the context of local media, and analyze the research trend of dynamic posters in the future, it can better help scholars to study dynamic posters. This paper summarizes the dynamic new form expression of posters in the context of new media, and explores the past research directions and current research hotspots, as well as the research differences at home and abroad; This paper makes a comparative study of posters from the perspective of dynamic and static, and comes to the conclusion that it is inevitable to change from static to dynamic. From the perspective of practical application of dynamic posters, this paper analyzes the design practice process, application classification, innovative methods and technical research; This paper explores the current evaluation methods of dynamic posters. The research shows that a more systematic theoretical system based on the new media background has not been formed in this research direction, and the actual validity of dynamic posters needs to be studied and excavated. It is hoped that the research trends and shortcomings obtained from this research review can provide some practical ideas for later scholars in the research process of dynamic posters.
Application of Art-Tech in Visual Experience
Authors: Ho Amic
Published: June 2022

Since the invention of the media in the 20th century, technological advances in the field of information have revolutionised the art and science of design. Thanks to new screen devices, communication platforms, printing technologies, and software, artists and designers have unprecedented creative freedom to combine a variety of tools and techniques to generate images and ideas based on their own ideas. The emerging immersive virtual reality technology in recent years is both complex and interesting. Because its development time is very short, there is still great room for exploration. Artists have traditionally been early adopters of new technologies focused on promoting innovative ideas, but now that role is more forward-looking than ever. Visual research is often cited as an aesthetic criterion by some artists and designers, but in fact, it should be explored how to use its theory to explore the communication of digital information communication technology or to develop more ideographic tools. It is not simply a feature or term used to identify the development of visual creativity over the past two decades; it is a discipline in itself worth exploring. This study aims to investigate what does visual experience means in general? Whether it is a medium independent of all fields of design and art or just another innovation in a long-term technological breakthrough, remains unclear. How does it affect our perception, understanding, and creation of visual communication? This article explores the issues listed above but also discusses the obstacles designers and artists face in the age of digital art and design.
Using Artificial Intelligence to Improve Human Performance: A Predictive Management Strategy
Authors: Palmas Fabrizio
Published: June 2022

In this paper, we introduce the novel concept of predictive management designed to support managers and their teams with achieving their long-term goals by adopting a new and sustainable AI and human-based approach that aims to identify a team's mood during short human-based control cycles. Predictive management helps managers, team leaders and employees to become more aware of the mood of a team and its members’ feelings by using AI, sentiment analysis and emotion detection. This allows managers to identify issues and solve them together with the team during short control cycles and thus maintain a productive workflow, instead of being overwhelmed by them and risking worsening the corporate performance.
Detection of inappropriate images on smartphones based on computer vision techniques
Authors: Imbaquingo Daisy , Ortega-Bustamante Macarthur , Jácome José , Saltos-Echeverria Tatyana , Vaca Roger
Published: June 2022

In recent years, the use of smartphones in children and adolescents has increased by a considerable number and, therefore, the dangers faced by this population are increasing. Due to this, it is important to develop a technological solution that allows combat this problem by making use of computer vision. Through a bibliographic review, it has been detected those children and adolescents frequently view violent and pornographic images, this allowed us to build a dataset of this type of images to develop an artificial intelligence model. It was successfully developed under the training and validation phases using a google supercomputer (Google Colab), while for the testing phase it was implemented on an android mobile device, using screenshots, images were extracted that the screen projected, and thus later the results were analyzed under statistics using R studio. The computational model detected, with a large percentage of true positives, images and videos of a pornographic and violent nature captured from the screen resolution of a smartphone while the user was using it normally.
Econometric Modeling for the Management and Decomposition of Financial Risk
Authors: Rubilar Torrealba Rolando , Chahuán Jiménez Karime , De La Fuente-Mella Hanns
Published: June 2022

