Search

In this study, we present a transfer learning method for gesture classification via an inductive and supervised transductive approach with an electromyographic dataset gathered via the Myo armband. A ternary gesture classification problem is presented by states of ’thumbs up’, ’thumbs down’, and ’relax’in order to communicate in the affirmative or negative in a non-verbal fashion to a machine. Of the nine statistical learning paradigms benchmarked over 10-fold cross validation (with three methods of feature selection), an ensemble of Random Forest and Support Vector Machine through voting achieves the best score of 91.74% with a rule-based feature selection method. When new subjects are considered, this machine learning approach fails to generalise new data, and thus the processes of Inductive and Supervised Transductive Transfer Learning are introduced with a short calibration exercise (15 s). Failure of generalisation shows that 5 s of data per-class is the strongest for classification (versus one through seven seconds) with only an accuracy of 55%, but when a short 5 s per class calibration task is introduced via the suggested transfer method, a Random Forest can then classify unseen data from the calibrated subject at an accuracy of around 97%, outperforming the 83% accuracy boasted by the proprietary Myo system. Finally, a preliminary application is presented through social interaction with a humanoid Pepper robot, where the use of our approach and a most-common-class metaclassifier achieves 100% accuracy for all trials of a ‘20 Questions’ game.

In this work we present the Chatbot Interaction with Artificial Intelligence (CI-AI) framework as an approach to the training of a transformer based chatbot-like architecture for task classification with a focus on natural human interaction with a machine as opposed to interfaces, code, or formal commands. The intelligent system augments human-sourced data via artificial paraphrasing in order to generate a large set of training data for further classical, attention, and language transformation-based learning approaches for Natural Language Processing (NLP). Human beings are asked to paraphrase commands and questions for task identification for further execution of algorithms as skills. The commands and questions are split into training and validation sets. A total of 483 responses were recorded. Secondly, the training set is paraphrased by the T5 model in order to augment it with further data. Seven state-of-the-art transformer-based text classification algorithms (BERT, DistilBERT, RoBERTa, DistilRoBERTa, XLM, XLM-RoBERTa, and XLNet) are benchmarked for both sets after fine-tuning on the training data for two epochs. We find that all models are improved when training data is augmented by the T5 model, with an average increase of classification accuracy by 4.01%. The best result was the RoBERTa model trained on T5 augmented data which achieved 98.96% classification accuracy. Finally, we found that an ensemble of the five best-performing transformer models via Logistic Regression of output label predictions led to an accuracy of 99.59% on the dataset of human responses. A highly-performing model allows the intelligent system to interpret human commands at the social-interaction level through a chatbot-like interface (e.g. “Robot, can we have a conversation?”) and allows for better accessibility to AI by non-technical users.

Automated computer generation of aesthetically pleasing artwork has been the subject of research for several decades. The unsolved problem of interest is how to please any audience without requiring too much of their involvement in the process of creation. Two-dimensional pictures have received a lot of attention; however, 3D artwork has remained relatively unexplored. This paper showcases an extended version of the Axial Generation Process (AGP), a versatile generation algorithm that can create both 2D and 3D items within the Concretism art style. The extensions presented here include calculating colour values for the artwork, increasing the range of forms that can be created through dynamic sizing of shapes and including more primitive shape types, finally, 2D items can be created from multiple viewpoints. Both 2D and 3D items generated through the AGP were evaluated against a set of formal aesthetic measures and compared against two established generation systems, one based on manipulating pixels/voxels and another tracking the path of particles through 2D and 3D space. This initial evaluation shows that the process is capable of generating visually varied items which exhibit a generally diverse range of values across the measures used, in both two and three dimensions. Comparatively, against the established generation processes, the AGP shows a good balance of performance and ability to create complex and visually varied items.

One dominant feature of the modern manufacturing chains is the movement of goods. Manufacturing companies would remain an unprofitable investment if the supplies/logistics of raw materials, semi-finished products or final goods are not handled in an effective way.

This paper investigates the relationship between code growth and problem difficulty in genetic programming. The symbolic regression problem domain is used to investigate this relationship using two different types of increased instance difficulty. Results are supported by a simplified model of genetic programming and show that increased difficulty induces higher selection pressure and less genetic diversity, which both contribute toward an increased rate of code growth.

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

The rapid growth of program code is an important problem in genetic programming systems. In the present paper we investigate a selection scheme based on multiobjective optimization. Since we want to obtain accurateand smallsolutions, we reformulate this problem as multiobjective optimization. We show that selection based on the Pareto nondomination criterion reduces code growth and processing time without significant loss of solution accuracy.