Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Yang, Wenzhuo"'
This paper aims to provide differentiated security protection for infotainment data communication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication techno
Externí odkaz:
http://arxiv.org/abs/2403.20136
With the rapid development of low-cost consumer electronics and cloud computing, Internet-of-Things (IoT) devices are widely adopted for supporting next-generation distributed systems such as smart cities and industrial control systems. IoT devices a
Externí odkaz:
http://arxiv.org/abs/2401.11968
Training a large and state-of-the-art machine learning model typically necessitates the use of large-scale datasets, which, in turn, makes the training and parameter-tuning process expensive and time-consuming. Some researchers opt to distil informat
Externí odkaz:
http://arxiv.org/abs/2310.10541
Autor:
Liu, Chenghao, Yang, Wenzhuo, Mittal, Himanshu, Singh, Manpreet, Sahoo, Doyen, Hoi, Steven C. H.
We introduce PyRCA, an open-source Python machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps). It provides a holistic framework to uncover the complicated metric causal dependencies and automati
Externí odkaz:
http://arxiv.org/abs/2306.11417
Autor:
Cheng, Qian, Sahoo, Doyen, Saha, Amrita, Yang, Wenzhuo, Liu, Chenghao, Woo, Gerald, Singh, Manpreet, Saverese, Silvio, Hoi, Steven C. H.
Artificial Intelligence for IT operations (AIOps) aims to combine the power of AI with the big data generated by IT Operations processes, particularly in cloud infrastructures, to provide actionable insights with the primary goal of maximizing availa
Externí odkaz:
http://arxiv.org/abs/2304.04661
Software and System logs record runtime information about processes executing within a system. These logs have become the most critical and ubiquitous forms of observability data that help developers understand system behavior, monitor system health
Externí odkaz:
http://arxiv.org/abs/2301.13415
Autor:
Arpit, Devansh, Fernandez, Matthew, Feigenbaum, Itai, Yao, Weiran, Liu, Chenghao, Yang, Wenzhuo, Josel, Paul, Heinecke, Shelby, Hu, Eric, Wang, Huan, Hoi, Stephen, Xiong, Caiming, Zhang, Kun, Niebles, Juan Carlos
We introduce the Salesforce CausalAI Library, an open-source library for causal analysis using observational data. It supports causal discovery and causal inference for tabular and time series data, of discrete, continuous and heterogeneous types. Th
Externí odkaz:
http://arxiv.org/abs/2301.10859
Detecting anomalies and the corresponding root causes in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. Previous anomaly detection appr
Externí odkaz:
http://arxiv.org/abs/2206.15033
We introduce OmniXAI (short for Omni eXplainable AI), an open-source Python library of eXplainable AI (XAI), which offers omni-way explainable AI capabilities and various interpretable machine learning techniques to address the pain points of underst
Externí odkaz:
http://arxiv.org/abs/2206.01612
Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning model is differentiable
Externí odkaz:
http://arxiv.org/abs/2205.15540