This research presents a methodological analysis that will allow to actively manage the risk of financial assets, through an understandable study and mix of technical differences used by the financial literature. In this way, the research will allow the delivery of precise information on the risk-generating components of the assets studied. The methodology used corresponds to the wavelet decomposition method, combined with the VaR methodology, which as a whole proves to be an efficient way of controlling the financial risk of the investment portfolios used, thus allowing to identify the main risk generating components to which it is applied. investors and fund managers submit.
The Effect of Varying Levels of Automation during Initial Triage of Intrusion Detection
Authors: Cassenti Daniel , Roy Aayushi , Hawkins Thom , Thomson Robert
Published: June 2022

With unrestrained optimism regarding the possibilities of artificial intelligence (AI) exceeding its actualization, AI developers are under increasing pressure to integrate AI into complex human decision-making tasks without fully understanding the implications of this automation. To investigate how automation may influence human performance in a high workload environment, this study utilizes a triage scenario from intrusion detection using a simulated SNORT interface. Participants classify a series of time-sensitive alerts as real intrusions or false alarms with the assistance of varying levels of automation (LOA) from no automation to fully autonomous. Preliminary results showed that participants tend to prefer and have some performance benefits with intermediate levels of automation.
An analysis model for Machine Learning using Support Vector Machine for the prediction of Diabetic Retinopathy
Authors: Hurtado Remigio , Matute Janneth , Boni Juan
Published: June 2022

Diabetic Retinopathy is a public health disease worldwide, which shows that around one percent of the population suffers from this disease. Likewise, another one percent of patients in the population suffer from this disease, but it is not diagnosed. It is estimated that, within three years, millions of people will suffer from this disease. This will increase the percentage of vascular, ophthalmological and neurological complications, which will translate into premature deaths and deterioration in the quality of life of patients. That is why we face a great challenge, which is to predict and detect the signs of diabetic retinopathy at an early stage.For this reason, this paper presents a Machine Learning model focused on the optimization of a classification method using support vector machines for the early prediction of Diabetic Retinopathy. The optimization of the support vector machine consists of adjusting parameters such as: separation margin penalty between support vectors, separation kernel, among others. This method has been trained using an image dataset called Messidor. In this way, the extraction and preprocessing of the data is carried out to carry out a descriptive analysis and obtain the most relevant variables through supervised learning. In this sense, we can see that the most outstanding variables for the risk of diabetic retinopathy are type 1 diabetes and type 2 diabetes.For the evaluation of the proposed method we have used quality measures such as: MAE, MSE, RSME, but the most important are Accuracy, Precision, Recall and F1 for the optimization of classification problems. Therefore, to show the efficacy and effectiveness of the proposed method, we have used a public database, which has allowed us to accurately predict the signs of diabetic retinopathy. Our method has been compared with other relevant methods in classification problems, such as neural networks and genetic algorithms. The support vector machine has proven to be the best for its accuracy.In the state of the art, the works related to Diabetic Retinopathy are presented, as well as the outstanding works with respect to Machine Learning and especially the most outstanding works in Support Vector Machines. We have described the main parameters of the method and also the general process of the algorithm with the description of each step of the analysis model. We have included the values of hyper parameters experienced in the compared methods. In this way we present the best values of the parameters that have generated the best results.Finally, the most relevant results and the corresponding analysis are presented, where the results of the comparison made with the methods of Neural Networks, SVM and Genetic Algorithm will be evidenced. This study gives way to future research related to diabetic retinopathy with the aim of conjecturing the information and thus seeking a better solution.
Supradyadic Trust in Artificial Intelligence
Authors: Dorton Stephen
Published: June 2022

There is a considerable body of research on trust in Artificial Intelligence (AI). Trust has been viewed almost exclusively as a dyadic construct, where it is a function of various factors between the user and the agent, mediated by the context of the environment. A recent study has found several cases of supradyadic trust interactions, where a user’s trust in the AI is affected by how other people interact with the agent, above and beyond endorsements or reputation. An analysis of these surpradyadic interactions is presented, along with a discussion of practical considerations for AI developers, and an argument for more complex representations of trust in AI.
I Am What I Am – Roles for Artificial Intelligence from the Users’ Perspective
Authors: Philipsen Ralf , Brauner Philipp , Biermann Hannah , Ziefle Martina
Published: June 2022

