A new Potential-Based Reward Shaping for Reinforcement Learning Agent

Potential-based reward shaping (PBRS) is a particular category of machine learning methods which aims to improve the learning speed of a reinforcement learning agent by extracting and utilizing extra knowledge while performing a task. There are two steps in the process of transfer learning: extracting knowledge from previously learned tasks and transferring that knowledge to use it in a target task. The latter step is well discussed in the literature with various methods being proposed for it, while the former has been explored less. With this in mind, the type of knowledge that is transmitted is very important and can lead to considerable improvement. Among the literature of both the transfer learning and the potential-based reward shaping, a subject that has never been addressed is the knowledge gathered during the learning process itself. In this paper, we presented a novel potential-based reward shaping method that attempted to extract knowledge from the learning process. The proposed method extracts knowledge from episodes' cumulative rewards. The proposed method has been evaluated in the Arcade learning environment and the results indicate an improvement in the learning process in both the single-task and the multi-task reinforcement learner agents

Word-level Persian Lipreading Dataset

Lip-reading has made impressive progress in recent years, driven by advances in deep learning. Nonetheless, the prerequisite such advances is a suitable dataset. This paper provides a new in-the-wild dataset for Persian word-level lipreading containing 244,000 videos from approximately 1,800 speakers. We evaluated the state-of-the-art method in this field and used a novel approach for word-level lip-reading. In this method, we used the AV-HuBERT model for feature extraction and obtained significantly better performance on our dataset

A Design Support System Using Analogy Based Reasoning

Abstract: This paper represents a procedure to support the designer in his/her process of mechanical system design, by inspiring the knowledge acquired from previous projects. To this end, the proposed method represents an appropriate means to capitalize the know-how of the professional experts. Based on this approach, an interactive programme is implemented, which assist designers in the specification of new products. The data structure of the implemented tool is based on the object oriented modelling. This structure allows several classifications of a same design, using different levels of abstraction. This approach enables designer to begin with a more general description of the product, and to refine the description by referring to similar data in the pattern bases

A fast image registration approach based on SIFT key-points applied to super-resolution, Imaging

Abstract An accurate image registration is a fundamental stage in many image processing problems. In this paper a new and fast registration approach based on Scale Invariant Feature Transform keypoints descriptors, under Euclidean transformation model is proposed. The core idea of the proposed method is estimation of rotation angle and vertical and horizontal shifts using averaging of differences of SIFT key-points pairs descriptors. The method is simple but requires some tuning modules for accurate estimation. Orientation modification and compensation and shift compensation are some of the proposed modules. The proposed method is fast, it is about 5 times faster than RANSAC method for model parameters estimation. The accuracy of the proposed method is compared with some popular registration methods. Various comparisons have been done with LIVE database images with known motion vectors. The experimental results show the high performance of the proposed algorithm in a super-resolution application

A Noisy Chaotic Neural Network for Solving Attributed Relational Graph Matching Problem

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