Intrusion detection is an essential task for protecting the cyber environment from attacks. Many studies have proposed sophisticated models to detect intrusions from a large amount of data, yet they ignore the fact that poor data quality has a direct impact on the performance of the intrusion detection systems. This article first summarizes existing machine learning-based intrusion detection systems and the datasets used for building these systems. Then the data preparation workflow and data quality requirements for intrusion detection are discussed. In order to investigate how data quality affects model performance, we conducted experiments on 11 HIDS datasets using eight machine learning (ML) models and two pre-trained language models (BERT and GPT-2). The experimental results show: 1. BERT and GPT outperform the other models on all of the datasets. 2. The pre-trained models and the classic ML models behave differently when duplicate data and overlapped data are removed from a dataset. The pre-trained models are more capable of learning from duplicate and overlapped data compared to the classic ML models. 3. Removing overlaps and duplicates can improve the performances of the pre-trained models and the traditional ML models on most datasets used in this study. However, doing this can sometimes cause model performance to be decreased. 4. The reliability of model performance is affected when a testing data contain duplicates. 5. The overlapped rate between the normal and intrusion classes seems to have an inverse relationship to the pre-trained models’ performances on the intrusion detection task. Given the results, we discuss model selection in HIDS, and quality assurance in training data and testing data based on nine data quality dimensions.

Big data validation and system verification are crucial for ensuring the quality of big data applications. However, a rigorous technique for such tasks is yet to emerge. During the past decade, we have developed a big data system called CMA for investigating the classification of biological cells based on cell morphology which is captured in diffraction images. CMA includes a collection of scientific software tools, machine learning algorithms, and a large-scale cell image repository. In order to ensure the quality of big data system CMA, we developed a framework for rigorously validating the massive scale image data as well as adequately verifying both the software tools and machine learning algorithms. The validation of big data is conducted by iteratively selecting the data using a machine learning approach. An experimental approach guided by a feature selection algorithm is introduced in the framework to select an optimal feature set for improving the machine learning performance. The verification of software and algorithms is developed on the iterative metamorphic testing approach due to the non-testable property of the software and algorithms. A machine learning approach is introduced for developing test oracles iteratively to ensure the adequacy of the test coverage criteria. Performance of the machine learning algorithm is evaluated with a stratified N-fold cross validation and confusion matrix. We describe the design of the proposed big data verification and validation framework with CMA as the case study, and demonstrate its effectiveness through verifying and validating the dataset, the software and the algorithms in CMA.

A high-quality corpus is essential for building an effective legal intelligence system. The quality of a corpus includes both the quality of original data and the quality of its corresponding labeling. The major quality dimensions of a legal corpus include comprehensiveness, freshness, and correctness. However, building a comprehensive, correct, and fresh legal corpus is a grand challenge. In this article, we propose a semiautomated machine learning framework to address the challenge. We first created an initial corpus with 4937 instances that were manually labeled. Several strategies were implemented to assure its quality. The initial results showed that class imbalance and insufficiency of training data are the two major quality issues that negatively impacted the quality of the system that was built on the data. We experimented and compared three class-imbalance-handling techniques and found that the mixed-sampling method, which combines upsampling and downsampling, was the most effective way to address the issue. In order to address the insufficiency of training data, we experimented several machine learning methods for automated data augmentation including pseudolabeling, co-training, expectation-maximization, and generative adversarial network (GAN). The results showed that GAN with deep learning models achieved the best performance. Finally, ensemble learning of different classifiers was proposed and experimented with for the construction of a legal corpus, which achieves higher quality in comprehensiveness, freshness, and correctness compared to existing work. The semiautomated machine learning framework and the data quality evaluation method developed in this research can be used for data augmentation and quality evaluation of a large dataset as well as a reference for the selection of machine learning methods for data augmentation and generation. The machine learning models, the training data, and the legal corpus are published and publicly accessible at [Online]. Available: https://github.com/haihua0913/legalArgumentmining .

During navigation, a pedestrian needs to recognize a landmark at a certain decision point. If a potential landmark located at a decision point is complicated to recognize, the complexity of the decision point is significantly increased. Thus, it is important to compute routes that avoid complicated decision points (CDPs) but still achieve optimal navigation performance. In this paper, we propose an approach for computing routes that avoid CDPs while optimizing the performance of landmark-based pedestrian navigation. The approach includes (1) a model for identifying CDPs based on the structures of pedestrian networks and landmark data in real scenes, and (2) a modified genetic algorithm for computing routes that avoid the identified CDPs and find the shortest route possible. To demonstrate the advantages and effectiveness of the proposed approach, we conducted an empirical study on the pedestrian network in a real-world scenario. The experimental results show that our approach can effectively avoid CDPs while still minimizing travel distance. Furthermore, our approach can provide the routes with the shortest travel distance if the distances of the routes without CDPs exceed a certain threshold.