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Keynote: Story Culture Live: Black American Story Spaces as Actionable Antiracism Work
Get PDF“Story Culture Live: Black American Story Spaces as Actionable Antiracism Work,“ was a keynote given at the Northeast Writing Centers Association Conference at the University of New Hampshire in spring 2023. The keynote details the genesis of my podcast, Story Culture Live, which reimagines storytelling as actionable activism in antiracist work and explores concepts such as Black teller agency, kinship, and collective responses to tensions through storytelling that can inform and build new stories in writing centers
Comments as Reviews: Predicting Answer Acceptance by Measuring Sentiment on Stack Exchange
No full textOnline communication has increased the need to rapidly interpret complex emotions due to the volatility of the data involved; machine learning tasks that process text, such as sentiment analysis, can help address this challenge by automatically classifying text as positive, negative, or neutral. However, while much research has focused on detecting offensive or toxic language online, there is also a need to explore and understand the ways in which people express positive emotions and support for one another in online communities. This is where sentiment dictionaries and other computational methods can be useful, by analyzing the language used to express support and identifying common patterns or themes.his research was conducted by compiling data from social question and answering around machine learning on the site Stack Exchange. Then a classification model was constructed using binary logistic regression. The objective was to discover whether predictions of marked solutions are accurate by treating the comments as reviews. Measuring collaboration signals may help capture the nuances of language around support and assistance, which could have implications for how people understand and respond to expressions of help online. By exploring this topic further, researchers can gain a more complete understanding of the ways in which people communicate and connect online
Curriculum Design of Artificial Intelligence and Sustainability in Secondary School
Get PDFArtificial Intelligence is revolutionizing numerous sectors with its transformative power, while at the same time, there is an increasing sense of urgency to address sustainability challenges. Despite the significance of both areas, secondary school curriculums still lack comprehensive integration of AI and sustainability education. This paper presents a curriculum designed to bridge this gap. The curriculum integrates progressive objectives, computational thinking competencies and system thinking components across five modules—awareness, knowledge, interaction, empowerment and ethics—to cater to varying learner levels. System thinking components help students understand sustainability in a holistic manner. Computational thinking competencies aim to cultivate computational thinkers to guide the design of curriculum activities
Closing the Gap: Leveraging AES-NI to Balance Adversarial Advantage and Honest User Performance in Argon2i
Get PDFThe challenge of providing data privacy and integrity while maintaining efficient performance for honest users is a persistent concern in cryptography. Attackers exploit advances in parallel hardware and custom circuit hardware to gain an advantage over regular users. One such method is the use of Application-Specific Integrated Circuits (ASICs) to optimize key derivation function (KDF) algorithms, giving adversaries a significant advantage in password guessing and recovery attacks. Other examples include using graphical processing units (GPUs) and field programmable gate arrays (FPGAs). We propose a focused approach to close the gap between adversarial advantage and honest user performance by leveraging the hardware optimization AES-NI (Advanced Encryption Standard New Instructions). AES-NI is widely available in modern x86 architecture microprocessors. Honest users can negate the adversary advantage by diminishing the utility of their computational power. We explore the impact of AES-NI on the Argon2i KDF algorithm, a widely-used and recommended password hashing function. Through our analysis, we demonstrate the effectiveness of incorporating AES-NI in reducing the advantage gained by attackers using ASICs. We also discuss the security and performance trade-offs to provide guidelines for practical implementation in deployed cryptosystems
2023 Standard-sized Seedless Watermelon Cultivar Evaluation in Indiana
Get PDFThe annual watermelon cultivar evaluation trial is conducted at Southwest Purdue Agricultural Center (SWPAC), in Vincennes, Indiana. The trial evaluates yield, fruit quality, and overall plant performance of commercial watermelon cultivars and advanced breeding lines. The trial is financially supported by Purdue Extension and seed companies. The 2023 standard-sized triploid watermelon cultivar trial had 35 cultivars, including six with solid dark-green rind patterns, and one with a solid light-green rind pattern
An Exclusion Distance Controls the Efflorescence Pattern Distribution on Porous Media during Salt Solution Evaporation
Get PDFTowards Prediction of A User\u27s Identity from Missing Biometric Data from IoT Devices and Understanding Associated Risks
Get PDFWith the emergence of the internet of things (IoT), smart sensing devices such as smartwatches and smartphones are rich with various sensors helping us with different services, including unlocking cars and validating financial transactions. But, often these services are delivered based on a user’s sensitive personal information, including demographic identity, and various sensitive data, such as heart rate. Therefore, it is important to understand how missing biometric samples can be fatal to predict a user’s identity and raise threats to the user’s IoT-connected cyber-physical space. This project will utilize machine learning and data fusion techniques on smartwatch/smartphone data to predict a user’s identity that may lead to different risks. Thereby, our findings will guide developers to develop robust authentications to foster global security
A Textile Architecture-Based Discrete Modeling Approach to Simulating Fabric Draping Processes
Get PDFFabric draping, which is referred to as the process of forming of textile reinforcements over a 3D mold, is a critical stage in composites manufacturing since it determines the fiber orientation that affects subsequent infusion and curing processes and the resulting structural performance. The goal of this study is to predict the fabric deformation during the draping process and develop in-depth understanding of fabric deformation through an architecture-based discrete Finite Element Analysis (FEA). A new, efficient discrete fabric modeling approach is proposed by representing textile architecture using virtual fiber tows modeled as Timoshenko beams and connected by the springs and dashpots at the intersections of the interlaced tows. Both picture frame and cantilever beam bending tests were carried out to characterize input model parameters. The predictive capability of the proposed modeling approach is demonstrated by predicting the deformation and shear angles of a fabric subject to hemisphere draping. Key deformation modes, including bending and shearing, are successfully captured using the proposed model. The development of the virtual fiber tow model provides an efficient method to illustrate individual tow deformation during draping while achieving computational efficiency in large-scale fabric draping simulations. Discrete fabric architecture and the inter-tow interactions are considered in the proposed model, promoting a deep understanding of fiber tow deformation modes and their contribution to the overall fabric deformation responses
Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling
Get PDFMachine learning (ML) models, including artificial neural networks (ANN), generalized neural regression networks (GRNN), and adaptive neuro-fuzzy interface systems (ANFIS), have received considerable attention for their ability to provide accurate predictions in various problem domains. However, these models may produce inconsistent results when solving linear problems. To overcome this limitation, this paper proposes hybridizations of ML and autoregressive integrated moving average (ARIMA) models to provide a more accurate and general forecasting model for evapotranspiration (ET0). The proposed models are developed and tested using daily ET0 data collected over 11 years (2010–2020) in the Samsun province of Türkiye. The results show that the ARIMA–GRNN model reduces the root mean square error by 48.38%, the ARIMA–ANFIS model by 8.56%, and the ARIMA–ANN model by 6.74% compared to the traditional ARIMA model. Consequently, the integration of ML with ARIMA models can offer more accurate and dependable prediction of daily ET0, which can be beneficial for many branches such as agriculture and water management that require dependable ET0 estimations