Co-distribution and co-infection of chikungunya and dengue viruses

Learning to rank developers for bug report assignment

Pneumonia is one of the leading causes of death in both infants and elderly people, with approximately 4 million deaths each year. It may be a virus, bacterial, or fungal, depending on the contagious pathogen that damages the lung’s tiny air sacs (alveoli). Patients with underlying disorders such as asthma, a weakened immune system, hospitalized babies, and older persons on ventilators are all at risk, particularly if pneumonia is not detected early. Despite the existing approaches for its diagnosis, low accuracy and efficiency require further research for more accurate systems. This study is a similar endeavor for the detection of pneumonia by the use of X-ray images. The dataset is preprocessed to make it suitable for transfer learning tasks. Different pre-trained convolutional neural network (CNN) variants are utilized, including VGG16, Inception-v3, and ResNet50. Ensembles are made by incorporating CNN with Inception-V3, VGG-16, and ResNet50. Besides the common evaluation metrics, the performance of the pre-trained and ensemble deep learning models is measured with Cohen’s kappa as well as the area under the curve (AUC). Experimental results show that Inception-V3 with CNN attained the highest accuracy and recall score of 99.29% and 99.73%, respectively.

https://www.mdpi.com/2075-4418/12/5/1280/pdf?version=1653369062

Pneumonia Classification from X-ray Images with Inception-V3 and Convolutional Neural Network

Perceiving human emotions from a multimodal perspective has received significant attention in knowledge engineering communities. Due to the variable receiving frequency for sequences from various modalities, multimodal streams usually have an inherent asynchronous challenge. Most previous methods performed manual sequence alignment before multimodal fusion, which ignored long-range dependencies among modalities and failed to learn reliable crossmodal element correlations. Inspired by the human perception paradigm, we propose a target and source Modality Co-Reinforcement (MCR) approach to achieve sufficient crossmodal interaction and fusion at different granularities. Specifically, MCR introduces two types of target modality reinforcement units to reinforce the multimodal representations jointly. These target units effectively enhance emotion-related knowledge exchange in fine-grained interactions and capture the crossmodal elements that are emotionally expressive in mixed-grained interactions. Moreover, a source modality update module is presented to provide meaningful features for the crossmodal fusion of target modalities. Eventually, the multimodal representations are progressively reinforced and improved via the above components. Comprehensive experiments are conducted on three multimodal emotion understanding benchmarks. Quantitative results show that MCR significantly outperforms the previous state-of-the-art methods in both word-aligned and unaligned settings. Additionally, qualitative analysis and visualization fully demonstrate the superiority of the proposed modules. 

Target and source modality co-reinforcement for emotion understanding from asynchronous multimodal sequences

Twitter being among the popular social media platforms, provide peoples’ opinions regarding specific ideas, products, services, etc. The large amounts of shared data as tweets can help extract users’ sentiment and provide valuable feedback to improve the quality of products and services alike. Similar to other service industries, the airline industry utilizes such feedback for determining customers’ satisfaction levels and improving the quality of experience where needed. This, of course, requires accurate sentiments from the user tweets. Existing sentiment analysis models suffer from low accuracy on account of the contradictions found in the tweet text and the assigned label. From this perspective, this study proposes a hybrid sentiment analysis approach where the lexicon-based methods are used with deep learning models to improve sentiment accuracy. Experiments involve analyzing the impact of TextBlob on the classification accuracy of models as against the original annotations, considering that the probability of the false annotations cannot be overlooked. Furthermore, the efficacy of TextBlob against Afinn and VADER (Valence Aware Dictionary for Sentiment Reasoning) is also evaluated. The CNN (Convolutional Neural Network), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), and CNN-LSTM are deployed in comparison with state-of-the-art machine learning models. Additionally, the efficiency and efficacy of TF-IDF (Term Frequency-Inverse Document Frequency) and BoW (Bag of Words) are also investigated. Results suggest that models perform better when trained using the TextBlob assigned sentiments as compared to the original sentiments in the dataset. LSTM-GRU outperforms all models and previous studies with the highest 0.97 accuracy and 0.96 F1 scores. From machine learning models, the support vector classifier and extra tree classifier achieve the highest accuracy score of 0.92, with TF-IDF and BoW, respectively. Despite the good performance of the models using the TextBlob labels, TextBlob-based annotation cannot replace humans. Our stance is that with humans, bias, error-proneness, and subjectivity cannot be ignored; so we propose that the TextBlob-annotated labels can be used as assistance for human annotators where human annotators can wet the TextBlob-annotated dataset. 

