Surface magnetic resonance sounding (MRS) offers the advantages of direct, quantitative, and unique interpretations in the field of groundwater detection. The time-lapse inversion (TLI) method, with its temporal continuity, has been applied to monitor the time-varying trends of the hydrological parameters of groundwater. However, the ambient noise levels in MRS data fluctuate significantly over time (daily), and the presence of low signal-to-noise ratio (SNR) data can lead to a deterioration in the results of TLI. Thus, we propose a new TLI of MRS data weighted by noise-level estimation in this article. Noise-weighted TLI (NW-TLI) quantifies the reliability of each MRS dataset on the basis of noise estimation residuals and incorporates time-lapse reference weights into the inversion process, thereby ensuring that the hydrological trends are more reasonably constrained by high-SNR data. In synthetic data experiments, we demonstrate that the NW-TLI method effectively mitigates interference from adjacent low-SNR data under various complicated noisy cases. Even with multiple sets of low-SNR MRS data, NW-TLI can provide more accurate hydrological time-varying trends than conventional TLI. Additionally, we assess the impact of the temporal variability of the water-bearing model and the degree of data weighting on the interpretative accuracy and ultimately validate the practicability of the NW-TLI method via field-measured data.

Concerns have been growing over fake news and its impact. Software that can automatically detect fake news is becoming more popular. However, the accuracy and reliability of such fake-news detection software remains questionable, partly due to a lack of testing and verification. Testing this kind of software may face the oracle problem, which refers to difficulty (or inability) of identifying the correctness of the software’s output in a reasonable amount of time. Metamorphic testing (MT) has a record of effectively alleviating the oracle problem, and has been successfully applied to testing fake-news detection software. This paper reports on a study, extending previous work, exploring the use of MT for fake-news detection software. The study includes new metamorphic relations and additional experimental results and analysis. Some alternative MR-generation approaches are also explored. The study targets software where the output is a real/fake news decision, enhancing the applicability of MT to current fake-news detection software. The paper also explores the impact of the prediction accuracy of the fake-news detection software on the MT process. The study demonstrates the validity and applicability of MT to fake-news detection software. The prediction accuracy of the software has a greater impact on MT experiments with greater changes between the source and follow-up inputs, and less dependence on prediction stability. Some possible factors affecting the experimental results are discussed, and directions for future work are provided.

Since the popularization of social media, news has entered our lives digitally. While news is spreading broader and faster, fake news is becoming an increasingly popular topic. Fake news detection is therefore important in both social media and research areas. With artificial intelligence technology, software engineers have developed a lot of fake news detection systems. One of the biggest challenges for such systems is that they may face the oracle problem, which means that there may not be a way, or it may take too long time, to confirm the correctness of a specific output. Metamorphic Testing has been applied successfully to alleviate the oracle problem in many different areas, including in artificial intelligence. In this paper, we propose several metamorphic relations for fake news detection and report on experiments using metamorphic testing on fake news detection applications.

Affective texts play a key role in sentiment classification/prediction and decision making. They are being increasingly used to form and/or share sentiments in financial, economic and/or political applications. However, the processing time is exponentially increased for large affective textual datasets. Moreover, casual expressions such as emoji, slang, abbreviation and misspelling words usually make data analysis (i.e., text classification) complicated. This paper proposes a pipeline model consisting of data pre-processing, feature extraction and classification model training to classify affective text datasets. It offers three contributions including Emoji recovery, misspelling word correction and abbreviation translation that results in maximised classification accuracy. A rigorous experimental plan is designed to evaluate the performance of the proposed approach according to three factors including dataset size (i.e., small, medium and large), NLP feature extraction technique (i.e., TF-IDF, word2vec and BERT) and classification model (i.e., MLP, Logistic Regression, Naive Bayes and SVM). In addition, the proposed approach is compared with a well-known Deep Learning sentiment analysis approach, named sentimentDLmodel, which addresses a pre-trained sentiment analysis. According to the results, the proposed approach significantly outperforms benchmarks in terms of classification model accuracy for most cases.