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A CatBoost Approach for Superconducting Material Prediction with AI

This study harnesses the CatBoost algorithm to predict transition temperatures (Tc) of superconducting materials, addressing challenges in dataset refinement and feature selection. Leveraging the Jabir and Soraya packages for generating atomic descriptors and selecting crucial features, the model achieved high accuracy with an R-squared (R2) of 0.952 and root mean square error (RMSE) of 6.45 K. Additionally, a novel web application for Tc prediction underscores the impactful synergy between AI and materials science.

23 Feb 2024

Preference Alignment Framework for Enhancing Multi-Modal Large Language Models

This research introduces a novel preference alignment framework to address performance degradation in multi-modal large language models (MLLMs) caused by visual instruction tuning. By leveraging preference data collected from a visual question answering dataset, the proposed method significantly improves the MLLM's instruction-following capabilities, surpassing the performance of the original language model on various benchmarks.

23 Feb 2024

VATr++: Advanced Few-Shot Styled Handwritten Text Generation

This study investigates the impact of visual and textual input on styled handwritten text generation (HTG) models, proposing strategies for input preparation and training regularization. The researchers extend the VATr architecture to VATr++, enhancing rare character generation and handwriting style capture. Additionally, they introduce a standardized evaluation protocol to facilitate fair comparisons and foster progress in the field of HTG.

23 Feb 2024

ML-based Prediction of Punching Shear Capacity in Ultra-High-Performance Concrete

Researchers investigated the feasibility of using machine learning (ML) models to predict the punching shear capacity of post-tensioned ultra-high-performance concrete (UHPC) flat slabs. By proposing correction factors based on finite element method-artificial intelligence (FEM-AI/ML) techniques, they extended the validity of punching shear capacity provisions in design codes like EC2 and ACI-318 to include PT-UHPC flat slabs.

23 Feb 2024

UK’s First Master’s Degree in Applying AI Tools to Engineering and Design is Open for Applications

The UK’s first master’s degree course focused on applying skills in AI to engineering and design is to begin this year at the University of Bath.

23 Feb 2024

Enhancing Few-Shot Semantic Segmentation with Transductive Meta-Learning

Researchers propose a novel approach for few-shot semantic segmentation, leveraging an ensemble of visual features learned from pre-trained classification and semantic segmentation networks. Their method utilizes a two-pass strategy, employing transductive meta-learning to improve prediction accuracy and mitigate false positives. Experimental results demonstrate significant performance improvements, achieving state-of-the-art results on benchmark datasets with minimal trainable parameters.

22 Feb 2024

ToolSword: Addressing Safety Concerns in Large Language Models for Tool Learning

Researchers introduced ToolSword, a framework designed to address safety challenges encountered by large language models (LLMs) during tool learning processes. By identifying and examining six safety scenarios spanning input, execution, and output stages, ToolSword comprehensively evaluated LLMs' ability to handle harmful queries, risky tools, and detrimental feedback. Experiments on 11 LLMs revealed persistent safety risks, emphasizing the urgent need for robust safety measures in practical applications of tool learning.

22 Feb 2024

Harnessing Machine Learning for Predicting School Dropout

Researchers utilized various machine learning algorithms to develop predictive models for identifying students at risk of dropping out of secondary and higher education in Mexico. Leveraging demographic, socioeconomic, and educational data, the study demonstrated the effectiveness of artificial neural networks (ANN) in achieving high reliability (99%) in predicting school dropout, highlighting key variables such as school attendance, type, location, occupation, income, and marital status.

22 Feb 2024

AutoGPT+P: Integrating Affordance-Based Scene Representation with LLMs for Robotic Task Planning

Researchers proposed AutoGPT+P, a novel system for robotic task planning that merges an affordance-based scene representation with large language models (LLMs). By combining visual data-based scene affordance extraction with LLMs' capabilities, AutoGPT+P enables robots to understand natural language commands, perceive their environment, and generate plans to fulfill user requests, even in scenarios where required objects are absent.

22 Feb 2024

LONGHEADS: Enhancing Large Language Models' Capability for Processing Long Contexts

Researchers introduced LONGHEADS, a training-free framework aimed at improving the effectiveness of large language models (LLMs) in handling long contexts. By strategically dividing input texts into chunks and allowing each attention head to focus on important segments, LONGHEADS addressed limitations in attention windows and computational demands, showcasing superior performance on various natural language processing tasks without additional training. The framework's query-aware chunk selection strategy and efficient utilization of attention heads demonstrated promise in enhancing LLMs' abilities for processing lengthy inputs.

21 Feb 2024

Ensemble Learning for Botnet Detection to Enhance IoT Security

Researchers proposed a novel intrusion detection system (IDS) leveraging ensemble learning and deep neural networks (DNNs) to combat botnet attacks on Internet of Things (IoT) devices. By training device-specific DNN models on heterogeneous IoT data and aggregating predictions through ensemble averaging, the system achieved remarkable accuracy and effectively detected botnet activities. The study's structured methodology, comprehensive evaluation metrics, and ensemble approach offer promise in bolstering IoT security against evolving cyber threats.

21 Feb 2024

A Novel Approach for Edge Unlearning in Graph Neural Networks

Researchers introduced Unlink to Unlearn (UtU), a novel method designed to simplify edge unlearning in graph neural networks (GNNs) and address the issue of over-forgetting. UtU facilitates unlearning by unlinking forget edges from the original graph structure, effectively preserving privacy protection capabilities and link prediction accuracy while minimizing computational overhead. Experimental evaluation across real-world datasets demonstrated UtU's efficacy in unlearning, maintaining high accuracy in downstream tasks, and effectively preventing over-forgetting, making it a practical and lightweight solution for edge unlearning in GNNs.

