AugSplicing: Synchronized Behavior Detection in Streaming Tensors
Authors: Jiabao Zhang, Shenghua Liu, Wenting Hou, Siddharth Bhatia, Huawei Shen, Wenjian Yu, Xueqi Cheng4653-4661
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We design the experiments to answer the following questions: Q1. Speed and Accuracy: How fast and accurate does our algorithm run compared to the state-of-the-art streaming algorithms and the re-run of batch algorithms on real data? Q2. Real-World Effectiveness: Which anomalies or lockstep behavior does AUGSPLICING spot in real data? Q3. Scalability: How does the running time of our algorithm increase as input tensor grows? |
| Researcher Affiliation | Collaboration | Jiabao Zhang1, Shenghua Liu1 #, Wenting Hou2, Siddharth Bhatia3, Huawei Shen1, Wenjian Yu4 #, Xueqi Cheng1 1 Institute of Computing Technology, Chinese Academy of Sciences 2 Beijing Innov Sharing Co.Ltd 3 National University of Singapore 4Dept. Computer Science & Tech., Tsinghua University |
| Pseudocode | Yes | Algorithm 1 Splice two dense blocks |
| Open Source Code | Yes | Reproducibility: Our code and datasets are publicly available at https://github.com/BGT-M/Aug Splicing. |
| Open Datasets | Yes | Two rating data are publicly available. App data is mobile device-app installation and uninstallation data under an NDA agreement from a company. Wi-Fi data is device-AP connection and disconnection data from Tsinghua University. |
| Dataset Splits | No | The paper describes a streaming tensor setting where data arrives incrementally, but it does not specify traditional train/validation/test dataset splits with percentages, sample counts, or predefined split citations. |
| Hardware Specification | Yes | All experiments are carried out on a 2.3GHz Intel Core i5 CPU with 8GB memory. |
| Software Dependencies | No | The paper mentions that 'D-CUBE is implemented in Java' but does not provide specific version numbers for Java or any other software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | We set time stride s to 30 in a day for Yelp, 15 in a day for Beer Advocate, 1 in a day for App and Wi-Fi, as different time granularity. k is set to 10 and l to 5 for all datasets. |