Biblio

Different security issues are a common problem for open source packages archived to and delivered through software ecosystems. These often manifest themselves as software weaknesses that may lead to concrete software vulnerabilities. This paper examines various security issues in Python packages with static analysis. The dataset is based on a snapshot of all packages stored to the Python Package Index (PyPI). In total, over 197 thousand packages and over 749 thousand security issues are covered. Even under the constraints imposed by static analysis, (a) the results indicate prevalence of security issues; at least one issue is present for about 46% of the Python packages. In terms of the issue types, (b) exception handling and different code injections have been the most common issues. The subprocess module stands out in this regard. Reflecting the generally small size of the packages, (c) software size metrics do not predict well the amount of issues revealed through static analysis. With these results and the accompanying discussion, the paper contributes to the field of large-scale empirical studies for better understanding security problems in software ecosystems.

In the current world, day by day the data growth and the investigation about that information increased due to the pervasiveness of computing devices, but people are reluctant to share their information on online portals or surveys fearing safety because sensitive information such as credit card information, medical conditions and other personal information in the wrong hands can mean danger to the society. These days privacy preserving has become a setback for storing data in data repository so for that reason data in the repository should be made undistinguishable, data is encrypted while storing and later decrypted when needed for analysis purpose in data mining. While storing the raw data of the individuals it is important to remove person-identifiable information such as name, employee id. However, the other attributes pertaining to the person should be encrypted so the methodologies used to implement. These methodologies can make data in the repository secure and PPDM task can made easier.

This paper begins to describe a new kind of database, one that explores a diverse range of movement in the field of dance through capture of different bodies and different backgrounds - or what we are terming movement vernaculars. We re-purpose Ivan Illich's concept of 'vernacular work' [11] here to refer to those everyday forms of dance and organized movement that are informal, refractory (resistant to formal analysis), yet are socially reproduced and derived from a commons. The project investigates the notion of vernaculars in movement that is intentional and aesthetic through the development of a computational approach that highlights both similarities and differences, thereby revealing the specificities of each individual mover. This paper presents an example of how this movement database is used as a research tool, and how the fruits of that research can be added back to the database, thus adding a novel layer of annotation and further enriching the collection. Future researchers can then benefit from this layer, further refining and building upon these techniques. The creation of a robust, open source, movement lexicon repository will allow for observation, speculation, and contextualization - along with the provision of clean and complex data sets for new forms of creative expression.