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HM-2 A Sunrise You Can Hear: Blending Code, Color, and Sound
No full textA Sunrise You Can Hear: Blending Code, Color, and Sound
Color perception is an important part of the human experience, yet the ability to perceive colors is not possible for those individuals who are blind or visually impaired. Tremendous strides have been made in developing technology that assists blind or visually impaired individuals to perceive colors through sound. In this project, we explore how a blind or visually impaired individual can hear an artistic, natural phenomenon such as a sunrise. By creating an algorithmic music video, we investigate how blind individuals can associate colors with sounds thereby creating a computer-generated audiovisual experience that creates a realistic sunrise simulation.
In this study, visible light frequencies are algorithmically mapped to audible sound frequencies. The project consists of three key components: color gradient generation, mapping colors to audio, and music video creation. First, the visual aspect of the sunrise is created using Wolfram Language. The gradient colors of the sky transition from black to red, red to yellow, and yellow to blue. Second, an audio composition is generated that matches each color transition to harmonic progressions in real-time. This is done by dividing a piano keyboard into three sections: red (left), green (middle), and blue (right), where each section corresponds to a specific set of pitches. As the sunrise grows, the mapped pitches are played by beautifully sounding instruments such as the Shakuhachi and Shamisen, producing a rich, immersive sound. Additional audio normalization and fading techniques are used to enhance the listening experience. Finally, the video component is created by generating multiple frames using graphical transformations in Wolfram Language. Each frame consists of a gradient sky background that changes according to the progression of the sunrise. The lower portion of the frame features a darkened horizon landscape, providing contrast to the transitioning colors of the sky. All of the frames are then combined into a video sequence, synchronized with the generated audio, resulting in a music video representation of a sunrise.
The results of this study show a correlation between visual and auditory senses, proving that you can experience visual beauty through sound. This work highlights the future potential of algorithmic tools to create fascinating multimedia experiences, even through the use of Artificial Intelligence (AI). Moreover, this project opens pathways for broader applications in assistive technology, education sectors, and artistic expressions
FA-3 51 Gasoline Stations: The people and places of my 26 Gas Stations and Ed Ruscha\u27s Twentysix Gasoline Stations
No full text“26 Gas Stations” is the title of the second movement of my forthcoming work for symphony orchestra, the Route 66 Suite. The music was inspired by the people I met at various current and former gas stations along Route 66. I whittled it down to a list of 26 stations as an homage to artist Ed Ruscha and his 1963 photography book Twentysix Gasoline Stations. My original intent was to find inspiration in the locations of the stations in Ruscha’s book, but through my research and travels, I learned that nearly all of them have been demolished, which I find rather un-inspiring. This paper serves to share the locations of those stations, the 26 places in my list, and to explain how all 51 locations inspired the music in my “26 Gas Stations.”
In October 1962, Ruscha drove from his home in Los Angeles, CA to his hometown of Oklahoma City, OK. He took photographs of gas stations along the way, self-publishing the series as a book, one of the first photography books. There were no descriptions of the stations or any text other than the name of the station and the city in which is stood (e.g. “CONOCO, SAYRE, OKLAHOMA”). The exact locations were never published, and they have remained somewhat of a treasure hunt for interested parties. Swiss photographer Martin Möll (1972–2019) may have discovered most of the locations, but he never published them nor did he include them in his series of photographs titled Twentysix Gasoline Stations Revisited (2009). In 2013, a group of students and faculty from the University of Oklahoma attempted to locate Ruscha’s 26 stations using Möll’s research and other sources. Results (such as site locations) from this project titled “Road to Ruscha” were never published. However, I was able to find some of the students’ photographs online.
Using aerial and satellite photography, road and topographic maps, phone books, Möll’s/Road to Ruscha’s locations, and other sources, I have been able to confirm the locations of 23 of Ruscha’s 26. I also discovered that four of Möll’s/Road to Ruscha’s locations were incorrect. None of the 26 stations in Ruscha’s book are still in operation. Only four of the buildings that once housed these 26 stations are still standing, of which one is still in business as a gift shop. After meeting the owners, it became one of the stations in my list of 26
Common Questions and Misconceptions About Energy Drinks: What Does the Scientific Evidence Really Show?