With increasing digitization, intelligent software systems are taking over more tasks in everyday human life, both in private and professional contexts. So-called artificial intelligence (AI) ranges from subtle and often unnoticed improvements in daily life, optimizations in data evaluation, assistance systems with which the people interact directly, to perhaps artificial anthropomorphic entities in the future. How-ever, no etiquette yet exists for integrating AI into the human living environment, which has evolved over millennia for human interaction. This paper addresses what roles AI may take, what knowledge AI may have, and how this is influenced by user characteristics. The results show that roles with personal relationships, such as an AI as a friend or partner, are not preferred by users. The higher the confidence in an AI's handling of data, the more likely personal roles are seen as an option for the AI, while the preference for subordinate roles, such as an AI as a servant or a subject, depends on general technology acceptance and belief in a dangerous world. The role attribution is independent from the usage intention and the semantic perception of artificial intelligence, which differs only slightly, e.g., in terms of morality and controllability, from the perception of human intelligence.
Faulty Signal Restoration Algorithm in the Emergency Situation Using Deep Learning Methods
Authors: Choi Younhee , Kim Jonghyun
Published: June 2022

To operate nuclear power plants (NPPs) safely and efficiently, signals from sensors must be valid and accurate. Signals deliver the current situation and status of the system to the operator or systems that use them as inputs. Therefore, faulty signals may degrade the performance of both control systems and operators in the emergency situation, as learned from past accidents at NPPs. Moreover, With the increasing interest in autonomous and automatic controls, the integrity and reliability of input signals becomes important for the successful control. This study proposes an algorithm for the faulty signal restoration under emergency situations using deep convolutional generative adversarial networks (DCGAN) that generates a new data from random noise using two networks (i.e., generator and discriminator). To restore faulty signals, the algorithm receives a faulty signal as an input and generates a normal signal using a pre-trained normal signal distribution. This study also suggests optimization steps to improve the performance of the algorithm. The optimization consists of three steps; 1) selection of optimal inputs, 2) determine of the hyper-parameters for DCGAN. Then, the data for implementation and optimization are collected by using a Compact Nuclear Simulator (CNS) developed by the Korea Atomic Energy Research Institute (KAERI). To reflect the characteristics of actual signals in NPPs, Gaussian noise with a 5% standard deviation is also added to the data.
Toward Understanding the Use of Centralized Exchanges for Decentralized Cryptocurrency
Authors: Zhou Zhixuan , Shen Bohui
Published: June 2022

Cryptocurrency has been extensively studied as a decentralized financial technology built on blockchain. However, there is a lack of understanding of user experience with cryptocurrency exchanges, the main means for novice users to interact with cryptocurrency. We conduct a qualitative study to provide a panoramic view of user experience and security perception of exchanges. All 15 Chinese participants mainly use centralized exchanges (CEX) instead of decentralized exchanges (DEX) to trade decentralized cryptocurrency, which is paradoxical. A closer examination reveals that CEXes provide better usability and charge lower transaction fee than DEXes. Country-specific security perceptions are observed. Though DEXes provide better anonymity and privacy protection, and are free of governmental regulation, these are not necessary features for many participants. Based on the findings, we propose design implications to make cryptocurrency trading more decentralized.
Detecting Potential Depressed Users in Twitter Using a Fine-tuned DistilBERT Model
Authors: Antonio Adarlo Miguel , De Leon Marlene
Published: June 2022

