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An instantaneous and precise coating inspection method is imperative to mitigate the risk of flaws, defects, and discrepancies on coated surfaces. While many studies have demonstrated the effectiveness of automated visual inspection (AVI) approaches enhanced by computer vision and deep learning, critical challenges exist for practical applications in the manufacturing domain. Computer vision has proven to be inflexible, demanding sophisticated algorithms for diverse feature extraction. In deep learning, supervised approaches are constrained by the need for annotated datasets, whereas unsupervised methods often result in lower performance. Addressing these challenges, this paper proposes a novel deep learning-based automated visual inspection (AVI) framework designed to minimize the necessity for extensive feature engineering, programming, and manual data annotation in classifying fuel injection nozzles and discerning their coating interfaces from scratch. This proposed framework comprises six integral components: It begins by distinguishing between coated and uncoated nozzles through gray level co-occurrence matrix (GLCM)-based texture analysis and autoencoder (AE)-based classification. This is followed by cropping surface images from uncoated nozzles, and then building an AE model to estimate the coating interface locations on coated nozzles. The next step involves generating autonomously annotated datasets derived from these estimated coating interface locations. Subsequently, a convolutional neural network (CNN)-based detection model is trained to accurately localize the coating interface locations. The final component focuses on enhancing model performance and trustworthiness. This framework demonstrated over 95% accuracy in pinpointing the coating interfaces within the error range of ± 6 pixels and processed at a rate of 7.18 images per second. Additionally, explainable artificial intelligence (XAI) techniques such as t-distributed stochastic neighbor embedding (t-SNE) and the integrated gradient substantiated the reliability of the models.

 

Data analytics and computational thinking are essential for processing and analyzing data from sensors, and presenting the results in formats suitable for decision-making. However, most undergraduate construction engineering and management students struggle with understanding the required computational concepts and workflows because they lack the theoretical foundations. This has resulted in a shortage of skilled workforce equipped with the required competencies for developing sustainable solutions with sensor data. End-user programming environments present students with a means to execute complex analysis by employing visual programming mechanics. With end-user programming, students can easily formulate problems, logically organize, analyze sensor data, represent data through abstractions, and adapt the results to a wide variety of problems. This paper presents a conceptual system based on end-user programming and grounded in the Learning-for-Use theory which can equip construction engineering and management students with the competencies needed to implement sensor data analytics in the construction industry. The system allows students to specify algorithms by directly interacting with data and objects to analyze sensor data and generate information to support decision-making in construction projects. An envisioned scenario is presented to demonstrate the potential of the system in advancing students’ data analytics and computational thinking skills. The study contributes to existing knowledge in the application of computational thinking and data analytics paradigms in construction engineering education. 

To promote engagement, recommendation algorithms on platforms like YouTube increasingly personalize users’ feeds, limiting users’ exposure to diverse content and depriving them of opportunities to reflect on their interests compared to others’. In this work, we investigate how exchanging recommendations with strangers can help users discover new content and reflect. We tested this idea by developing OtherTube—a browser extension for YouTube that displays strangers’ personalized YouTube recommendations. OtherTube allows users to (i) create an anonymized profile for social comparison, (ii) share their recommended videos with others, and (iii) browse strangers’ YouTube recommendations. We conducted a 10-day-long user study (n = 41) followed by a post-study interview (n = 11). Our results reveal that users discovered and developed new interests from seeing OtherTube recommendations. We identified user and content characteristics that affect interaction and engagement with exchanged recommendations; for example, younger users interacted more with OtherTube, while the perceived irrelevance of some content discouraged users from watching certain videos. Users reflected on their interests as well as others’, recognizing similarities and differences. Our work shows promise for designs leveraging the exchange of personalized recommendations with strangers. 