Sentiment analysis on Twitter data integrating TextBlob and deep learning models: The case of US airline industry

Test smells are defined as sub-optimal design choices developers make when implementing test cases. Hence, similar to code smells, the research community has produced numerous test smell detection tools to investigate the impact of test smells on the quality and maintenance of test suites. However, little is known about the characteristics, type of smells, target language, and availability of these published tools. In this paper, we provide a detailed catalog of all known, peer-reviewed, test smell detection tools. 
We start with performing a comprehensive search of peer-reviewed scientific publications to construct a catalog of 22 tools. Then, we perform a comparative analysis to identify the smell types detected by each tool and other salient features that include programming language, testing framework support, detection strategy, and adoption, among others. From our findings, we discover tools that detect test smells in Java, Scala, Smalltalk, and C++ test suites, with Java support favored by most tools. These tools are available as command-line and IDE plugins, among others. Our analysis also shows that most tools overlap in detecting specific smell types, such as General Fixture. Further, we encounter four types of techniques these tools utilize to detect smells. We envision our study as a one-stop source for researchers and practitioners in determining the tool appropriate for their needs. Our findings also empower the community with information to guide future tool development.

Test smell detection tools: A systematic mapping study

In recent years, mobile accessibility has become an important trend with the goal of allowing all users the possibility of using any app without many limitations. User reviews include insights that are useful for app evolution. However, with the increase in the amount of received reviews, manually analyzing them is tedious and time-consuming, especially when searching for accessibility reviews. The goal of this paper is to support the automated identification of accessibility in user reviews, to help technology professionals in prioritizing their handling, and thus, creating more inclusive apps. Particularly, we design a model that takes as input accessibility user reviews, learns their keyword-based features, in order to make a binary decision, for a given review, on whether it is about accessibility or not. The model is evaluated using a total of 5,326 mobile app reviews. The findings show that (1) our model can accurately identify accessibility reviews, outperforming two baselines, namely keyword-based detector and a random classifier; (2) our model achieves an accuracy of 85% with relatively small training dataset; however, the accuracy improves as we increase the size of the training dataset.

Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

/pdf/covid-19-vaccination-related-sentiments-analysis-a-case-2wupdkmb.pdf

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

Bug localization is the automated process of finding the possible faulty files in a software project. Bug localization allows developers to concentrate on vital files. Information retrieval (IR)-based approaches have been proposed to assist automatically identify software defects by using bug report information. However, some bug reports that are not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based reporting system can lead to false-positive results. In this paper, we propose a classification model for classifying a bug report as either uninformative or informative. Our approach helps to lower false positives and increase ranking performances by filtering uninformative information before running an IR-based bug location system. The model is based on implicit features learned from bug reports that use neural networks and explicit features defined manually. We test our proposed model on three open-source software projects that contain over 9000 bug reports. The results of the evaluation show that our model enhances the efficiency of a developed IR-based system in the trade-off between precision and recall. For implicit features, our tests with comparisons show that the LSTM network performs better than the CNN and multilayer perceptron with respect to the F-measurements. Combining both implicit and explicit features outperforms using only implicit features. Our classification model helps improve precision in bug localization tasks when precision is considered more important than recall.

Wajdi Aljedaani

Papers

Test smell detection tools: A systematic mapping study

Finding the Needle in a Haystack: On the Automatic Identification of Accessibility User Reviews

Learning to rank developers for bug report assignment

COVID-19 Vaccination-Related Sentiments Analysis: A Case Study Using Worldwide Twitter Dataset

On the classification of bug reports to improve bug localization