With the spread of Major Depressive Disorder, otherwise known simply as depression, around the world, various efforts have been made to combat it and to potentially reach out to those suffering from it. Part of those efforts includes the use of technology, such as machine learning models, to screen a potential person for depression through various means, including social media narratives, such as tweets from Twitter. Hence, this study aims to evaluate how well a pre-trained DistilBERT, a transformer model for natural language processing that was fine-tuned on a set of tweets coming from depressed and non-depressed users, can detect potential users in Twitter as having depression. Two models were built using the same procedure of preprocessing, splitting, tokenizing, training, fine-tuning, and optimizing. Both the Base Model (trained on CLPsych 2015 Dataset) and the Mixed Model (trained on the CLPsych 2015 Dataset and a half of the dataset of scraped tweets) could detect potential users in Twitter for depression more than half of the time by demonstrating an Area under the Receiver Operating Curve (AUC) score of 65% and 63%, respectively, when evaluated using the test dataset. These models performed comparably in identifying potential depressed users in Twitter given that there was no significant difference in their AUC scores when subjected to a z-test at 95% confidence interval and 0.05 level of significance (p = 0.21). These results suggest DistilBERT, when fine-tuned, may be used to detect potential users in Twitter for depression.
The Songbird and the Robotic Self-Awakening
Authors: Yerdon Valarie
Published: June 2022

The songbird sings a beautiful melody when there is no ecological need, and the imagination and curiosity are fueled for investigation with biological models of cognitive mechanisms of animal communication. Many animal sensory signals remain a mystery to the logical reasoning of science. Through the evolutionary game theory in ecological cognitive science, predictions are made regarding the signal cost, circumstances, and the individual agent’s state, about which signals (continuous or discrete) should be valued in certain circumstances, but not the details of signal design nor any clue as to why the signals are so diverse in form. In this, investigations have the what, when/where, but not the why. This is reflective of where the debate on robotic consciousness sits. A robot can be programmed to decide to carry out an action in an “if-then” case and use logical algorithms to ensure the calculations can be made to match the possibilities of situations, but to act randomly as an expression of feelings, emotions, passions, or just for the sake of the act, is beyond a calculation. It is the “why” of an existent consciousness, in the “just because” reasoning for the feeling, thought, emotion, passion, or compassion that occurred for the act to come to fruition. A sentient act from emotion or passion may not be a programmable option, as it comes from the identity and free will of the conscious self. The question to be discussed in this paper is whether robots could someday possess a level of consciousness and sentience, to match that of a living human being. This paper will investigate the position that robots will reach a level of sentience and consciousness through the intelligent learning systems of AI. There is strong support for the position that there is a way for electronic networks to become more like human neural networks. The nano and biotechnology grow and the understanding of the human physiology will increase, throughout the smallest of details with neurons, networks, and into the compatibility of neural with electronic systems. AI systems have begun to find support and integration with biotechnology with nanotechnology (West, 2000).
Development of a platform based on artificial vision with SVM and KNN algorithms for the identification and classification of ceramic tiles
Authors: Pugo-Mendez Edisson , Serpa-Andrade Luis
Published: June 2022

In the ceramic tile manufacturing industry, the quality of production achieved depends to a large extent on the quality of the tile, which is very important for its classification and price. Currently, this process is performed by human operators, but many industries aim to improve performance and production through automation of this process. In this work, we present the development of a platform based on an artificial vision that allows the identification of defects in ceramic tiles, so that we can classify them according to their quality. The algorithms chosen to develop the platform are Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). In order to implement these algorithms, the images are preprocessed, the descriptors for defect detection are obtained, then the algorithms are used and the results obtained
Synchronization procedure for data collection in offline-online sessions
Authors: Viscaino - Quito Andres , Serpa-Andrade Luis
Published: June 2022

This article proposes a system for data replication and synchronization in mobile devices which is managed offline, allowing data collection in remote locations or deprived of internet connection. In this process there were shortcomings in the convergence and stability of the data, for which a synchronization procedure (web services) is used to assist it. As a result it was obtained that the synchronization between a database hosted in the cloud, a database hosted locally on the mobile device, the compatibility between different programming languages such as Django of Python as server, the deployment of Web Services and C# as client in the consumption of synchronization services is a success, carrying out a synchronization where the integrity of the data is not lost, enabling the connection of the devices in offline mode, performing the corresponding activities, to the time of having an internet connection to upload the data and keep them synchronized.
Development of a virtual assistant chatbot based on Artificial Intelligence to control and supervise a process of 4 tanks which are interconnected
Authors: Gonzalez-Gonzalez Sandro , Serpa-Andrade Luis
Published: June 2022

