COBRA: Context-Aware Bernoulli Neural Networks for Reputation Assessment
Authors: Leonit Zeynalvand, Tie Luo, Jie Zhang7317-7324
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The performance of COBRA is validated by our experiments using a real dataset, and by our simulations, where we also show that COBRA outperforms other state-of-the-art TRM systems. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Nanyang Technological University, Singapore 2Department of Computer Science, Missouri University of Science and Technology, USA leonit001@e.ntu.edu.sg, tluo@mst.edu, zhangj@ntu.edu.sg |
| Pseudocode | Yes | Algorithm 1 Training data initialization, Algorithm 2 Update training data vertically, Algorithm 3 Update training data horizontally |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology described is open-source or publicly available. |
| Open Datasets | Yes | Dataset. We use a public dataset obtained from (Zheng, Zhang, and Lyu 2014) which contains the response-time values of 4, 532 web services invoked by 142 service users over 64 time slices. The dataset contains 30, 287, 611 records of data in total, which translates to a data sparsity of 26.5%. |
| Dataset Splits | Yes | We employ 10-fold cross validation and compare the performance of COBRA with the benchmark methods described in Section 5.1. |
| Hardware Specification | No | All measurements are conducted using the same Linux workstation with 12 CPU cores and 32GB of RAM. The paper does not specify the exact CPU model or any GPU used. |
| Software Dependencies | No | The functional API of Keras is used for the implementation of the neural network architectures on top of Tensor Flow backend while scikit-learn is used for the implementation of Gaussian process, decision tree, and Gaussian Naive Bayes models. However, no specific version numbers for these software components are provided. |
| Experiment Setup | No | The paper describes the network topology (N=3 layers, width calculation), activation functions (sigmoid, ReLU), and loss function (cross-entropy), and states that weights are computed using gradient descent backpropagation. However, it does not provide specific hyperparameters such as learning rate, batch size, or number of epochs for training. |