21 Feb 2024

Optimizing Content Caching in Vehicular Networks: A Gale–Shapley Approach

Researchers proposed a novel content caching scheme for fog nodes in 5G-enabled vehicular networks, utilizing a modified Gale–Shapley algorithm to optimize content placement based on the preferences of content providers and vehicles. Their approach effectively reduced content downloading time, improved quality of service, and ensured fair and efficient content placement by considering factors such as content popularity, vehicle connectivity, and channel quality.

21 Feb 2024

Urban Park Management Through Social Media and Deep Learning

Researchers propose a groundbreaking framework utilizing social media data and deep learning techniques to assess urban park management effectively. By analyzing visitor comments on seven parks in Wuhan City, the study evaluated various management aspects and identified improvement suggestions, demonstrating the potential of this approach to enhance park service quality and management efficiency. The framework's dynamic visualization capabilities and scalability make it a valuable tool for improving public spaces and contributing to the development of smart cities, with opportunities for expansion to other urban areas and data sources in future research.

20 Feb 2024

Speech-Based Classification of Parkinson's Disease and Essential Tremor: A Gaussian Mixture Models Approach

This paper addresses the diagnostic challenges of distinguishing between Parkinson’s disease (PD) and essential tremor (ET) by proposing a Gaussian mixture models (GMMs) method for speech assessment. By adapting speech analysis technology to Czech and employing machine learning techniques, the study demonstrates promising accuracy in classifying PD and ET patients, highlighting the potential of automated speech analysis as a robust diagnostic tool for movement disorders.

20 Feb 2024

NLP-based Quantification of Low-Carbon Policies in China's Manufacturing Sector

This article presents a novel method for quantifying low-carbon policies in China's manufacturing industries, addressing previous deficiencies in direct measurement. By constructing a comprehensive low-carbon policy intensity index and utilizing innovative natural language processing techniques, researchers provided valuable insights into policy quantification and its impact. The resulting dataset, comprising 7282 policies, offers multidisciplinary researchers a robust foundation for analyzing the effectiveness of low-carbon policies in China's manufacturing sector.

20 Feb 2024

Automated Dimension Reduction in Archaeometry Using Autoencoder Neural Networks

The article discusses the application of autoencoder neural networks in archaeometry, specifically in reducing the dimensions of X-ray fluorescence spectra for analyzing cultural heritage objects. Researchers utilized autoencoders to compress data and extract essential features, facilitating efficient analysis of elemental composition in painted materials. Results demonstrated the effectiveness of this approach in attributing paintings to different creation periods based on pigment composition, highlighting its potential for automating and enhancing archaeological analyses.

20 Feb 2024

LabVantage Introduces a Digitally Native Ecosystem to Boost Speed and Success in R&D Laboratories

LabVantage Solutions, Inc., the leading provider of laboratory informatics solutions and services, including purpose-built LIMS solutions that allow labs to go live faster and at a lower total cost, has made available its semantic search (AILANI), advanced analytics, and LIMS solutions in an integrated, digitally native ecosystem capable of seamlessly supporting R&D laboratory processes.

19 Feb 2024

Lightweight Enhancements in YOLOv5 for Vehicle Detection

Researchers introduce a lightweight enhancement to the YOLOv5 algorithm for vehicle detection, integrating integrated perceptual attention (IPA) and multiscale spatial channel reconstruction (MSCCR) modules. The method reduces model parameters while boosting accuracy, making it optimal for intelligent traffic management systems. Experimental results showcase superior performance compared to existing algorithms, promising advancements in efficiency and functionality for vehicle detection in diverse traffic environments.

18 Feb 2024

WiFi Wonders: Revolutionizing Human Presence Perception in IoT

Researchers introduce a cutting-edge method utilizing WiFi technology for non-contact human presence perception in IoT, achieving remarkable precision levels of up to 99%. Through preprocessing, feature extraction, and classification, the system demonstrates robustness and adaptability, offering promising applications in smart homes and healthcare. Despite environmental influences, the method maintains stability, heralding a significant advancement in human presence detection technology.

18 Feb 2024

AI News

A CatBoost Approach for Superconducting Material Prediction with AI

Preference Alignment Framework for Enhancing Multi-Modal Large Language Models

VATr++: Advanced Few-Shot Styled Handwritten Text Generation

ML-based Prediction of Punching Shear Capacity in Ultra-High-Performance Concrete

UK’s First Master’s Degree in Applying AI Tools to Engineering and Design is Open for Applications

Enhancing Few-Shot Semantic Segmentation with Transductive Meta-Learning

ToolSword: Addressing Safety Concerns in Large Language Models for Tool Learning

Harnessing Machine Learning for Predicting School Dropout

AutoGPT+P: Integrating Affordance-Based Scene Representation with LLMs for Robotic Task Planning

LONGHEADS: Enhancing Large Language Models' Capability for Processing Long Contexts

Ensemble Learning for Botnet Detection to Enhance IoT Security

A Novel Approach for Edge Unlearning in Graph Neural Networks

Optimizing Content Caching in Vehicular Networks: A Gale–Shapley Approach

Urban Park Management Through Social Media and Deep Learning

Speech-Based Classification of Parkinson's Disease and Essential Tremor: A Gaussian Mixture Models Approach

NLP-based Quantification of Low-Carbon Policies in China's Manufacturing Sector

Automated Dimension Reduction in Archaeometry Using Autoencoder Neural Networks

LabVantage Introduces a Digitally Native Ecosystem to Boost Speed and Success in R&D Laboratories

Lightweight Enhancements in YOLOv5 for Vehicle Detection

WiFi Wonders: Revolutionizing Human Presence Perception in IoT

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