This article presents the gathering of works related to the usage of virtual assistants into the 4.0 industry in order to stablish the parameters and essential characteristics to define the creation of a ‘chatbot’ virtual assistant. This device should be applicable to a process of 4 tanks which are interconnected with a robust multivariable PID control with the aim of controlling and supervising this process using a mobile messaging application from a smartphone by sending key words in text messages which will be interpreted by the chatbot and this will be capable of acting depending on the message it receives; it can be either a consultation of the status of the process and the tanks which will be answered with a text message with the required information, or a command which will make it work starting or stopping the process. This system is proposed as a solution in the case of long-distance supervision and control during different processes. With this, an option to optimize the execution of actions such as security, speed, reliability of data, and resource maximization can be implemented, which leads to a better general performance of an industry
University footballers’ preference for smart trousers
Authors: Huang Ying-Chia , Yang Wei-Hao
Published: June 2022

This paper describes the development of smart trousers for self-training purposes during Covid-19, based on the requirements of two footballers from a Taiwanese University team. The two professional athletes aimed to be able to adapt their play as a result of self-training data that would be received visually from the smart trousers. The researcher collected feedback via two interviews, undertaken during the design preparation and garment fitting phases to ensure the garment design would be ready for production.The semi-structured interview technique and Kawakita Jiro method were adopted by the researcher. Firstly, the study investigated the footballer’s preferred smart trouser design in interview. Secondly, smart trousers were developed as initial garment prototypes, both virtually and physically. Thirdly, after the two athletes had been fitted with and worn the trousers for self-training purposes, the researcher issued a questionnaire and conducted a second interview to collect wearer feedback. The results showed that using both emulator data for a virtual prototype and physical garment sample in the step of prototype development has high degree of accuracy to assist pattern-making, fitting and sizing. The users’ feedback focused on three issues: pocket size, comfort of hem stitching, and label position.
Automation of the Collection and Processing of Physiological Functions from Wearable Sensors
Authors: Fodor Kristian , Balogh Zoltán
Published: June 2022

The current period is evidence of the massive replacement of human strength by the results of the mental cognitive work of researchers. To cope with this situation, it is necessary to examine the impact of these technologies on the quality of life in the work and home environment when introducing new technologies. The main purpose of this work will be the collection of physiological functions such as heart rate in real-time from wearable sensors by creating an application that automatically collects sensor data. The work also proposes a mobile application that will be able to read the sensor data from the database and display it. The data from the sensors will be later used in a complex system and its proposal will also be described in this paper. Such a system could be used in many industries, like the automotive industry, industrial factories, smart homes and even education.
Affective computing for stress, anxiety and cybersickness detection in virtual reality
Authors: Jose De Jesus Junior Belmir , Henrique Falk Tiago , Dandiwal Yuvika , Alemu Helen
Published: June 2022

The prevalence of stress and anxiety has increased dramatically in recent decades, especially with the global COVID-19 pandemic. In parallel, effective ways of objectively assessing and quantifying these conditions have continued to be explored. Affective computing is one such technique that has gained popularity recently, using physiological signals to interpret, and infer human emotion. Additionally, virtual reality (VR) is a rapidly developing tool with promising advancements in the health sciences. Given the emergence of new unobtrusive wearables and biosensors-instrumented VR headsets, the combined use of VR and affective computing has enabled the development of new immersive applications to objectively evaluate stress and anxiety. In this paper, we examine various affective computing methods that have been combined with VR with the goal of quantitatively measuring stress and anxiety levels. Additionally, we explore how affective computing has been used in the assessment of cybersickness. In particular, we surveyed current VR studies and summarized the most common physiological measurements used to characterize stress, anxiety, and cybersickness. Methods monitoring heart rate, skin conductance, muscle movement, and brain activity are described. We highlight the current challenges and propose opportunities for future research directions.Keywords: Stress, Anxiety, Affective Computing, Physiological Measurements, Virtual Reality (VR), Cybersickness
Assessing the Effect of a Powered Ankle Exoskeleton on Human Agility with Inertial Measurement Units
Authors: King Katelyn , Gonzalez Sarah , Stirling Leia
Published: June 2022

