Halloween is one of my favorite days of the year. What other day allows anyone and everyone to dress up in costumes, visit haunted houses and hand out candy. It can be fun for both kids and adults alike. The notion of the spooky holiday dates back over 2,000 years, and is rooted in ancient Celtic festival of Samhain, which marked the end of the harvest season and the beginning of winter. The Celts believed that on the night of October 31st, the boundary between the living and the dead was blurred, and ghosts and spirits could roam freely. While that’s good enough reason to stay in, universal participation in the holiday gives plenty reason to get out and explore the neighborhood.

I grew up in a less technical era, where Halloween decorations consisted of pumpkin carvings, skeletons and spider webs. Today’s digital age offers new ways to scare, making it possible to take your Halloween celebrations to the next level of spookiness and fun. Just take a look at these creepy looking holograms!

Here are some more examples of Halloween-themed technology and tech-related activities:

1. Smart Halloween Decorations: You can use smart lighting systems like Philips Hue or smart plugs to control your Halloween decorations remotely. Set up eerie lighting effects or automate your decorations to turn on and off at specific times.
2. Augmented Reality (AR) and Virtual Reality (VR) Apps: Download Halloween-themed AR apps or games that can transform your surroundings or take you on virtual ghost tours. VR can also be used for immersive horror experiences.
3. Halloween-Themed Apps: There are many apps available for creating digital Halloween decorations, finding nearby haunted houses, or generating spooky soundscapes.
4. 3D Printing: Create your own custom Halloween props and decorations using 3D printing technology. You can find a plethora of designs online, from pumpkin carving stencils to spooky figurines.
5. LED Costumes: Design costumes with LED lights or EL wire to create glowing, eye-catching effects. These costumes can be programmed to change colors and patterns.
6. Online Costume Shopping: Technology makes it easier to shop for Halloween costumes. Many websites and apps offer virtual try-on features, allowing you to see how a costume looks on you before buying.
7. Halloween-Themed Video Games: Play spooky video games to get into the Halloween spirit. Many games release special Halloween events or updates during the season.
8. DIY Electronics Projects: Create your own Halloween-themed electronics projects, such as motion-activated props, sound-activated decorations, or Arduino-controlled displays.
9. Home Automation: Use home automation systems like Amazon Alexa, Google Home, or Apple HomeKit to control Halloween decorations, play spooky music, or trigger Halloween-themed routines with voice commands.
10. Social Media and AR Filters: Platforms like Instagram and Snapchat often introduce special Halloween-themed augmented reality filters and stickers to enhance your photos and videos.
11. Halloween-Themed Soundtracks and Podcasts: Listen to creepy podcasts or Halloween-themed music playlists using streaming services like Spotify or Apple Music.
12. Drone Decorations: Drones equipped with LED lights can create spooky aerial displays in the night sky for your Halloween party.
13. Escape Room Games: Many places offer Halloween-themed escape room experiences that blend technology with interactive storytelling for a spooky adventure.
14. Smart Doorbells: Upgrade to a smart doorbell with a camera, which allows you to see and interact with trick-or-treaters, even when you’re not at home.
15. Virtual Halloween Parties: Host virtual Halloween parties using video conferencing tools and online games to celebrate with friends and family.

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I’ll be honest, I haven’t been diving into AI software as fast as I should be. I continue to integrate ChatGPT into my software, but I haven’t explored the wider array of offerings by OpenAI. Recently, I was introduced to DALL·E from a student using it to create images for a capstone project. I did some more research to learn more about the software. DALL·E is an artificial intelligence model developed by OpenAI and builds upon the GPT (Generative Pre-trained Transformer) architecture, the same underlying technology as ChatGPT, but with a focus on generating images from textual descriptions. Its name “DALL·E” is a combination of “Dali” (after the surrealist artist Salvador Dalí) and “WALL·E” (the Disney-Pixar robot character), reflecting its ability to generate surreal and creative images from textual descriptions.

Here’s a more detailed explanation of DALL·E:

1. Model Architecture: DALL·E is based on a variant of the Transformer architecture, which is a neural network architecture originally designed for natural language processing tasks. It extends this architecture to perform image generation from text.

2. Text-to-Image Generation: DALL·E takes textual descriptions as input and generates corresponding images as output. The text input can be a short phrase, sentence, or even a longer paragraph, describing a particular scene or concept. The model’s primary goal is to generate images that match the textual description provided.

