Public Launch Research Articles

Active remote sensing data and dispersal processes improve predictions for an invasive aquatic plant during a climatic extreme in Great Lakes coastal wetlands

Invasive aquatic plants pose a significant threat to coastal wetlands. Predicting suitable habitat for invasive aquatic plants in uninvaded yet vulnerable wetlands remains a critical task to prevent further harm to these ecosystems. The integration of remote sensing and geospatial data into species distribution models (SDMs) can help predict where new invasions are likely to occur by generating spatial outputs of habitat suitability. The objective of this study was to assess the efficacy of utilizing active remote sensing datasets (synthetic aperture radar (SAR) and light detection and ranging (LiDAR) with multispectral imagery and other geospatial data in predicting the potential distribution of an invasive aquatic plant based on its biophysical habitat requirements and dispersal dynamics. We also considered a climatic extreme (lake water levels) during the study period to investigate how these predictions may change between years. We compiled a time series of 1628 field records on the occurrence of Hydrocharis morsus-ranae (European frogbit; EFB) with nine remote sensing and geospatial layers as predictors to train and assess the predictive capacity of random forest models to generate habitat suitability in Great Lakes coastal wetlands in northern Michigan, USA. We found that SAR and LiDAR data were useful as proxies for key biophysical characteristics of EFB habitat (emergent vegetation and water depth), and that a vegetation index calculated from spectral imagery was one of the most important predictors of EFB occurrence. Our SDM using all predictors yielded the highest mean overall accuracy of 88.3% and a true skill statistic of 75.7%. Two of the most important predictors of EFB occurrence were dispersal-related: 1) distance to the nearest known EFB population (m), and 2) distance to nearest public boat launch (m). The area of highly suitable habitat (pixels assigned ≥0.8 probability) was 74% larger during a climatically extreme high water-level year compared to an average year. Our findings demonstrate that active remote sensing can be integrated into SDM workflows as proxies for important drivers of invasive species expansion that are difficult to measure in other ways. Moreover, the importance of a proxy variable for endogenous dispersal (distance to nearest known population) in these SDMs indicates that EFB is currently spreading, and thereby less influenced by within-site dynamics such as interspecific competition. Lastly, we found that extreme climatic conditions can dramatically change this species’ niche, and therefore we recommend that future studies include dynamic climate conditions in SDMs to more accurately forecast the spread during early invasion stages.

AI Hesitancy and Acceptability-Perceptions of AI Chatbots for Chronic Health Management and Long COVID Support: Survey Study.

Artificial intelligence (AI) chatbots have the potential to assist individuals with chronic health conditions by providing tailored information, monitoring symptoms, and offering mental health support. Despite their potential benefits, research on public attitudes toward health care chatbots is still limited. To effectively support individuals with long-term health conditions like long COVID (or post-COVID-19 condition), it is crucial to understand their perspectives and preferences regarding the use of AI chatbots. This study has two main objectives: (1) provide insights into AI chatbot acceptance among people with chronic health conditions, particularly adults older than 55 years and (2) explore the perceptions of using AI chatbots for health self-management and long COVID support. A web-based survey study was conducted between January and March 2023, specifically targeting individuals with diabetes and other chronic conditions. This particular population was chosen due to their potential awareness and ability to self-manage their condition. The survey aimed to capture data at multiple intervals, taking into consideration the public launch of ChatGPT, which could have potentially impacted public opinions during the project timeline. The survey received 1310 clicks and garnered 900 responses, resulting in a total of 888 usable data points. Although past experience with chatbots (P<.001, 95% CI .110-.302) and online information seeking (P<.001, 95% CI .039-.084) are strong indicators of respondents' future adoption of health chatbots, they are in general skeptical or unsure about the use of AI chatbots for health care purposes. Less than one-third of the respondents (n=203, 30.1%) indicated that they were likely to use a health chatbot in the next 12 months if available. Most were uncertain about a chatbot's capability to provide accurate medical advice. However, people seemed more receptive to using voice-based chatbots for mental well-being, health data collection, and analysis. Half of the respondents with long COVID showed interest in using emotionally intelligent chatbots. AI hesitancy is not uniform across all health domains and user groups. Despite persistent AI hesitancy, there are promising opportunities for chatbots to offer support for chronic conditions in areas of lifestyle enhancement and mental well-being, potentially through voice-based user interfaces.

