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Arxiv
2d
241
Image Credit: Arxiv
A Methodology to extract Geo-Referenced Standard Routes from AIS Data
- This study proposes a methodology to analyze route between maritime points of interest and extract geo-referenced standard routes from raw AIS data.
- The methodology involves segmenting AIS data into distinct routes using a finite state machine (FSM) and aggregating the segments based on departure and destination ports.
- Iterative density-based clustering is used to model the routes, with clustering parameters assigned manually and extended to the entire dataset using linear regression.
- The unsupervised approach has been tested on a six-year AIS dataset covering the Arctic region and the Europe, Middle East, North Africa areas, proving effective in extracting standard routes with less than 5% outliers.
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Arxiv
2d
249
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Cyborg Data: Merging Human with AI Generated Training Data
- Automated scoring systems used in large-scale assessment traditionally require a large quantity of hand-scored data for accurate predictions.
- Generative Large Language Models can generalize to new tasks with little to no data but still need fine-tuning.
- The proposed model distillation pipeline, named 'Cyborg Data', combines human and machine-scored responses in training.
- Student models trained on 'Cyborg Data' achieve performance similar to training on the entire dataset, using only 10% of the original hand-scored data.
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Arxiv
2d
256
Image Credit: Arxiv
ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning
- ShieldAgent is a guardrail agent designed to enforce safety policy compliance for other autonomous agents.
- It constructs a safety policy model by extracting verifiable rules from policy documents and generates a shielding plan.
- ShieldAgent-Bench, a dataset with 3K safety-related pairs of agent instructions and action trajectories, is introduced.
- Experiments show that ShieldAgent outperforms prior methods, achieving high precision and efficiency in safeguarding agents.
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Arxiv
2d
347
Image Credit: Arxiv
CSPO: Cross-Market Synergistic Stock Price Movement Forecasting with Pseudo-volatility Optimization
- The stock market presents challenges in forecasting stock price movements in quantitative finance.
- The CSPO framework introduces an effective deep neural architecture to leverage external futures knowledge and enhance predictive capability.
- CSPO incorporates pseudo-volatility to model stock-specific forecasting confidence, improving accuracy and robustness.
- Extensive experiments demonstrate CSPO's superior performance over existing methods and effectiveness of proposed modules.
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Arxiv
2d
355
Image Credit: Arxiv
Adaptive Integrated Layered Attention (AILA)
- Adaptive Integrated Layered Attention (AILA) is a neural network architecture that combines dense skip connections with different mechanisms for adaptive feature reuse across network layers.
- AILA has been evaluated on price forecasting, image recognition, and sentiment analysis tasks, achieving comparable performance to strong deep learning baselines.
- Two versions of AILA have been implemented - AILA-Architecture 1 and AILA-Architecture 2, which differ in their connection mechanisms between layers.
- Results show that AILA's adaptive inter-layer connections improve overall performance for various tasks, with reduced training and inference time.
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Arxiv
2d
359
Image Credit: Arxiv
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection
- State-space modeling has emerged as a powerful paradigm for sequence analysis in various tasks.
- The proposed Adaptive State-Space Mamba (ASSM) framework is designed for real-time sensor data anomaly detection.
- The framework leverages sequential hidden states and introduces an adaptive gating mechanism for efficient and scalable detection.
- Extensive experiments demonstrate superior performance compared to existing baselines.
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Arxiv
2d
375
Image Credit: Arxiv
LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence
- Demand is spiking in industrial fields for multidisciplinary forecasting, where a broad spectrum of sectors needs planning and forecasts to streamline intelligent business management.
- LeForecast is an enterprise intelligence platform tailored for time series tasks, integrating advanced interpretations of time series data and multi-source information.
- It includes a three-pillar modelling engine combining a large foundation model (Le-TSFM), multimodal model, and hybrid model to drive optimization across multiple sectors in enterprise operations.
- Experimental results verify the efficiency of the platform, making it a profound and practical solution for time series forecasting in various industry use cases.
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Arxiv
2d
379
Image Credit: Arxiv
Ignite Forecasting with SPARK: An Efficient Generative Framework for Refining LLMs in Temporal Knowledge Graph Forecasting
- Ignite Forecasting with SPARK: An Efficient Generative Framework for Refining LLMs in Temporal Knowledge Graph Forecasting
- Temporal Knowledge Graph (TKG) forecasting is crucial for predicting future events using historical data.
