We consider incorporating topic information into a sequenceto-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior human knowledge that guides them to form informative and interesting responses in conversation, and leverages topic information i...

The development of new technologies has confronted the entire domain of science and industry with issues of big data's scalability as well as its integration with the purpose of forecasting analytics in its life cycle. In predictive analytics, the forecast of near-future and recent past - or in other words, the now-casting - is the continuous study of real-time events and constantly updated whe...

In the digital era, preserving old documents to prevent damage is a significant challenge. One solution this problem reconstruct damaged or lost using image processing and natural language technologies. This article discusses design of tool for correcting reconstructing writing in papers that can be implemented on mini PC. The uses state-of-the-art algorithms such as Convolutional Neural Networ...

Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing algorithms are mostly developed for single network, which fail generalized feature across different networks. In this paper, we study cross-network classification problem, aims at leveraging the abundant labeled information ...

Automated Text Summarization is becoming important due to the vast amount of data being generated. Manual processing documents tedious, mostly absence standards. Therefore, there a need for mechanism reduce text size, structure it, and make it readable users. Natural Language Processing (NLP) critical analyzing large amounts unstructured, text-heavy data. This project aims address concerns with...

Abstract. It is now well established to use shallow artificial neural networks (ANNs) obtain accurate and reliable groundwater level forecasts, which are an important tool for sustainable management. However, we observe increasing shift from conventional ANNs state-of-the-art deep-learning (DL) techniques, but a direct comparison of the performance often lacking. Although they have already clea...

On account of its many successes in inference tasks and imaging applications, Dictionary Learning (DL) related sparse optimization problems have garnered a lot research interest. In DL area, most solutions are focused on single-layer dictionaries, whose reliance handcrafted features achieves somewhat limited performance. With the rapid development deep learning, improved methods called Deep (DD...

In recent years, deep learning approaches have gained significant interest as a way of building hierarchical representations from unlabeled data. However, to our knowledge, these deep learning approaches have not been extensively studied for auditory data. In this paper, we apply convolutional deep belief networks to audio data and empirically evaluate them on various audio classification tasks...