Perceptions of artificial intelligence system's aptitude to judge morality and competence amidst the rise of Chatbots

This paper examines how humans judge the capabilities of artificial intelligence (AI) to evaluate human attributes, specifically focusing on two key dimensions of human social evaluation: morality and competence. Furthermore, it investigates the impact of exposure to advanced Large Language Models on these perceptions. In three studies (combined N = 200), we tested the hypothesis that people will find it less plausible that AI is capable of judging the morality conveyed by a behavior compared to judging its competence. Participants estimated the plausibility of AI origin for a set of written impressions of positive and negative behaviors related to morality and competence. Studies 1 and 3 supported our hypothesis that people would be more inclined to attribute AI origin to competence-related impressions compared to morality-related ones. In Study 2, we found this effect only for impressions of positive behaviors. Additional exploratory analyses clarified that the differentiation between the AI origin of competence and morality judgments persisted throughout the first half year after the public launch of popular AI chatbot (i.e., ChatGPT) and could not be explained by participants' general attitudes toward AI, or the actual source of the impressions (i.e., AI or human). These findings suggest an enduring belief that AI is less adept at assessing the morality compared to the competence of human behavior, even as AI capabilities continued to advance.

Forensic analysis of OpenAI's ChatGPT mobile application

Since its public launch, OpenAI's ChatGPT has achieved significant success, attracting millions of users within the first few months of its release. Although numerous similar applications have emerged, none have yet matched the success of OpenAI's ChatGPT. Last year, OpenAI released the ChatGPT mobile app. This application serves a broad range of uses, some of which may be malicious and, unfortunately, it has not yet been parsed by either commercial or open-source tools. Nevertheless, the data stored by this application, such as JSON files that store a user's conversations with ChatGPT, can be instrumental in attributing user actions, discerning perpetrators' knowledge and motivations, and resolving practical investigations. In this paper, OpenAI's ChatGPT mobile application is examined on both Android and iOS operating systems, focusing on potential evidentiary data within. The cloud-native data associated with the app, which can be retrieved through user data export requests are also investigated. The primary objective of this study is to discover artifacts that investigators can use in real-world cases involving this mobile app. Additionally, the authors have contributed to FOSS to support professionals in this field.

Development of a Machine Learning (ML)-Based Computational Model to Estimate the Engineering Properties of Portland Cement Concrete (PCC)

Portland cement concrete (PCC) is the construction material most used worldwide. Hence, its proper characterization is fundamental for the daily-basis engineering practice. Nonetheless, the experimental measurements of the PCC’s engineering properties (i.e., Poisson’s Ratio -v-, Elastic Modulus -E-, Compressive Strength -ComS-, and Tensile Strength -TenS-) consume considerable amounts of time and financial resources. Therefore, the development of high-precision indirect methods is fundamental. Accordingly, this research proposes a computational model based on deep neural networks (DNNs) to simultaneously predict the v, E, ComS, and TenS. For this purpose, the Long-Term Pavement Performance database was employed as the data source. In this regard, the mix design parameters of the PCC are adopted as input variables. The performance of the DNN model was evaluated with 1:1 lines, goodness-of-fit parameters, Shapley additive explanations assessments, and running time analysis. The results demonstrated that the proposed DNN model exhibited an exactitude higher than 99.8%, with forecasting errors close to zero (0). Consequently, the machine learning-based computational model designed in this investigation is a helpful tool for estimating the PCC’s engineering properties when laboratory tests are not attainable. Thus, the main novelty of this study is creating a robust model to determine the v, E, ComS, and TenS by solely considering the mix design parameters. Likewise, the central contribution to the state-of-the-art achieved by the present research effort is the public launch of the developed computational tool through an open-access GitHub repository, which can be utilized by engineers, designers, agencies, and other stakeholders.

Predictors of adherence to a publicly available self-guided digital mental health intervention

ABSTRACT Low adherence to self-guided digital mental health interventions (DMHIs) have raised concerns about their real-world effectiveness. Naturalistic data from self-guided DMHIs are often not available, hindering our ability to assess adherence among real-world users. This study aimed to analyze 3 years of user data from the public launch of an empirically supported 12-session self-guided DMHI, to assess overall program adherence rates and explore predictors of adherence. Data from 984 registered users were analyzed. Results showed that only 14.8% of users completed all 12 modules and 68.6% completed less than half of the modules. Users who were younger, had milder depression, had never seen a mental health provider, and who rejected signing-up for weekly program emails completed significantly more modules. Results add to concerns about the generalizability of controlled research on DMHIs due to lower adherence outside of research trials. This study highlights the potential of user data in identifying key factors that may be related to adherence. By examining adherence patterns among different sub-sets of users, we can pinpoint and focus on individuals who may adhere and benefit more from self-guided programs. Findings could also have implications for guiding intervention personalization for individuals who struggle to complete DMHIs.