Human agility describes the capacity to quickly adjust body movements in response to the environment. This study quantifies agility through performance on 0º, 45º, 90º, and 180º turns on an outdoor agility course. Participants (n=17) walked the course while wearing an ankle exoskeleton in powered and unpowered states, and their own shoes before and after the exoskeleton trials. Agility was quantified using Inertial Measurement Units placed on the feet. All metrics varied significantly with turn type and exhibited larger effect sizes than with changes in condition. Stride duration moderately increased in both exoskeleton conditions on 0º, 45º, and 90º turns. On 180º turns, the unpowered exoskeleton moderately decreased radial acceleration while the powered exoskeleton moderately increased speed and tangential acceleration. The results suggest that the evaluated ankle exoskeleton would be unobtrusive for similar healthy young users in their daily environments. The methods propose a framework for further study of exoskeletons and agility in a broader set of users with additional exoskeleton systems.
Comparative evaluation of cervical exoskeletons using IMUs
Authors: Giovanelli Yonnel , Puel Fréderic , Mahdi Camélia , Gouelle Arnaud , Bertucci William
Published: June 2022

Musculoskeletal disorders and pain in the neck and shoulders are commonly reported in workers whose activities imply overhead tasks. Repetitive passive head support or traumatic movements of the neck can cause damage to the ligaments and tendons of this region, with mild to severe long-term consequences. Exoskeletons are one of the solutions to help workers and their evaluation requires scientific methods and protocols to prove their effectiveness and make recommendations (Crea et al., 2021) (De Bock et al., 2022). Cervical exoskeletons could therefore be a valuable ergonomic solution to reduce stress on the neck and shoulders. However, while the growth of exoskeleton technology has led to multiple systems available on the market, it is still difficult to objectively determine which type or model of neck exoskeleton is the best adapted for overhead work and if the user’s perception matches with biomechanical outcomes.In this randomized crossover design study, 8 participants (3 women) performed dynamic and static extensions of the head in sitting position without trunk support for a period of 3 minutes (then 3 minutes of rest) while wearing three different head/neck exoskeletons in comparison with a situation without an exoskeleton. This allowed us to evaluate comfort, utility, usability, safety and impact (AFNOR, 2017) (Giovanelli & Touchard, 2018). A solution, based on synchronized merger of wireless inertial sensors, EMG signals, Polar OH1+ optical heart rate sensor (Hettiarachchi et al., 2019) and videos of the task (Motion CAPTIV, TEA, France) (Peeters et al., 2019) was used to examine joint angles of the head and spine movements, the bioelectrical activity of the sternocleidomastoid muscle and heart rate. Further these biomechanical and physiological outcomes, the perception of intensity was assessed by the Borg scale (Meyer, 2014) : CR10 Scale for the cervical and lumbar spine as well by the Rated Perceived Exertion (RPE) Scale for the global level of activity.The synthesis of this comparative analysis was carried out and compiled in the form of a conceptual basis from the C-K theory (Hatchuel & Weil, 2003) from the analysis of the design logic of exoskeletons.The results of this comparative analysis showed differences in terms of comfort, utility, usability, safety depending on the design logic of the solutions tested, but also depending on the morphology of the testers.
Toward a Consequential Validity Perspective for Selecting Participant Groups in Testing and Evaluation Studies for Complex Systems
Authors: Lau Nathan , Boring Ronald
Published: June 2022

