STDs

Who cares about testing?: Co-creations of Socio-technical Software Testing Experiences

Software testing is crucial for ensuring software quality, yet developers' engagement with it varies widely. Identifying the technical, organizational and social factors that lead to differences in engagement is required to remove barriers and utilize enablers for testing. Much research emphasizes the usefulness of testing strategies and technical solutions, less is known about why developers do...

Using ML filters to help automated vulnerability repairs: when it helps and when it doesn't

[Context:] The acceptance of candidate patches in automated program repair has been typically based on testing oracles. Testing requires typically a costly process of building the application while ML models can be used to quickly classify patches, thus allowing more candidate patches to be generated in a positive feedback loop. [Problem:] If the model predictions are unreliable (as in vulnerabi...

Machine learning to improve HIV screening using routine data in Kenya.

INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yie...

STI-Bench: Are MLLMs Ready for Precise Spatial-Temporal World Understanding?

The use of Multimodal Large Language Models (MLLMs) as an end-to-end solution for Embodied AI and Autonomous Driving has become a prevailing trend. ...

GAA-TSO: Geometry-Aware Assisted Depth Completion for Transparent and Specular Objects

Transparent and specular objects are frequently encountered in daily life, factories, and laboratories. However, due to the unique optical propertie...

AtSubP-2.0: An integrated web server for the annotation of Arabidopsis proteome subcellular localization using deep learning.

The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein-protein interactions, ident...

Enhancing HER2 testing in breast cancer: predicting fluorescence in situ hybridization (FISH) scores from immunohistochemistry images via deep learning.

Breast cancer affects millions globally, necessitating precise biomarker testing for effective treatment. HER2 testing is crucial for guiding therapy,...

Association of normalization, non-differentially expressed genes and data source with machine learning performance in intra-dataset or cross-dataset modelling of transcriptomic and clinical data

Cross-dataset testing is critical for examining machine learning (ML) model's performance. However, most studies on modelling transcriptomic and cli...

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesio...

Loop unrolling: formal definition and application to testing

Testing processes usually aim at high coverage, but loops severely limit coverage ambitions since the number of iterations is generally not predicta...

WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing

Radiographic testing is a fundamental non-destructive evaluation technique for identifying weld defects and assessing quality in industrial applicat...

Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data

For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification appl...

Testing Conditional Mean Independence Using Generative Neural Networks

Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In th...

Multimodal AI on Wound Images and Clinical Notes for Home Patient Referral

Chronic wounds affect 8.5 million Americans, particularly the elderly and patients with diabetes. These wounds can take up to nine months to heal, m...

Generalized Single-Image-Based Morphing Attack Detection Using Deep Representations from Vision Transformer

Face morphing attacks have posed severe threats to Face Recognition Systems (FRS), which are operated in border control and passport issuance use ca...

Quantum Testing in the Wild: A Case Study with Qiskit Algorithms

Although classical computing has excelled in a wide range of applications, there remain problems that push the limits of its capabilities, especiall...

Advanced Tutorial: Label-Efficient Two-Sample Tests

Hypothesis testing is a statistical inference approach used to determine whether data supports a specific hypothesis. An important type is the two-s...

Test Input Validation for Vision-based DL Systems: An Active Learning Approach

Testing deep learning (DL) systems requires extensive and diverse, yet valid, test inputs. While synthetic test input generation methods, such as me...

Nanopore- and AI-empowered microbial viability inference

The ability to differentiate between viable and dead microorganisms in metagenomic data is crucial for various microbial inferences, ranging from asse...

An organotypic in vitro model of human papillomavirus-associated precancerous lesions allowing automated cell quantification for preclinical drug testing

A durable organotypic epithelial raft culture was established as a model of cervical precancer. Plausible time- and dose-dependent effects of cisplati...