3. Creativity and Imagination: One of the interesting aspects of DALL·E is its ability to generate highly imaginative and surreal images. It can combine concepts and ideas in novel ways, creating visuals that may not exist in the real world. For example, if you describe “an armchair in the shape of an avocado,” DALL·E can generate a realistic image of just that.

4. Image Resolution: DALL·E can generate images at a resolution of 256×256 pixels, which, while not as high as some other image generation models, is still quite impressive given its ability to generate novel and creative visuals.

5. Training Data: DALL·E was trained on a massive dataset of text and images from the internet. This dataset includes various sources, allowing the model to learn from a wide range of concepts and ideas.

6. Ethical and Societal Considerations: DALL·E, like other AI models, raises ethical and societal considerations. The generated content can be used for both positive and negative purposes, and it’s essential to use the technology responsibly and consider its potential impact on privacy, copyright, and misinformation.

7. Limitations: DALL·E, like all AI models, has limitations. It may not always generate images that precisely match the textual descriptions, and the output can sometimes be unpredictable. Additionally, there is a possibility of the model generating inappropriate or harmful content.

8. Applications: DALL·E has a wide range of potential applications, including creative design, art, content generation, and even assisting in brainstorming and ideation processes. It can be a valuable tool for artists, designers, and anyone looking to visually represent their ideas.

As taken from ChatGPT, “DALL·E is intriguing for generating unique images for model that can generate images from textual descriptions, showcasing the capabilities of AI in the creative domain. Its ability to generate imaginative and surreal images has the potential to revolutionize various industries and creative processes. However, its use also comes with ethical considerations that need to be carefully addressed.”

At the same time, when asking DALL·E to provide me with a unique image that incorporates social media, artificial intelligence and WordPress, the following images was constructed:

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My colleagues and I recently published our article, “Formation and Action of a Learning Community with Collaborative Learning Software,” in the Journal of Management Information Systems. Below is the abstract and citation.

Abstract
This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model.

Reference
Evren Eryilmaz, Brian Thoms, Zafor Ahmed & Howard Lee (2023) Formation and Action of a Learning Community with Collaborative Learning Software, Journal of Management Information Systems, 40:1, 38-55, DOI: 10.1080/07421222.2023.2172774

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My colleagues and I recently presented and published research at EDULEARN 2022 titled, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”. The paper presents on differences in online interaction before and during COVID-19 lockdowns with an emphasis on sentiment analysis. Below is the tag cloud, abstract and full citation of the research.

Abstract
In early 2020, there was a drastic shift in the teaching modality across institutions of higher education as colleges and universities adapted to federal guidelines in the face of COVID-19 restrictions. While most, if not all institutions already had mature learning management systems (LMS), alternative approaches using social components proved invaluable during a time when face-to-face social interaction was largely restricted. More so, a large concern in the shift to remote instruction during COVID-19 lockdowns was the toll on student well-being as traditional learning management systems typically lack social networking capabilities. In this paper, we explore the use of online social networking (OSN) software to facilitate more positive interactions. More specifically, we analyse the sentiment within online discussions to show key differences between users using traditional LMS software and those using modified OSN software. Findings show that students participating during the pandemic, yielded higher levels of social presence and posted more positively to the discussion.

Reference
Thoms, E. Eryilmaz, “Measuring the Effects of a Dynamic Sentiment Analyzer within Online Social Networking Software During COVID-19”, 14th Annual International Conference on Education and New Learning Technologies (EDULEARN 22), July 5, 2022.

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The following abstract is from a student co-authored paper, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” presented by Dr. Jason Isaacs at the 8th International Conference on Data Mining and Applications (DMA 2022), presented on May 28, 2022.

Abstract
Classifier algorithms are a subfield of data mining and play an integral role in finding patterns and relationships within large datasets. In recent years, fake news detection has become a popular area of data mining for several important reasons, including its negative impact on decision-making and its virality within social networks. In the past, traditional fake news detection has relied primarily on information context, while modern approaches rely on auxiliary information to classify content. Modelling with machine learning and natural language processing can aid in distinguishing between fake and real news. In this research, we mine data from Reddit, the popular online discussion forum and social news aggregator, and measure machine learning classifiers in order to evaluate each algorithm’s accuracy in detecting fake news using only a minimal subset of data.

Reference
M. Shariff, B. Thoms, J. Isaacs, “Approaches in Fake News Detection: An Evaluation of Natural Language Processing and Machine Learning Techniques on the Reddit Social Network,” Accepted for inclusion in 8th International Conference on Data Mining and Applications (DMA 2022), May 28-29, 2022.

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