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Results for "Timothy Shaw"

TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection

arXiv ·

Researchers introduce TII-SSRC-23, a new network intrusion detection dataset designed to improve the diversity and representation of modern network traffic for machine learning models. The dataset includes a range of traffic types and subtypes to address the limitations of existing datasets. Feature importance analysis and baseline experiments for supervised and unsupervised intrusion detection are also provided.

Sovereign AI: Rethinking Autonomy in the Age of Global Interdependence

arXiv ·

This paper proposes a framework for understanding AI sovereignty as a balance between autonomy and interdependence, considering global data, supply chains, and standards. It introduces a planner's model with policy heuristics for equalizing marginal returns across sovereignty pillars and setting openness. The model is applied to India and the Middle East (Saudi Arabia and UAE), finding that managed interdependence, rather than isolation, is key for AI sovereignty.

Machine Learning Risk Intelligence for Green Hydrogen Investment: Insights for Duqm R3 Auction

arXiv ·

This paper introduces an AI-driven decision support system for green hydrogen investment in Oman, specifically for the Duqm R3 auction. The system uses publicly available meteorological data to predict maintenance pressure on hydrogen infrastructure, creating a Maintenance Pressure Index (MPI). This tool supports regulatory oversight and operational decision-making by enabling temporal benchmarking against performance claims.

Smart Waste Management System for Makkah City using Artificial Intelligence and Internet of Things

arXiv ·

A research paper proposes a smart waste management system called TUHR for Makkah, Saudi Arabia, leveraging IoT and AI to handle waste accumulation during the annual pilgrimage. The system uses ultrasonic sensors to monitor waste levels and gas detectors to identify harmful substances, alerting authorities when containers are full or hazards are detected. The proposed system aligns with Saudi Vision 2030 by promoting sustainability and improving public health through optimized waste management.

Alumni Spotlight: How Abdelrahman Shaker learned to redefine impact in AI

MBZUAI ·

MBZUAI alumnus Abdelrahman Shaker discusses his evolving perspective on impactful AI research, shifting from publication counts to real-world usefulness. He highlights the success of his SwiftFormer and EdgeNext papers, which have been adopted by third parties and reached millions of users. Shaker chose MBZUAI for its faculty expertise, which led to 10 publications and over 2,500 citations during his Ph.D.

DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information

arXiv ·

This paper introduces DaringFed, a novel dynamic Bayesian persuasion pricing mechanism for online federated learning (OFL) that addresses the challenge of two-sided incomplete information (TII) regarding resources. It formulates the interaction between the server and clients as a dynamic signaling and pricing allocation problem within a Bayesian persuasion game, demonstrating the existence of a unique Bayesian persuasion Nash equilibrium. Evaluations on real and synthetic datasets demonstrate that DaringFed optimizes accuracy and convergence speed and improves the server's utility.

Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing

arXiv ·

The paper introduces TimeHUT, a new method for learning time-series representations using hierarchical uniformity-tolerance balancing of contrastive representations. TimeHUT employs a hierarchical setup to learn both instance-wise and temporal information, along with a temperature scheduler to balance uniformity and tolerance. The method was evaluated on UCR, UAE, Yahoo, and KPI datasets, demonstrating superior performance in classification tasks and competitive results in anomaly detection.