Who is using ChatGPT and why?: extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model

Introduction. Since its public launch, ChatGPT has gained the world's attention, demonstrating the immense potential of artificial intelligence (AI). Method. To explore factors influencing the adoption of ChatGPT, we ran structural equation modelling to test the unified theory of acceptance and use of technology model while incorporating relative risk (vs. benefit) perception and emotional factors into its original form to gain a better understanding of the process. Analysis. This study utilized partial least squares–structural equation modelling (PLS-SEM) with SmartPLS4. Results. The findings revealed that in addition to individuals' technology-specific perceptions (i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions), relative risk perception and emotional factors play significant roles in predicting favourable attitude and behaviour intentions towards ChatGPT. Conclusions. Our extended model fits the data well, suggesting that it is not merely a matter of convenience but also of people's reservations, expectations, and emotions toward technology, which significantly influence their willingness to adopt ChatGPT.

How ChatGPT Will Shape the Teaching Learning Landscape in Future?

ChatGPT quickly garnered over a million users within a week of its public launch on November 30, 2022, astounded the globe with its remarkable powers in managing exceedingly complex activities. The notable proficiency of ChatGPT in executing complex educational assignments has evoked a range of sentiments among educators, signaling a potential transformation in established educational practices. This exploratory inquiry consolidates recent scholarly works to discuss the potential benefits and downsides of ChatGPT in terms of increasing teaching and learning. Among the benefits are the facilitation of personalized and interactive learning and the generation of prompts for formative assessments, providing continuous feedback for instructional improvement. Conversely, the study underscores inherent limitations in ChatGPT, including the potential for generating inaccurate information, biases stemming from data training that might perpetuate existing prejudices, and concerns related to privacy. The research concludes with recommendations for optimizing the use of ChatGPT in educational contexts. Collaboration among policymakers, researchers, educators, and technology experts is encouraged to initiate dialogues on the safe and constructive utilization of these evolving generative AI tools to enhance education and support student learning.&#x0D; &#x0D; Received: 1 January 2024 / Accepted: 1 March 2024 / Published: 5 March 2024

Lehigh Libraries digital repositories migration: A case study

In 2020, the Lehigh University Libraries migrated their CONTENTdm and Digital Commons repositories to the Islandora 7 platform. This migration was the culmination of a three-year process that included an assessment of the digital repository landscape, the selection of a software platform, the choice of a vendor for support, the migration of content and metadata and a public launch. Due to concerns about platform stability, the Libraries embarked on another migration to the Islandora 2 platform in 2023, involving a similar process to the earlier migration. This case study addresses the challenges faced and the lessons learned from conducting multiple migrations over a short period of time.

Designing a Long-Structure NFT Generative Art Project: Catharsis As a Case Study

Abstract For decades considered a fringe artistic discipline, generative art has in recent years piqued an unusual amount of interest due to its integration with blockchain technology, which has given rise to new ways of designing, generating, acquiring, and collecting generative artworks. This article presents, by way of a chronicle, the author’s process in designing and creating a long-form generative project entitled Catharsis, from its conceptual genesis through the challenges and innovations it presented upon its public launch in September 2022, with a view to offering a guide that may inspire future generative artists or scholars.

ChatGPT Use Among Pediatric Health Care Providers: Cross-Sectional Survey Study.

The public launch of OpenAI's ChatGPT platform generated immediate interest in the use of large language models (LLMs). Health care institutions are now grappling with establishing policies and guidelines for the use of these technologies, yet little is known about how health care providers view LLMs in medical settings. Moreover, there are no studies assessing how pediatric providers are adopting these readily accessible tools. The aim of this study was to determine how pediatric providers are currently using LLMs in their work as well as their interest in using a Health Insurance Portability and Accountability Act (HIPAA)-compliant version of ChatGPT in the future. A survey instrument consisting of structured and unstructured questions was iteratively developed by a team of informaticians from various pediatric specialties.The survey was sent via Research Electronic Data Capture (REDCap) to all Boston Children's Hospital pediatric providers.Participation was voluntary and uncompensated, and all survey responses were anonymous. Surveys were completed by 390 pediatric providers. Approximately 50% (197/390) of respondents had used an LLM; of these, almost 75% (142/197) were already using an LLM for nonclinical work and 27% (52/195) for clinical work. Providers detailed the various ways they are currently using an LLM in their clinical and nonclinical work. Only 29% (n=105) of 362 respondents indicated that ChatGPT should be used for patient care in its present state; however, 73.8% (273/368) reported they would use a HIPAA-compliant version of ChatGPT if one were available. Providers' proposed future uses of LLMs in health care are described. Despite significant concerns and barriers to LLM use in health care, pediatric providers are already using LLMs at work. This study will give policy makers needed information about how providers are using LLMs clinically.