Testing and evaluation of technology design for complex systems cannot readily attain conclusive results. This is because skilled professionals are often not available for testing while non-professionals may not be capable of operating the actual systems or high-fidelity simulators. Thus, practitioners and applied scientists can be challenged with decisions on selecting participant groups, which can severely constrain choices in the experimental tasks. This article presents the perspective of consequential validity, highlighting that general validity or rules to participant selection probably do not exist. Most importantly, the validity of a testing method or an empirical finding critically rests on the decisions of interest that must take into account nuances or idiosyncrasies of specific situations and desired outcomes. This perspective stands in contrast to how the literature predominantly portrays validity of testing methods or empirical findings as universal rather than focusing on outcomes within the confines of the study methods. The perspective of consequential validity calls for studies on how classical metrics of reliability and validity could manifest in consequence of specific decisions informed by empirical testing.
Impact of Camera Perspective and Image Throughput on Human Trust of a Quadrupedal Robot Scout
Authors: Brewer Ralph , Guyton Zachary , Long Tyler , Vantreese Angela , Russell Mason , Kessens Chad , Rovira Ericka
Published: June 2022

The objective of this study is to understand user perceptions of robot behaviors. Specifically, we are interested in the possible effects of providing the user with different camera perspectives and with regular snapshots versus a continuous camera feed in the context of a small-unit military operation. The study will employ a mixed 2 (camera perspective: 1st person vs over the shoulder 3rd person) x 2 (camera feed: snapshots vs continuous) factorial design, with participants viewing a robot performing military tasks in both rural and urban operational settings. After viewing the robot’s performance, participants will answer performance questions based on the context of the military mission, as well as questionnaires that measure trust in the autonomous system. Dependent variables include performance outcomes from tactical performance questions and subjective results of the trust questionnaires. Data from participants will be analyzed with a 2x2 between subjects ANOVA. We anticipate that the findings will suggest that a third person perspective and continuous camera feed will result in the highest trust and best performance outcomes.
Evaluation of Human-Autonomy Team Trust for Weaponized Robotic Combat Vehicles
Authors: Brewer Ralph , Baker Anthony , Neubauer Catherine , Krausman Andrea , Forster Daniel , Scharine Angelique , Berg Samantha , Davis Kristi , Schaefer Kristin
Published: June 2022

Phase I of the Soldier Operational Experiment was held at Fort Carson, Colorado in 2020, to assess the current capability of a manned vehicle and unmanned weaponized vehicle collaborative team capabilities during live fire gunnery operations and situational training exercises. Here we discuss the performance of the crews during these exercises, and the implementation of team trust metrics to evaluate crew dynamics in these human-autonomy lethality teams. The gunnery exercise performance scores demonstrated that teams were often able to achieve qualifying scores on the relevant gunnery standards. However, subjective measures showed relatively low to moderate levels of trust across crew members. Through further analysis we found that Soldiers opted to perform many tasks manually and were slow to adapt to and use the technologies, even with substantial training on the systems. One possible reason for this response to the technology was due to the technology being early in a development cycle and completely new to the users. Linguistic analyses were conducted on the crew communication in order to provide a more fine-grained analysis of the team dynamic. Results indicated that higher performing crews used more formal communication with words associated with perception (e.g., seeing, hearing, etc.). In line with previous field studies through the Wingman Joint Capabilities Technology Demonstration, this study further validated a multi-method approach to understanding performance, trust, and cohesion in human-autonomy teams.
Toward Understanding Development of Team Resilience during Stress Exposure Training
Authors: Johnston Joan , Patton Debra
Published: June 2022

