All Outputs (3)

Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset] (2022)
Dataset
SENANAYAKE, J., KALUTARAGE, H., AL-KADRI, M.O., PIRAS, L. and PETROVSKI, A. 2023. Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models [Dataset]. Hosted on GitHub (online). Available from: https://github.com/softwaresec-labs/LVDAndro

Many of the Android apps get published without appropriate security considerations, possibly due to not verifying code or not identifying vulnerabilities at the early stages of development. This can be overcome by using an AI based model trained on a... Read More about Labelled Vulnerability Dataset on Android source code (LVDAndro) to develop AI-based code vulnerability detection models. [Dataset].

MVVA-net: a video aesthetic quality assessment network with cognitive fusion of multi-type feature–based strong generalization. [Dataset] (2022)
Dataset
LI, M., WANG, Z., REN, J. and SUN, M. 2022. MVVA-net: a video aesthetic quality assessment network with cognitive fusion of multi-type feature–based strong generalization. [Dataset]. Hosted on GitHub [online]. Available from: https://github.com/Lm0324/MVVA-Net

Most of the existing video aesthetic quality assessment datasets (as seen in Table 1) are not public, some are not large enough, which makes the trained depth model perform poorly and some are based on the professionalism of video shooting or the rat... Read More about MVVA-net: a video aesthetic quality assessment network with cognitive fusion of multi-type feature–based strong generalization. [Dataset].

Using artificial intelligence methods for systematic review in health sciences: a systematic review. [Appendices] (2022)
Dataset
BLAIZOT, A., VEETTIL, S.K., SAIDOUNG, P., MORENO-GARCIA, C.F., WIRATUNGA, N., ACEVES-MARTINS, M., LAI, N.M. and CHAIYAKUNAPRUK, N. 2022. Using artificial intelligence methods for systematic review in health sciences: a systematic review. [Appendices]. Research synthesis methods [online], 1393), pages 353-362. Available from: https://onlinelibrary.wiley.com/action/downloadSupplement?doi=10.1002%2Fjrsm.1553&file=jrsm1553-sup-0001-supinfo.docx

Systematic reviews are fundamental to evidence-based decision making, as they use a comprehensive search and synthesis of the available literature. Such an operation usually requires a team of reviewers to evaluate thousands of articles. With the exp... Read More about Using artificial intelligence methods for systematic review in health sciences: a systematic review. [Appendices].