Incorporating Artificial Intelligence Into Athletic Training Education: Developing Case-Based Scenarios Using ChatGPT

Context ChatGPT is an AI-based large language model platform capable of mimicking human language patterns by responding to user queries. Although concerns over AI-generated content exist in health care and higher education, the ChatGPT platform can assist athletic training educators in creating academic content to alleviate aspects of their academic workload and responsibilities. Objective To provide a brief historical overview of ChatGPT, accepted positives and negatives, and examples of how athletic training educators can use ChatGPT for case-based scenario contention creation. Background The initial development of ChatGPT began in 2018, with its public launch in November 2022. ChatGPT uses machine and in-context learning to replicate human language expression. Since its public launch, students, researchers, educators, clinicians, governments, and companies have sought to determine how to incorporate ChatGPT into their workflow operations. Data Synthesis Athletic training educators can incorporate ChatGPT into their academic content creation methodology. With the capability of ChatGPT, athletic training educators have the potential to facilitate athletic training student learning more efficiently. Results ChatGPT can ease the academic workload of athletic training educators while being a beneficial clinical tool that athletic training students may use in their future workplace settings. Recommendation(s) Athletic training educators should consider using ChatGPT or a similar AI-based large language model platform when developing education content and simulation scenarios. Conclusions ChatGPT can lessen athletic training educator workload and potentially facilitate athletic training student learning.

How good is the advice from ChatGPT for building science? Comparison of four scenarios

This paper resulted from several questions discussed between its human authors shortly after the public launch of OpenAI’s ChatGPT:Can a language model, trained on an unimaginably vast database, be able to resolve fundamental data inference and data-driven forecasting problems which have been ’typical’ research fare in the building science domain? Is it possible that research problems which ’typically’ require user-intensive tools, such as building performance simulation and problem-specific machine learning models, can today be addressed by ChatGPT in a manner of seconds? If so, what does this mean for the future of building science, let alone the writing of novel research contributions in academia?The entirety of this paper was produced with significant use of ChatGPT. Four arbitrarily-selected case studies were extracted from recent peer-reviewed journals and reputable sources. ChatGPT was tasked with attempting to infer the same results as the publications using only each case study’s input data. Not only were ChatGPT’s results found to be relatively credible, ChatGPT was able to communicate its results instantly and in an academic language. From start to finish, the entirety of this paper, from initial brainstorming to final editing, was completed in no more than 8 human-hours by the study’s (human) authors. The content of this paper is original and has not been published previously.

ChatGPT and mental healthcare: balancing benefits with risks of harms

Against the global need for increased access to mental services, health organisations are looking to technological advances to improve the delivery of care and lower costs. Since November 2022, with...

Alternative data and trade credit financing: Evidence from third-party online sales disclosure

This study examines the relationship between the introduction of alternative data and trade credit financing. Using the event of the public launch of firm online sales data in a well-known financial database in China, we show that firms obtain more trade credit from suppliers with the public disclosure of such alternative data. We extend the literature by shedding light on the important role of alternative data in capital markets to assess the operational transparency and credit risk of firms.

Generative artificial intelligence and large language models for digital banking: First outlook and perspectives

