Digital Forensics, A Need for Credentials and Standards

The purpose of the conducted study was to explore the credentialing of digital forensic investigators, drawing from applicable literature. A qualitative, descriptive research design was adopted which entailed searching across Google Scholar and ProQuest databases for peer reviewed articles on the subject matter. The resulting scholarship was vetted for timeliness and relevance prior to identification of key ideas on credentialing. The findings of the study indicated that though credentialing was a major issue in digital forensics with an attentive audience of stakeholders, it had been largely overshadowed by the fundamental curricula problems in the discipline. A large portion of research and efforts were directed towards designing a clear and standardized framework for teaching digital forensics. While contending with an apparent scarcity of literature, it was apparent that state and federal governments were relatively passive in offering credentials to digital forensic investigators. This had been mostly left to private companies such as Mile2, EC-Council and ISC2, with the government providing some guidelines through the Department of Justice (DoJ) and affiliates like NIST, OSAC and NAS. The involvement of private credentialing in some cases had led to mis-trials and thus there has been a need to have a unified framework for collection, reporting, and submission of digital forensic evidence. It would be recommended that more efforts be directed towards credentialing including advocacy, funding and research and a national framework for teaching digital forensics to be developed together with a standard credentialing system. Additionally, the state and federal governments would need to step up and take active roles in the credentialing process

A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems

Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants