arXiv subjects

Chanuka Algama

Publications and source records attributed to Chanuka Algama.

5 recordsLinked to original sources

arXiv2026

Residential Electricity Consumption Dataset for Sri Lanka (RECON-SL)

This article describes the Residential Electricity Consumption Dataset for Sri Lanka (RECON-SL), a multi-source resource that integrates utility records, high-frequency smart meter data, and household surveys from the service area of Lanka Electricity Company (LECO) in Sri Lanka. The dataset captures electricity use for 4,063 households, collected over the period October 2022 to January 2025, with coverage at monthly, 6-hour, and 15-minute intervals. Monthly billing-cycle data provide complete coverage across all households, while smart meter data are available for a subsample of 1,438 households, offering both fine-grained 15-minute readings and coarser 6-hour records. Three survey waves complement these consumption measures, collecting information on demographics, housing, appliance ownership, and perceptions of energy security. Across its components, the dataset includes more than 50 million meter readings and detailed survey responses from over 11,000 household interviews. As the first dataset of its kind in Sri Lanka, and among the few worldwide to link smart meter records with longitudinal survey data at this scale, it provides a unique resource for energy, machine learning and policy research, with known gaps in smart meter coverage documented for users. The dataset can support diverse applications, including electricity load forecasting, socio-economic and behavioral energy research, distribution planning, and policy analysis in energy affordability and equity. Access is provided through an open repository, with accompanying documentation and scripts to facilitate reuse.

cs.DL↗

arXiv2026

Decision-Centered Evaluation of Machine Learning Poverty Maps Using Mobile Phone and Satellite Data

Identifying the poorest communities is essential for poverty alleviation, but household surveys and censuses are costly and infrequent. Machine learning offers alternative poverty estimates from mobile phone and satellite data, yet average prediction accuracy alone does not show whether maps support targeting under limited budgets. We apply a decision-centered evaluation to 13,985 Grama Niladhari divisions in Sri Lanka, combining call detail records (CDRs), remote sensing (RS), and CNN-derived Landsat 8 embeddings. We assess recovery of the poorest administrative units, compare random and spatially grouped validation, and examine errors in socioeconomically atypical communities. Against a census-derived asset index (PC1), Random Forest achieves Recall@25\% of 0.830, compared with 0.450 for nighttime lights. Random splitting raises recall by 4.1 percentage points relative to Divisional Secretariat Division (DSD)-grouped holdouts. The combined model recovers 86\% of the 25 poorest DSDs by PC1, versus 69\% for RS-only and 67\% for CDR-only models. Spatially isolated divisions have 10.8\% higher prediction error. Stronger isolation--error association in RS-only models is consistent with spatial smoothing, although its cause remains unconfirmed. These findings support evaluating poverty maps by targeting performance and geographic transfer, while recognising that agreement with an asset index does not establish consumption-poverty accuracy.

cs.CY↗

arXiv2026

Robust Optical Flow Computation: A Higher-Order Differential Approach

In the domain of computer vision, optical flow stands as a cornerstone for unraveling dynamic visual scenes. However, the challenge of accurately estimating optical flow under conditions of large nonlinear motion patterns remains an open question. The image flow constraint is vulnerable to substantial displacements, and rapid spatial transformations. Inaccurate approximations inherent in numerical differentiation techniques can further amplify such intricacies. In response, this research proposes an innovative algorithm for optical flow computation, utilizing the higher precision of second-order Taylor series approximation within the differential estimation framework. By embracing this mathematical underpinning, the research seeks to extract more information about the behavior of the function under complex real-world scenarios and estimate the motion of areas with a lack of texture. An impressive showcase of the algorithm's capabilities emerges through its performance on renowned optical flow benchmarks such as KITTI (2015) and Middlebury. The average endpoint error (AEE), which computes the Euclidian distance between the calculated flow field and the ground truth flow field, stands notably diminished, validating the effectiveness of the algorithm in handling complex motion patterns.

cs.CV↗

arXiv2026

SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area

We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000,000 location records capturing complete, noise-free, multi-modality trajectories of 500,000 simulated agents observed at a 1Hz frequency navigating the San Francisco Bay Area network over a 70-day period. The data captures (1) needs-driven daily agendas of individual agents generated by an agent-based simulation of human patterns of life and (2) detailed kinematic trajectories moving agents across the OpenStreetMap representation of San Francisco using data from 40+ transit agencies across 9 counties. SF-LIFE provides unprecedented scale and detail as trajectories are based on real transit infrastructure using San Francisco General Transit Feed Specification (GTFS) data, having agent movements across multiple modalities, including bus, rail, bike, automobile, and walking. For this high-fidelity simulated representation of San Francisco, we provide (1) the full trajectory data annotated with transportation mode labels, (2) reduced-size versions of the trajectory data with reduced temporal frequency, (3) agent activity information describing the causal activity why an agent visits a place, (4) agent demographic data, and (5) the underlying OSM road network and building data. As the first dataset of its scale and level of detail, SF-LIFE overcomes the privacy, noise, and completeness limitations inherent in real-world tracking data, providing a robust and ethically sourced resource for research in transit optimization, human mobility analysis, and urban computing.

physics.soc-ph↗

arXiv2024

A Review on Message Complexity of the Algorithms for Clock Synchronization in Distributed Systems

In this work, we present an extensive analysis of clock synchronization algorithms, with a specific focus on message complexity. We begin by introducing fundamental concepts in clock synchronization, such as the Byzantine generals problem and specific concepts like clock accuracy, precision, skew, offset, timestamping, and clock drift estimation. Describing the concept of logical clocks, their implementation in distributed systems is discussed, highlighting their significance and various approaches. The paper then examines four prominent clock synchronization algorithms: Lamport's Algorithm, Ricart-Agrawala Algorithm, Vector Clocks Algorithm, and Christian's Algorithm. Special attention is given to the analysis of message complexity, providing insights into the efficiency of each algorithm. Finally, we compare the message complexities of the discussed algorithms.

cs.DC↗