Full text linkEnergy drinks are a commonly consumed beverage, and studies suggest a possible performance-enhancing effect. A Google Scholar search using the keywords “energy drinks” and “exercise” yields numerous results, underscoring the voluminous research on this topic. However, there are questions regarding the effectiveness and safety of energy drinks. These questions include, but are not limited to: (1) What are the main active ingredients in energy drinks? (2) Do energy drinks assist in weight management? (3) Do energy drinks enhance aerobic performance? (4) Do energy drinks enhance athletic speed? (5) Do energy drinks improve reaction time? (6) Do energy drinks enhance lean tissue mass? (7) Can energy drinks improve cognitive performance? (8) Does the acute consumption of energy drinks elevate resting energy expenditure? (9) Is there any evidence to suggest that energy drinks are more effective than an identical serving of caffeine alone? (10) Are there sex differences in the response to energy drink consumption? (11) Do energy drinks affect sleep or sleepiness? (12) Should pregnant women avoid energy drinks? (13) Do energy drinks adversely affect cardiovascular function? (14) Does consuming energy drinks cause brain damage? (15) What are other safety considerations regarding energy drinks? (16) Is there any evidence to suggest that energy drinks are more effective than an identical serving of caffeine alone? (17) If caffeine is the main active ingredient in energy drinks and coffee, why is there a discrepancy in the adverse events reported for each? To address these questions, we performed an evidence-based scientific evaluation of the literature on energy drink supplementation
LAB: Developing Explainable Multimodal AI Models With Hands-on Lab on the Life-cycle of Rare Event Prediction in Manufacturing
Full text linkIn the age of Industry 4.0 and smart automation, unplanned downtime is costing industries over 2 million per hour due to downtime caused by unexpected or rare\u27\u27 events. The extreme rarity of these events makes their detection and prediction a significant challenge for AI practitioners. Factors such as the lack of high-quality data, methodological gaps in the literature, and limited practical experience with multimodal data exacerbate the difficulty of rare event detection and prediction. This lab will provide hands-on experience to learn how to address these challenges by exploring the entire lifecycle of rare event analysis, from data generation and preprocessing to model development and evaluation. The development of a \textit{process ontology} and its use for user-level explanations will also be demonstrated. Participants will be introduced to the limited publicly available datasets and, more importantly, gain hands-on experience with a newly developed multi-modal dataset designed explicitly for rare event prediction. Through several hands-on sessions, participants will learn how to generate such a high-quality dataset and the practical use of this dataset to develop rare event prediction models. Those interested in developing AI models involving diverse multimodal data for other applications will also benefit from participation
Villain or Victor: Advocating for Middle Level Students in the Face of Systemic Challenges
Full text linkMiddle level students often face significant systemic challenges that can hinder their academic and personal development, positioning them in narratives where they are perceived as “villains” instead of “victors.” This paper explores the role of educators as advocates for these students, drawing parallels between the concept of superhero villains— figures who, like many students, are misunderstood or marginalized by society. Preservice educators will examine historical examples of heroes, who were initially viewed as villains for challenging the status quo but later celebrated for their transformative impact. Grounded in the principles of This We Believe (2020) by the Association for Middle Level Education (AMLE), the discussion highlights how creating developmentally responsive, challenging, and equitable learning environments empowers students to overcome systemic barriers and emerge as heroes in their own educational journeys
An Exploration of the Disproportionate Representation of Youth with Learning Disabilities in the Juvenile Justice System
Full text linkThis article examines the factors and potential remedial approaches to the longstanding overrepresentation of adolescents with learning disabilities in the juvenile justice system. As schools look to harden themselves and reduce their appeal as possible targets of violence, this group of students becomes more vulnerable to policies and decisions that can increase the risk of engagement with the juvenile justice system and prolonged criminality versus normal developmental adjustment into adulthood. This requires considering the unique challenges that this group faces, the impacts of the main hypotheses explicating the phenomenon, and considering ways that classroom educators can help to meaningfully divert students using trauma-informed and responsive practices and policies
SS-16 Using political affiliation to study in-group and out-group behavior
No full textSocial conformity occurs when many people change their beliefs or behaviors to better fit with the characteristics and beliefs of the group. This conformity can take many forms, it could be a person going along with the group decision even if they disagree, pretending to like something to fit into the group, or coming to believe something is true because their group believes it. The consistent finding in the literature is that people are more likely to conform to the norms and behaviors of their own group (in-group) than to a group to which they do not belong (out-group). While other studies have shown that cultural background, age, gender, and personality can influence conformity, the current study wanted to determine if stated political affiliation (Democrat/Republican) would also influence one’s willingness to either conform or not to the group.
Participants were presented with a series of 50/50 green and blue color distributed pixelated squares on a computer. They were first asked to identify if the image was more green or more blue, and then they were asked to submit their confidence rating of their decision on a Likert scale from 1 (not confident) to 5 (very confident). After each presentation, the participants were prompted with a statement that either stated the opposing party or their political party picked the opposite color that they previously selected (e.g., if they selected green, they were told the group had selected blue). The participants were then asked again about the distribution of blue and green in the image and asked if they wanted to change their response. It was hypothesized that the participants would be more likely to change their response (i.e., conform) for the in-group (same political party) and less likely to change their response for the outgroup (opposite political party)
SS-21 American Bipartisanship and the Media Effect
No full textOver the past 20 years, digital media use in the United States has skyrocketed. Social media and other online platforms have become the primary sources for political news, gaining widespread popularity among Americans. As new technologies have become more prevalent new challenges have arisen. Most notably the spread of misinformation on digital platforms has become prevalent, and American’s often self-select into news outlets that reinforce their beliefs. Another worrisome trend is that the American political climate has also grown increasingly polarized. The two major parties more divided than ever and partisan animosity within the mass public is increasing. This backdrop leads to a question of do social media and online news contribute to this growing political divide? Because social media algorithms amplify users\u27 existing beliefs by tailoring content based on their interactions, these websites could further reinforce political polarization by creating echo chambers. Given this, examining the relationship between the digital age and political division is essential to finding potential solutions to bridge the gap. For my analysis, I used data from the American National Election Studies (ANES) 2020-2022 Social Media Study and conducted additional analysis using multiple regression. The independent variables included feeling thermometer ratings of the Democratic and Republican parties (based on party identification), while the dependent variable was whether an individual consumed online news. The results suggest that increased online news consumption correlates with more negative feelings toward the Republican Party among Democrats but does not have the same effect on Republicans. Specifically, Democrats tend to view Republicans less favorably as their consumption of online news increases, while Republicans\u27 views of Democrats remain unchanged by their social media use