The demand for understanding stress resilience in Soldiers has continued unabated for decades. In this paper we applied the Bowers et al. (2017) team resilience model to test hypotheses about whether U.S. Army squads participating in a three-day Stress Exposure Training would respond with resilient stress reactions, positive team and learning climate attitudes, and learning outcomes. Anxiety, depression, hostility, sensation seeking, and positive affect showed mild to strong indications of resilient “bounce back” after scenario based training, and positive team attitudes emerged early in training and remained high. Soldiers that reported higher team cohesion and learning climate scored higher on a post-training knowledge test. These findings indicate that individual and team resilience are emergent states and multiple measures of individual and team attitudes and behaviors are critical for diagnosing a team development over time. Recommendations for future research are discussed.
Interdisciplinary Learning - Tomorrow’s Executive as Creative Problem Solvers
Authors: Sethi Simrun
Published: June 2022

Incorporating creative processes in the business curriculum can provide a framework for dealing with uncertainty in new product development. The paper explores the role and significance of user-focused aspects of the human-centered process in creating successful products and services. Demystifying the creative process can be challenging, as many students have trouble when engaging in 'need-finding' and problem-solving for the first time. Coping with uncertainty seems to be a vital factor in a student's ability to generate new products. Bringing creative tools and approaches into the classroom and sharing how to solve problems through observation, experiment, analysis, reflection establishes the place of the creative process, as a bridge, as a guiding principle – as a method for generating new products and new products business strategies.Keywords: Creative Process, Embracing Ambiguity, Interdisciplinary Learning, User-Focused.
Entrepreneurial education: The case of South American countries
Authors: Morales Urrutia Ximena , Morales Urrutia Diana , Avila Herrera Stalyn
Published: June 2022

The interest in the development of an entrepreneurial culture has acquired special importance, both in the academic and social spheres, since it is considered as the engine of growth and economic development of countries, particularly, those that are in the process of development. The objective of the article is to analyze the level of education and its role in entrepreneurial activity from a comparative point of view in South American countries. From a theoretical point of view, a review of the different contributions regarding education, academic and business training in the generation of entrepreneurship is carried out. Methodologically, the research is approached from a descriptive and correlational perspective, based on the data obtained from the Global Entrepreneurship Monitor, for the case of 5 South American countries during the 2010 – 2016 period. The results suggest that education of entrepreneurs should be oriented to the development of skills to face the challenges of today's society.
An evaluation of material customized modular protective helmets
Authors: Shah Parth , Yarlagadda Tejeswar , Luximon Ameersing , An Dong , Luximon Yan
Published: June 2022

Helmets are often recommended as primary safety equipment for protecting the head. Several studies have been conducted to test the effectiveness of different impact-absorbing liner materials and their response to high impact loading. The standard helmets have an impact-absorbing liner of a single material, which may not be the best design approach. Hence, the possibility of modular helmet liner design with two different materials has been explored in this study. The chapter presents a preliminary Finite element analysis (FEA) study to evaluate the performance of hard foam, soft foam and a combination of both the foams as impact-absorbing liners for helmets. The results suggest that the hard foam liner showed better performance for high impact loads, while the soft foam liner was more effective for low impact loads. The results also indicated that the modular design-based helmet liners developed using both the hard and soft foam performed better than single foam liners.
Introducing PATI: The Pareto Analysis for Technology Insertion - a human-centered methodology to identify and prioritize innovation in complex systems
Authors: Bruni Sylvain
Published: June 2022

The commoditization of artificial intelligence (AI), machine learning (ML) algorithms, and automated agents (AA) affords innovators and entrepreneurs myriad opportunities for the creative pursuit of new business endeavors. This abundance is, however, overwhelming, and no adequate methodology currently exists to identify and select what technologies to insert into existing systems or processes to create value. In particular, there is a lack of techniques that drive such decision-making towards desireability and utilization, two critical factors that entrepreneurs and innovators must diligently consider in their efforts. To bridge this gap, we propose the simple, human-centered “pareto analysis for technology insertion,” or PATI, as an approach to identify and prioritize the insertion of AI, ML, or AA into complex systems. Through the application of PATI to the use case of aircraft maintenance, we demonstrate how this straightforward methodology yields artifacts that are both useful and comprehensive for most stakeholders in the innovation entrepreneurship ecosystem, thereby accelerating and making more resilient their creative pursuits.