After several years of steady progress, the Generative artificial intelligence (AI) and large language models (LLMs, their applications to text) fields have accelerated tremendously since the end of 2022 and the public launch of ChatGPT. This is due to record-breaking model sizes and performances in the last couple of months, triggering unprecedented adoption curves from end users across the world. Even though regulators reacted fast, sharing their first recommendations, auditing emerging players, amending their AI regulation drafts or launching dedicated working groups, these efforts will require several months or years to come to fruition. There are multiple reasons for this. LLMs are complex technological objects made of gigantic foundational models trained on enormous quantities of texts, coupled with dedicated interfaces and action agents. They present a huge potential to perform high varieties of tasks with very high quality but also important risks in terms of costs, content accuracy, transparency, data privacy, security and ethics. Finally, the current ecosystem of stakeholders is very dynamic but also immature. In this uncertain context, the digital banking industry has been reacting ambivalently, with major players banning employee access to ChatGPT and publicly communicating on new LLM initiatives at the same time. This can be explained by the huge potential offered by these technologies to transform their business, coupled with many open questions in terms of technological set-up, usage, compliance and profitability. As these technologies seem to be too transformative for the industry incumbents to just wait and see, they should start creating the right conditions to learn how to use them, by identifying relevant use cases, choosing adapted and simple solutions, designing relevant user experiences, building the right teams, environment, data sets and operating model, and actively engaging in regulatory conversations.

AI-Fused Automation [From the Editor’s Desk

ChatGPT is everywhere. From the weekend party table to the weekday university seminars, people have been talking about ChatGPT. Since its public launch at the end of last November, it reached 100 million users in January and has become the fastest-growing Internet service ever. GPT is short for generative pretrained transformer, and the chatbot is the large language model pretrained with text databases from the Internet and human feedback. The hype around ChatGPT may be well justified, as the tech innovation could transform some fields, such as how humans interact with machines. Will it be long before truly intelligent machines are developed to transform industrial sectors such as manufacturing?

Finding a safe zone in the highlands: Exploring evaluator competencies in the world of AI

AbstractSince the public launch of ChatGPT in November 2022, disciplines across the globe have grappled with questions about how emerging artificial intelligence will impact their fields. In this article I explore a set of foundational concepts in artificial intelligence (AI), then apply them to the field of evaluation broadly, and the American Evaluation Association's evaluator competencies more specifically. Given recent developments in narrow AI, I then explore two potential frameworks for considering which evaluation competencies are most likely to be impacted—and potentially replaced—by emerging AI tools. Building on Moravec's Landscape of Human Competencies and Lee's Risk of Replacement Matrix I create an exploratory Landscape of Evaluator Competencies and an Evaluation‐Specific Risk of Replacement Matrix to help conceptualize which evaluator competencies may be more likely to contribute to long‐term sustainability for the field. Overall, I argue that the interpersonal, and contextually‐responsive aspects of evaluation work—in contrast to the more technical, program management, or methodological aspects of the field—may be the competencies least likely to be impacted or replaced by AI. As such, these may be the competencies we continue to emphasize, both in the day‐to‐day aspects of our operations, and in the training of new and emerging evaluators. This article is intended to be a starting point for discussions that continue throughout the remainder of this issue.

Does alternative data reduce stock price crash risk? Evidence from third-party online sales disclosure in China

This study empirically investigates the effect of releasing alternative data on firm-specific price crash risk. Using the public launch of a firm's third-party online sales data in a well-known Chinese financial database as an exogenous shock, we find that stock price crash risk significantly decreases with the disclosure of third-party online sales data. The results are robust to a series of endogeneity corrections and robustness checks. We also find that the reduction of stock price crash risk is due to the decrease in managers' bad news withholdings and the increase in the accuracy of market expectations. In addition, the negative association between third-party online sales disclosure and crash risk is more pronounced for firms with weaker external governance, higher earnings volatility, greater likelihood of sales manipulation, and lower book-to-market ratio. Our findings yield important implications for a comprehensive understanding of the information disclosure effect of online sales data in the capital market and the mechanisms to reduce stock price crash risk.

Exploring the Capabilities and Limitations of ChatGPT and Alternative Big Language Models

ChatGPT, an artificial intelligence (AI)-powered chatbot developed by OpenAI, has gained immense popularity since its public launch in November 2022. With its ability to write essays, emails, poems, and even computer code, it has become a useful tool for professionals in various fields. However, ChatGPT’s responses are not always rooted in reality and are instead generated by a Generative Adversarial Network (GAN). This paper aims to build a text classification model for a chatbot using Python. The model is trained on a dataset consisting of customer responses to a survey and their corresponding class labels. Many classifiers are trained and tested, such as naive Bayes, random forest, extra trees, and decision trees. The results show that the extra trees classifier performs the best with an accuracy of 90%. The system demonstrates the importance of text preprocessing and selecting appropriate classifiers for text classification tasks in building an effective chatbot. In this paper, we also explore the capabilities and limitations of ChatGPT and its alternatives in 2023. We present a comprehensive overview of the alternative’s performance. The work here concludes with a discussion of the future directions of large language models and their impact on society and technology.