From Raw Sensor Data to Detailed Spatial Knowledge
Authors: Peng Zhang, Jae Hee Lee, Jochen Renz
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to evaluate the quality and accuracy of our method, we developed a tool that can randomly generate arbitrarily complex spatial regions (see Figure 4)... We created 100 random complex regions and two measurements with random angle per region and per sensor density and applied our method to match the measurements and to extract the spatial relations from the resulting match. We then evaluated the effectiveness of the initial match and the additional fine-tuning step... |
| Researcher Affiliation | Academia | Peng Zhang and Jae Hee Lee and Jochen Renz Australian National University Canberra, Australia {p.zhang, jae-hee.lee, jochen.renz}@anu.edu.au |
| Pseudocode | Yes | Algorithm 1: Integrating sensor measurements. and Algorithm 2: Extracting spatial knowlege. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states, 'we developed a tool that can randomly generate arbitrarily complex spatial regions' and 'We created 100 random complex regions'. This indicates a self-generated dataset for experiments, but no public access information (link, DOI, citation) is provided for this dataset. |
| Dataset Splits | No | The paper describes using '100 random complex regions' for evaluation but does not specify any training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper mentions 'sensor networks' but does not specify any hardware details (e.g., GPU/CPU models, memory, or specific computing infrastructure) used for running the experiments or training the models. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow 2.x, PyTorch 1.x) that were used in the implementation or experiments. |
| Experiment Setup | No | The paper describes how regions and sensor measurements are randomly generated for evaluation, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rate, batch size, epochs), optimizer settings, or other configuration parameters for any model training or data processing. |