Struggling to curb misinformation, social media platforms are experimenting with design interventions to enhance consumption of credible news on their platforms. Some of these interventions, such as the use of warning messages, are examples of nudges---a choice-preserving technique to steer behavior. Despite their application, we do not know whether nudges could steer people into making conscious news credibility judgments online and if they do, under what constraints. To answer, we combine nudge techniques with heuristic based information processing to design NudgeCred--a browser extension for Twitter. NudgeCred directs users' attention to two design cues: authority of a source and other users' collective opinion on a report by activating three design nudges---Reliable, Questionable, and Unreliable, each denoting particular levels of credibility for news tweets. In a controlled experiment, we found that NudgeCred significantly helped users (n=430) distinguish news tweets' credibility, unrestricted by three behavioral confounds---political ideology, political cynicism, and media skepticism. A five-day field deployment with twelve participants revealed that NudgeCred improved their recognition of news items and attention towards all of our nudges, particularly towards Questionable. Among other considerations, participants proposed that designers should incorporate heuristics that users' would trust. Our work informs nudge-based system design approaches for online media. 

As news organizations embrace transparency practices on their websites to distinguish themselves from those spreading misinformation, HCI designers have the opportunity to help them effectively utilize the ideals of transparency to build trust. How can we utilize transparency to promote trust in news? We examine this question through a qualitative lens by interviewing journalists and news consumers---the two stakeholders in a news system. We designed a scenario to demonstrate transparency features using two fundamental news attributes that convey the trustworthiness of a news article: source and message. In the interviews, our news consumers expressed the idea that news transparency could be best shown by providing indicators of objectivity in two areas (news selection and framing) and by providing indicators of evidence in four areas (presence of source materials, anonymous sourcing, verification, and corrections upon erroneous reporting). While our journalists agreed with news consumers' suggestions of using evidence indicators, they also suggested additional transparency indicators in areas such as the news reporting process and personal/organizational conflicts of interest. Prompted by our scenario, participants offered new design considerations for building trustworthy news platforms, such as designing for easy comprehension, presenting appropriate details in news articles (e.g., showing the number and nature of corrections made to an article), and comparing attributes across news organizations to highlight diverging practices. Comparing the responses from our two stakeholder groups reveals conflicting suggestions with trade-offs between them. Our study has implications for HCI designers in building trustworthy news systems. 

ABSTRACT Bacteria form complex multicellular structures on solid surfaces known as biofilms, which allow them to survive in harsh environments. A hallmark characteristic of mature biofilms is the high-level antibiotic tolerance (up to 1,000 times) compared with that of planktonic cells. Here, we report our new findings that biofilm cells are not always more tolerant to antibiotics than planktonic cells in the same culture. Specifically, Escherichia coli RP437 exhibited a dynamic change in antibiotic susceptibility during its early-stage biofilm formation. This phenomenon was not strain specific. Upon initial attachment, surface-associated cells became more sensitive to antibiotics than planktonic cells. By controlling the cell adhesion and cluster size using patterned E. coli biofilms, cells involved in the interaction between cell clusters during microcolony formation were found to be more susceptible to ampicillin than cells within clusters, suggesting a role of cell-cell interactions in biofilm-associated antibiotic tolerance. After this stage, biofilm cells became less susceptible to ampicillin and ofloxacin than planktonic cells. However, when the cells were detached by sonication, both antibiotics were more effective in killing the detached biofilm cells than the planktonic cells. Collectively, these results indicate that biofilm formation involves active cellular activities in adaption to the attached life form and interactions between cell clusters to build the complex structure of a biofilm, which can render these cells more susceptible to antibiotics. These findings shed new light on bacterial antibiotic susceptibility during biofilm formation and can guide the design of better antifouling surfaces, e.g., those with micron-scale topographic structures to interrupt cell-cell interactions. IMPORTANCE Mature biofilms are known for their high-level tolerance to antibiotics; however, antibiotic susceptibility of sessile cells during early-stage biofilm formation is not well understood. In this study, we aim to fill this knowledge gap by following bacterial antibiotic susceptibility during early-stage biofilm formation. We found that the attached cells have a dynamic change in antibiotic susceptibility, and during certain phases, they can be more sensitive to antibiotics than planktonic counterparts in the same culture. Using surface chemistry-controlled patterned biofilm formation, cell-surface and cell-cell interactions were found to affect the antibiotic susceptibility of attached cells. Collectively, these findings provide new insights into biofilm physiology and reveal how adaptation to the attached life form may influence antibiotic susceptibility of bacterial cells.