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With the rise of GenAI, the demand for data has increased dramatically, making it more valuable than ever. In the current digital era, website owners face the significant challenge of keeping their data safe from AI bots scraping their content without permission.  AI companies often use content from public websites to train their large language models (LLMs). While some larger companies such as Google and OpenAI offer website operators to opt out of scraping, not all LLM developers are that transparent. This issue of web scrapping was highlighted a few months ago when Reddit struck a $60m deal with Google…

[Submitted on 4 Jul 2024] View a PDF of the paper titled Deep learning architectures for data-driven damage detection in nonlinear dynamic systems, by Harrish Joseph and 3 other authors View PDF HTML (experimental) Abstract:The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. The in-depth investigation in the present work addresses deep learning applied to data-driven damage detection in nonlinear dynamic systems. In particular, autoencoders (AEs) and generative adversarial networks (GANs) are implemented leveraging on 1D convolutional neural networks. The onset of damage is detected in the investigated nonlinear…

[Submitted on 3 Jul 2024] View a PDF of the paper titled Iris and Palmprint Multimodal Biometric Recognition using Novel Preactivated Inverted ResNet and Hybrid Metaheuristic Optimized DenseNet, by Indu Singh and 4 other authors View PDF HTML (experimental) Abstract:Biometric recognition technology has witnessed widespread integration into daily life due to the growing emphasis on information security. In this domain, multimodal biometrics, which combines multiple biometric traits, has overcome limitations found in unimodal systems like susceptibility to spoof attacks or failure to adapt to changes over time. This paper proposes a novel multimodal biometric recognition system that utilizes deep learning…

arXiv:2407.03621v1 Announce Type: new Abstract: Large Language Models (LLMs) have an unrivaled and invaluable ability to "align" their output to a diverse range of human preferences, by mirroring them in the text they generate. The internal characteristics of such models, however, remain largely opaque. This work presents the Injectable Realignment Model (IRM) as a novel approach to language model interpretability and explainability. Inspired by earlier work on Neural Programming Interfaces, we construct and train a small network -- the IRM -- to induce emotion-based alignments within a 7B parameter LLM architecture. The IRM outputs are injected via layerwise addition at…