DFCV: A Novel Approach for Message Dissemination in Connected Vehicles using Dynamic Fog

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities

Hybrid-Vehcloud: An Obstacle Shadowing Approach for VANETs in Urban Environment

Routing of messages in Vehicular Ad-hoc Networks (VANETs) is challenging due to obstacle shadowing regions with high vehicle densities, which leads to frequent disconnection problems and blocks radio wave propagation between vehicles. Previous researchers used multi-hop, vehicular cloud or roadside infrastructures to solve the routing issue among the vehicles, but they suffer from significant packet delays and frequent packet losses arising from obstacle shadowing. We proposed a vehicular cloud based hybrid technique called Hybrid-Vehcloud to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. The novelty of our approach lies in the fact that our proposed technique dynamically adapts between obstacle shadowing and non-obstacle shadowing regions. Simulation based performance analysis of Hybrid-Vehcloud showed improved performance over Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP) and Cloud-VANET schemes at high vehicle densities

Asian Indian Perceptions of Normality: A Qualitative Study

Normal mental health has always been defined from a Euro-centric worldview that excludes non-Westem cultures. In fact, what is normal is biased against non-Westem cultural ideals that influenced the definition of mental health. The difference between Eastern and Western cultural values suggest that the two cultures may also have differing views on the definition of normal mental health. The most commonly accepted definition of normality currently in use in the West is based on the models of health, utopia, average, transactional systems, and pragmatism. However, people from non-European cultures, such as Asian Indians, may not be represented by these current parameters of mental health and illness. In this study, the construct of normality was investigated from an Asian Indian perspective. Specifically, interviews were conducted with Asian Indian graduate students in which participants were asked to discuss their perceptions of normal mental health. A Consensual Qualitative Research analysis strategy was then conducted. Five domains were created: Perceptions of Normal, Perceptions of Abnormal, Cause of Mental Illness, Criteria Used to Differentiate Normal from Abnormal, and Difficulties in Defining Normal. The categories within these domains were discussed as they related to psychological treatment services for international students such as well as implications for future research

Vision Encoder-Decoder Models for AI Coaching

This research paper introduces an innovative AI coaching approach by integrating vision-encoder-decoder models. The feasibility of this method is demonstrated using a Vision Transformer as the encoder and GPT-2 as the decoder, achieving a seamless integration of visual input and textual interaction. Departing from conventional practices of employing distinct models for image recognition and text-based coaching, our integrated architecture directly processes input images, enabling natural question-and-answer dialogues with the AI coach. This unique strategy simplifies model architecture while enhancing the overall user experience in human-AI interactions. We showcase sample results to demonstrate the capability of the model. The results underscore the methodology's potential as a promising paradigm for creating efficient AI coach models in various domains involving visual inputs. Importantly, this potential holds true regardless of the particular visual encoder or text decoder chosen. Additionally, we conducted experiments with different sizes of GPT-2 to assess the impact on AI coach performance, providing valuable insights into the scalability and versatility of our proposed methodology.Comment: 6 pages, 2 figure