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Meta-Learning: Why it's a big deal, it's future for foundation models, and how to improve it | by Devansh | Nov, 2023

Ever since BERT and other large deep-learning models started becoming accessible to the mainstream, we’ve seen more and more companies shift to utilizing these large proven ‘foundation models’ as a… ....

Autoregressive Llms , Sam Altman , Foundation Models , Oriented University , Meta Learning , Transfer Learning , Fine Tuning , Retrieval Augmented Generation , Neural Architecture Search , Based Evolution Optimizes , Population Based Meta Learning , Machine Learning Agents , Machine Learning , Cancer Detection , Medically Oriented , Because Cancer , Medical Data , Computer Vision Classification , Computer Vision Classifier , Tabular Classifiers , Meta Learnings Cost Problem , Evolutionary Algorithms , Evolutionary Genomes , Single Genome , Random Drift , Sparse Weight Activation Training ,

"An edge-aided parallel evolutionary privacy-preserving algorithm for I" by Akbar Telikani, Asadollah Shahbahrami et al.

Data sanitization in the context of Internet of Things (IoT) privacy refers to the process of permanently and irreversibly hiding all sensitive information from vast amounts of streaming data. Taking into account the dynamic and real-time characteristics of streaming IoT data, we propose a parallel evolutionary Privacy-Preserving Data Mining (PPDM), called High-performance Evolutionary Data Sanitization for IoT (HEDS4IoT), and implement two mechanisms on a Graphics Processing Units (GPU)-aided parallelized platform to achieve real-time streaming protected data transmission. The first mechanism, the Parallel Indexing Engine (PIE), generates retrieval index lists from the dataset using GPU blocks. These lists are used in place of the dataset during the PPDM process. The second mechanism, called Parallel Fitness Function Engine (PF2E), parallelizes the index lists on the GPU threads to speed up the computation of the quality of solutions generated by the evolutionary algorithm, in which d ....

Privacy Preserving Data Mining , High Performance Evolutionary Data Sanitization , Graphics Processing Units , Parallel Indexing Engine , Parallel Fitness Function Engine , Big Data , Data Privacy , Edge Computing , Evolutionary Algorithms , Pu Platform , Internet Of Things Iot ,

Compilers For The Future - Adam

How can we future-proof our programming languages? ....

Mcdonald Mclambda , Factorial Program , One True Way , Future Proof Compilers , Proposed Architecture , Evolutionary Algorithms ,

"Pumps-as-Turbines' (PaTs) performance prediction improvement using evo" by Akbar Telikani, Mosé Rossi et al.

Energy production from clean sources is mandatory to reduce pollutant emissions. Among different options for hidden hydropower potential exploitation, Pump-as-Turbine (PaT) represents a viable solution in pico- and micro-hydropower applications for its flexibility and low-cost. Pumps are widely available in the global market in terms of both sizes and spare parts. To date, there are several PaTs’ performance prediction models in the literature, but very few of them use optimization algorithms and only for specific and limited prediction goals. The present work proposes evolutionary Artificial Neural Networks (ANNs) based on JADE, which is a typology of differential evolution algorithm, to forecast Best Efficiency Point (BEP) and performance curves of a PaT starting from the pump operational data. In this model, JADE is employed as optimizer of basic ANNs to upgrade parameter values of the learning rate, weights, and biases. The accuracy of the proposed model is evaluated through expe ....

Artificial Neural Networks Anns , Artificial Neural Networks , Best Efficiency Point , Adaptive Differential Evolution , Artificial Neural Network , Evolutionary Algorithms , Idden Hydropower , Performance Forecast , Umps As Turbines ,

"Industrial IoT intrusion detection via evolutionary cost-sensitive lea" by Akbar Telikani, Jun Shen et al.

Cyber-attacks and intrusions have become the major obstacles to the adoption of the Industrial Internet of Things (IIoT) in critical industries. Imbalanced data distribution is a common problem in IIoT environments that negatively influence machine learning-based intrusion detection systems. To address this issue, we introduce EvolCostDeep, a hybrid model of stacked auto-encoders (SAE) and convolutional neural networks (CNNs) with a new cost-dependent loss function. The loss function aims to optimize the model’s parameters, where the costs are determined using an evolutionary algorithm. The combination of evolutionary algorithms and deep learning on Big data hinders the scalability of IIoT intrusion detection systems. In this regard, a fog computing-enabled framework, called DeepIDSFog, is designed at the data level, where the master node shares the EvolCostDeep model with worker nodes. In each fog worker node, the EvolCostDeep is parallelized through one task-level and two model-lev ....

Industrial Internet , Class Imbalance , Computational Modeling , Convolutional Neural Networks , Cost Sensitive Learning , Deep Learning , Edge Computing , Evolutionary Algorithms , Fog Computing , Industrial Internet Of Things , Industrial Internet Of Things Iiot , Intrusion Detection ,