Qiao Cai

Bio: Qiao Cai is an academic researcher from Stevens Institute of Technology. The author has contributed to research in topics: Self-organizing map & Anomaly detection. The author has an hindex of 6, co-authored 8 publications receiving 146 citations.

Risk-Aware Attacks and Catastrophic Cascading Failures in U.S. Power Grid

Abstract: The power grid network is a complex network which is subjected to attacks and cascading failures. In this paper, we study the vulnerabilities of power grid in terms of cascading failures caused by node failures. Specifically, we define three metrics, the percentage-of-failure, Required Redundancy (RED), and Risk if Failure (RIF), to represent the critical level for each node. Based on these metrics, we can easily find the optimal victim nodes which attackers should choose to attack in order to cause cascading failure. From the defense point of view, these nodes are the weakest components of the power grid system and need more protection. With the results in this paper, we can be more aware of the risk level faced by the system if some nodes are taken down. Simulation results demonstrate the effectiveness of the proposed optimal victim nodes selection strategy.

An integrated incremental self-organizing map and hierarchical neural network approach for cognitive radio learning

Abstract: In this paper, an incremental self-organizing map integrated with hierarchical neural network (ISOM-HNN) is proposed as an efficient approach for signal classification in cognitive radio networks. This approach can effectively detect unknown radio signals in the uncertain communication environment. The adaptability of ISOM can improve the real-time learning performance, which provides the advantage of using this approach for on-line learning and control of cognitive radios in many real-world application scenarios. Furthermore, we propose to integrate the ISOM with the hierarchical neural network (HNN) to improve the learning and prediction accuracy. Detailed learning algorithm and simulation results are presented in this work to demonstrate the effectiveness of this approach.

SOMSO: A self-organizing map approach for spatial outlier detection with multiple attributes

Abstract: In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an effective method for visualization and cluster of high dimensional data. It can preserve intrinsic topological and metric relationships in datasets. The SOMSO method can solve high dimensional problems for spatial attributes and accurately detect spatial outliers with irregular features. The experimental results for the dataset based on U.S. population census indicate that SOMSO approach can successfully be applied in complicated spatial datasets with multiple attributes.

The Self-Organizing Map

Abstract: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

Incremental learning algorithms and applications

Abstract: Incremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years.

A Comprehensive Investigation of the Role of Imbalanced Learning for Software Defect Prediction

Abstract: Context: Software defect prediction (SDP) is an important challenge in the field of software engineering, hence much research work has been conducted, most notably through the use of machine learning algorithms. However, class-imbalance typified by few defective components and many non-defective ones is a common occurrence causing difficulties for these methods. Imbalanced learning aims to deal with this problem and has recently been deployed by some researchers, unfortunately with inconsistent results. Objective: We conduct a comprehensive experiment to explore (a) the basic characteristics of this problem; (b) the effect of imbalanced learning and its interactions with (i) data imbalance, (ii) type of classifier, (iii) input metrics and (iv) imbalanced learning method. Method: We systematically evaluate 27 data sets, 7 classifiers, 7 types of input metrics and 17 imbalanced learning methods (including doing nothing) using an experimental design that enables exploration of interactions between these factors and individual imbalanced learning algorithms. This yields 27 × 7 × 7 × 17 = 22491 results. The Matthews correlation coefficient (MCC) is used as an unbiased performance measure (unlike the more widely used F1 and AUC measures). Results: (a) we found a large majority (87 percent) of 106 public domain data sets exhibit moderate or low level of imbalance (imbalance ratio $ 10; median = 3.94); (b) anything other than low levels of imbalance clearly harm the performance of traditional learning for SDP; (c) imbalanced learning is more effective on the data sets with moderate or higher imbalance, however negative results are always possible; (d) type of classifier has most impact on the improvement in classification performance followed by the imbalanced learning method itself. Type of input metrics is not influential. (e) only ${\sim} 52\%$ ∼ 52 % of the combinations of Imbalanced Learner and Classifier have a significant positive effect. Conclusion: This paper offers two practical guidelines. First, imbalanced learning should only be considered for moderate or highly imbalanced SDP data sets. Second, the appropriate combination of imbalanced method and classifier needs to be carefully chosen to ameliorate the imbalanced learning problem for SDP. In contrast, the indiscriminate application of imbalanced learning can be harmful.

Revealing cascading failure vulnerability in power grids using risk-graph

Abstract: Security issues related to power grid networks have attracted the attention of researchers in many fields. Recently, a new network model that combines complex network theories with power flow models was proposed. This model, referred to as the extended model, is suitable for investigating vulnerabilities in power grid networks. In this paper, we study cascading failures of power grids under the extended model. Particularly, we discover that attack strategies that select target nodes (TNs) based on load and degree do not yield the strongest attacks. Instead, we propose a novel metric, called the risk graph, and develop novel attack strategies that are much stronger than the load-based and degree-based attack strategies. The proposed approaches and the comparison approaches are tested on IEEE 57 and 118 bus systems and Polish transmission system. The results demonstrate that the proposed approaches can reveal the power grid vulnerability in terms of causing cascading failures more effectively than the comparison approaches.