Augmented reality allows the superimposing of computer-generated images over real scenes in real time. This paper describes a project in which an interactive solar system was developed to help middle school students in the sciences understand spatial concepts using augmented reality. The marker-based augmented reality application works by using a camera to read a marker, then calculates the camera position in 3D space, and superimposes the solar system. With a webcam and a laptop, augmented reality application development is highly accessible. The visualization application research detailed here will be used for formal pedagogical research examining whether students benefit from this method by conducting both pre-event and post-event assessment surveys. The surveys will gather valuable feedback from middle-school science students concerning whether the augmented reality method is effective for teaching and learning. Preliminary feedback from students and educators indicates that this visual teaching method is effective.

A sensor-based community network for environmental data gathering and predictive analysis has been developed. A mesh network of wireless sensors reports data to a central site for environmental monitoring and risk identification. Data analysis and visual presentation is provided in a geographical and temporal context. This network is considered green due to decreased energy usage by the overall network as well as its actual application, which permits environmental information to be contextually presented and communicated with concerned urban community as well as decision makers. Periodic data reporting from the sensor network, in contrast with the usual timestamp synchronisation, reduces the amount of communication required between network nodes, resulting in an overall energy saving, while not compromising the nature of the data gathered. The sensor network applications provide an outstanding representation of green networking as sparse but sufficient environmental monitoring, accompanied by real-time data analysis, and historical pattern identification permits risk identification in support of public safety and protection.

Despite significant advancements in Artificial Intelligence (AI) and Large Language Models (LLMs), detecting and mitigating bias remains a critical challenge, particularly on social media platforms like X (formerly Twitter), to address the prevalent cyberbullying on these platforms. This research investigates the effectiveness of leading LLMs in generating synthetic biased and cyberbullying data and evaluates the proficiency of transformer AI models in detecting bias and cyberbullying within both authentic and synthetic contexts. The study involves semantic analysis and feature engineering on a dataset of over 48,000 sentences related to cyberbullying collected from Twitter (before it became X). Utilizing state-of-the-art LLMs and AI tools such as ChatGPT-4, Pi AI, Claude 3 Opus, and Gemini-1.5, synthetic biased, cyberbullying, and neutral data were generated to deepen the understanding of bias in human-generated data. AI models including DeBERTa, Longformer, BigBird, HateBERT, MobileBERT, DistilBERT, BERT, RoBERTa, ELECTRA, and XLNet were initially trained to classify Twitter cyberbullying data and subsequently fine-tuned, optimized, and experimentally quantized. This study focuses on intersectional cyberbullying and multilabel classification to detect both bias and cyberbullying. Additionally, it proposes two prototype applications: one that detects cyberbullying using an intersectional approach and the innovative CyberBulliedBiasedBot that combines the generation and detection of biased and cyberbullying content.

In an era where dietary habits significantly impact health, technological interventions can offer personalized and accessible food choices. This paper introduces Chef Dalle, a recipe recommendation system that leverages multi-model and multimodal human-computer interaction (HCI) techniques to provide personalized cooking guidance. The application integrates voice-to-text conversion via Whisper and ingredient image recognition through GPT-Vision. It employs an advanced recipe filtering system that utilizes user-provided ingredients to fetch recipes, which are then evaluated through multi-model AI through integrations of OpenAI, Google Gemini, Claude, and/or Anthropic APIs to deliver highly personalized recommendations. These methods enable users to interact with the system using voice, text, or images, accommodating various dietary restrictions and preferences. Furthermore, the utilization of DALL-E 3 for generating recipe images enhances user engagement. User feedback mechanisms allow for the refinement of future recommendations, demonstrating the system’s adaptability. Chef Dalle showcases potential applications ranging from home kitchens to grocery stores and restaurant menu customization, addressing accessibility and promoting healthier eating habits. This paper underscores the significance of multimodal HCI in enhancing culinary experiences, setting a precedent for future developments in the field.

With the growth of Infrastructure as a Service (IaaS) in cloud computing, and the Internet of Things (IoT), network monitoring of the global infrastructure will continue to expand, as cybersecurity will become of primary importance. Current network monitoring approaches, particularly those that are agent-based, are proprietary, costly, and may not scale. This paper proposes a lightweight, scalable agentless system which can be configured, setup, and begin monitoring network health within minutes. The architecture utilizes a polling script to query Simple Network Monitoring Protocol (SNMP) demon for metrics, Prometheus Time Series Database instances for storage, and a Grafana Dashboard for metric presentation and alerting. The system design removes the learning curve necessary for most open source monitoring systems, while still providing similar metrics. The approach demonstrated here shows the ease and security with which specific site network monitoring can be deployed in a cloud configuration. Data from the network monitoring can be forwarded to a central analysis site for assessment and correlation. The proposed system is scalable, extensible for use in most cloud hosting environments, and provides network managers with direct control over the monitoring process and dashboard.