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Climate change has a severe impact on the overall ecosystem of the whole world, including humankind. This demo paper presents Climate Bot - a machine reading comprehension system for question answering over documents about climate change. The proposed Climate Bot provides an interface for users to ask questions in natural language and get answers from reliable data sources. The purpose of the climate bot is to spread awareness about climate change and help individuals and communities to learn about the impact and challenges of climate change. Additionally, we open-sourced an annotated climate change dataset CCMRC to promote further research on the topic. This paper describes the dataset collection, annotation, system design, and evaluation.

Sign language is a medium of communication for a person with an auditory and verbal disability or deficiency. Therefore, it is essential to understand their hand gestures without difficulty in order to have effortless and improved communication. Hand gesture detection is a challenging task. In this paper, we proposed an efficient method to recognize and classify images that contains hand gesture, using image Segmentation and the Bottleneck feature from a pre-trained model of Deep Neural Network. Our model achieved a descent accuracy over 96% therefore can be used to build an efficient system which can work as an interpreter between the disabled person and the other party. A comparison between conventional CNN (Convolutional Neural Network) model and our model is also shown to measure the effectiveness of our proposed method.