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Mobile computing research has been focused on developing technologies for handheld devices such as mobile phones, notebook computers, and mobile IP. Today, emphasis is increasing on context‐aware computing, which aims to build the intelligence into mobile devices to sense and respond to the user's context. The purpose of this paper is to present a context‐aware mobile computing model (ContextAlert) that senses the user's context and intelligently configures the mobile phone alert mode accordingly.

The paper proposes a three‐step approach in designing the model based on the embedded sensor data (accelerometer, GPS antenna, and microphone) of a G1 Adriod phone. As adaptivity is essential for context‐aware computing, within this model a new learning mechanism is presented to maintain a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal.

The model has been evaluated in many aspects using data collected from human subjects. The experiment results show that the proposed model performs well and yields a promising result.

This paper is distinguished from other previous papers by: first, using multiple sensors embeded in the mobile phone, which is more realistic for detecting the user's context than having various sensors attached to different parts of user's body; second, by being a novel model that uses sensed contextual information to provide a service that better synchronizes the user's daily life with a context‐aware alert mode. With this service, the user can avoid the problems such as forgetting to switch to vibrate mode while in a meeting or a movie theater, and taking the risk of picking up a phone call while driving, and third, being an adaptive learning algorithm that maintains a constant adaptivity rate for new learning while keeping the catastrophic forgetting problem minimal.

The indigenous and sustainable knowledge possessed by the tribal people and communities plays an extremely important role in the conservation of the climate. As per the United Nations Sustainable Development Goals (UN SDGs), tribal communities play an important role in preserving traditional knowledge, culture, food practices and textiles. The remains of the Indus Valley civilisation highlight the importance of the culture and religion. Animism and the worship of nature to achieve success in routines and tasks have been practised by these tribes for centuries. India is a mosaic of more than 705 different colourful tribes, and each of these tribes has their own identity that differentiates it from the other tribes. With increasing ecological complexities, the knowledge owned by the tribes is extremely useful in the preservation of the climate. This chapter seeks to explore the various hues and colours that constitute the mosaic of Indian tribal culture.

This work aims to argue that it is possible to address discrimination issues that naturally arise in contemporary audio CAPTCHA challenges and potentially enhance the effectiveness of audio CAPTCHA systems by adapting the challenges to the user characteristics.

A prototype has been designed, called PrivCAPTCHA, to offer privacy-preserving, user-centric CAPTCHA challenges. Anonymous credential proofs are integrated into the Session Initiation Protocol (SIP) protocol and the approach is evaluated in a real-world Voice over Internet Protocol (VoIP) environment.

The results of this work indicate that it is possible to create VoIP CAPTCHA services offering privacy-preserving, user-centric challenges while maintaining sufficient efficiency.

The proposed approach was evaluated through an experimental implementation to demonstrate its feasibility. Additional features, such as appropriate user interfaces and efficiency optimisations, would be useful for a commercial product. Security measures to protect the system from attacks against the SIP protocol would be useful to counteract the effects of the introduced overhead. Future research could investigate the use of this approach on non-audio CAPTCHA services.

PrivCAPTCHA is expected to achieve fairer, non-discriminating CAPTCHA services while protecting the user’s privacy. Adoption success relies upon the general need for employment of privacy-preserving practices in electronic interactions.

This approach is expected to enhance the quality of life of users, who will now receive CAPTCHA challenges closer to their characteristics. This applies especially to users with disabilities. Additionally, as a privacy-preserving service, this approach is expected to increase trust during the use of services that use it.

To the best of authors’ knowledge, this is the first comprehensive proposal for privacy-preserving CAPTCHA challenge adaptation. The proposed system aims at providing an improved CAPTCHA service that is more appropriate for and trusted by human users.