An exploration of dropout with RNNs for natural language inference.

Dropout is a crucial regularization technique for the Recurrent Neural Network (RNN) models of Natural Language Inference (NLI). However, dropout has not been evaluated for the effectiveness at different layers and dropout rates in NLI models. In this paper, we propose a novel RNN model for NLI and empirically evaluate the effect of applying dropout at different layers in the model. We also investigate the impact of varying dropout rates at these layers. Our empirical evaluation on a large (Stanford Natural Language Inference (SNLI)) and a small (SciTail) dataset suggest that dropout at each feed-forward connection severely affects the model accuracy at increasing dropout rates. We also show that regularizing the embedding layer is efficient for SNLI whereas regularizing the recurrent layer improves the accuracy for SciTail. Our model achieved an accuracy 86.14% on the SNLI dataset and 77.05% on SciTail

Evaluation and Forecasting of the Financial Performance of the Unions of Rural Cooperatives by a Decision Support System

The evaluation and forecasting of the performance of the Unions of Rural Cooperatives (URC) can be performed through the use of various types of financial ratios, such as ratios of efficiency, reliability and management. A computer decision support system (DSS) was designed and implemented for this purpose. The system evaluates and ranks the URC by applying principles of multicriteria analysis. Assigning various weights to the financial ratios enacts different scenarios. Actually each scenario calls for a different type of evaluation. The types of evaluation are determined by a variety of performance indicators. Actual financial data concerning the URC of north Greece for the last ten years was used as input to the DSS. The DSS applies fuzzy logic in order to forecast the future performance of each URC. The application of the system with original financial URC data and the use of a fuzzy forecasting method constitute the original contribution of the paper. The paper can be used in any country of the world without any revisions

EDITORIAL

This special issue of the International Journal on Artificial Intelligence Tools (IJAIT) hosts a selection of extended versions of papers presented in the 6th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2010). After a peer review process, a total of 48 papers authored by scientists from 16 countries were accepted and presented at the conference, which was held in Larnaca, Cyprus on the 6th and 7th of October, 2010. IFIP’s AIAI is an annual meeting that grows in significance every year, attracting researchers from different countries around the globe. It maintains high quality standards and welcomes research papers describing prototypes, innovative systems, tools and techniques of AI, as well as applications of AI in real-world problems

Editorial

This special issue of the International Journal on Artificial Intelligence Tools (IJAIT) hosts a selection of extended versions of papers presented in the 6th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI 2010). After a peer review process, a total of 48 papers authored by scientists from 16 countries were accepted and presented at the conference, which was held in Larnaca, Cyprus on the 6th and 7th of October, 2010. IFIP’s AIAI is an annual meeting that grows in significance every year, attracting researchers from different countries around the globe. It maintains high quality standards and welcomes research papers describing prototypes, innovative systems, tools and techniques of AI, as well as applications of AI in real-world problems

Brain-inspired computing and machine learning

Iliadis LS, Kurkova V, Hammer B. Brain-inspired computing and machine learning. NEURAL COMPUTING & APPLICATIONS. 2020

Extreme deep learning in biosecurity: the case of machine hearing for marine species identification

Biosafety is defined as a set of preventive measures aimed at reducing the risk of infectious diseases’ spread to crops and animals, by providing quarantine pesticides. Prolonged and sustained overheating of the sea, creates significant habitat losses, resulting in the proliferation and spread of invasive species, which invade foreign areas typically seeking colder climate. This is one of the most important modern threats to marine biosafety. The research effort presented herein, proposes an innovative approach for Marine Species Identification, by employing an advanced intelligent Machine Hearing Framework (MHF). The final target is the identification of invasive alien species (IAS) based on the sounds they produce. This classification attempt, can provide significant aid towards the protection of biodiversity, and can achieve overall regional biosecurity. Hearing recognition is performed by using the Online Sequential Multilayer Graph Regularized Extreme Learning Machine Autoencoder (MIGRATE_ELM). The MIGRATE_ELM uses an innovative Deep Learning algorithm (DELE) that is applied for the first time for the above purpose. The assignment of the corresponding class ‘native’ or ‘invasive’ in its locality, is carried out by an equally innovative approach entitled ‘Geo Location Country Based Service’ that has been proposed by our research team