- SPARK is a Sequence-level Proxy-Adapting framework for Refining LLMs in TKG forecasting.
- SPARK offers a cost-effective, plug-and-play solution for efficient TKG forecasting by utilizing beam search and traditional TKG models.
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Arxiv
2d
383
Image Credit: Arxiv
Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
- Adaptive Clipping for Privacy-Preserving Few-Shot Learning: Enhancing Generalization with Limited Data
- A new approach called Meta-Clip is introduced for enhancing the utility of privacy-preserving few-shot learning methods.
- The Adaptive Clipping method dynamically adjusts clipping thresholds during training to balance data privacy preservation with learning capacity maximization.
- Experiments demonstrate the effectiveness of Adaptive Clipping in minimizing utility degradation and improving generalization performance.
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Arxiv
2d
79
Image Credit: Arxiv
Advancing Spatiotemporal Prediction using Artificial Intelligence: Extending the Framework of Geographically and Temporally Weighted Neural Network (GTWNN) for Differing Geographical and Temporal Contexts
- This paper proposes an extension to the Geographically and Temporally Weighted Neural Network (GTWNN) framework for spatio-temporal prediction.
- The authors formulate a novel semi-analytical approach to solving Geographically and Temporally Weighted Regression (GTWR) and apply it to London crime data.
- The results demonstrate high-accuracy predictive evaluation scores, validating the assumptions and approximations in the approach.
- The study highlights the importance of considering specific geographic and temporal characteristics when selecting modeling strategies for improved accuracy and suitability.
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Arxiv
2d
83
Image Credit: Arxiv
From Individual to Group: Developing a Context-Aware Multi-Criteria Group Recommender System
- Group decision-making is becoming increasingly common in various domains.
- Conventional recommender systems are not effective in group settings due to their limitations.
- A Context-Aware Multi-Criteria Group Recommender System (CA-MCGRS) is developed to address these challenges.
- CA-MCGRS outperforms other approaches in improving group recommendations by integrating context and multi-criteria evaluations.
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Arxiv
2d
86
Image Credit: Arxiv
Combating the Bullwhip Effect in Rival Online Food Delivery Platforms Using Deep Learning
- The wastage of perishable items has led to significant health and economic crises, increasing business uncertainty and fluctuating customer demand.
- Accurate demand forecasting helps stabilize inventory, optimize supplier orders, and reduce waste.
- A Third-Party Logistics (3PL) supply chain model involving restaurants, online food apps, and customers is presented.
- A deep learning-based demand forecasting model using a two-phase Long Short-Term Memory (LSTM) network is proposed to combat the bullwhip effect in online food delivery platforms.
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Arxiv
2d
205
Image Credit: Arxiv
The Cost of Local and Global Fairness in Federated Learning
- With the emerging application of Federated Learning (FL) in finance, hiring, and healthcare, fairness is crucial to prevent disparities across legally protected attributes like race or gender.
- Global fairness addresses the disparity across the entire population, while local fairness focuses on the disparity within each client.
- This paper introduces a framework that investigates the minimum accuracy lost for enforcing specified levels of global and local fairness in multi-class FL settings.
- Experimental results show that the proposed algorithm outperforms the current state of the art in terms of accuracy-fairness tradeoffs, computational costs, and communication costs.
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Arxiv
2d
324
Image Credit: Arxiv
GroundHog: Revolutionizing GLDAS Groundwater Storage Downscaling for Enhanced Recharge Estimation in Bangladesh
- Long-term groundwater level (GWL) measurement is vital for effective policymaking and recharge estimation.
- A Machine Learning model is developed to mitigate data gaps and accurately predict maximum and minimum GWL.
- An Upsampling Model is trained to produce high-resolution GWLs using low-resolution GLDAS data as input.
- The approach successfully upscales GLDAS data, allowing high-resolution recharge estimations and proactive resource management.
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Arxiv
2d
339
Image Credit: Arxiv
Invariant Control Strategies for Active Flow Control using Graph Neural Networks
- Reinforcement learning (RL) has shown potential in learning complex control strategies for active flow control tasks.
- However, RL applications in turbulent flows are computationally challenging and have limited generalization capabilities.
- To address these limitations, this work proposes the use of graph neural networks (GNNs) for active flow control.
- The results demonstrate that GNN-based control policies achieve comparable performance and improved generalization properties.
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