Genetic algorithm as self-test path and circular self-test path design method

The paper presents the use of Genetic Algorithm to search for non-linear Autonomous Test Structures (ATS) in Built-In Testing approach. Such structures can include essentially STP and CSTP and their modifications. Non-linear structures are more difficult to analyze than the widely used structures such as independent Test Pattern Generator and the Test Response Compactor realized by Linear Feedback Shift Registers. To reduce time-consuming test simulation of sequential circuit, it was used an approach based on the stochastic model of pseudo-random testing. The use of stochastic model significantly affects the time effectiveness of the search for evolutionary autonomous structures. In test simulation procedure, the block of sequential circuit memory is not disconnected. This approach does not require a special selection of memory registers such as BILBOs. A series of studies to test circuits set ISCAS’89 are made. The results of the study are very promising

The Future Impacts of Autonomous Aid on Disaster Relief Efforts

Natural disasters and other catastrophes have significantly increased in recent years (Oishi & Komiya, 2017). Currently, resources available to succor in response to cataclysms are limited. Humans save lives, while ultimately risking their own. In order to bypass this risk, autonomous robot programming is essential. Research and advocacy regarding autonomous aid in the scientific community has yet to be fully addressed (Oishi & Komiya, 2017). Because of this, first responders, firefighters, and policemen are perpetually endangered. Furthermore, technological contributions would also eliminate human error, promote productivity, and stimulate collaboration on matters unable to be solved autonomously. A robot prototype, designed to retrieve 6x6х6 inch cubes, was programmed to a controller, but also operated autonomously. Despite the controller being beneficial for specific functions, autonomous programming proved to be advantageous when used applicably. While the initial task was direct, the knowledge acquired from the project possesses the potential to enhance future ventures seeking to aid disaster relief

RiPLE: Recommendation in Peer-Learning Environments Based on Knowledge Gaps and Interests

Various forms of Peer-Learning Environments are increasingly being used in post-secondary education, often to help build repositories of student generated learning objects. However, large classes can result in an extensive repository, which can make it more challenging for students to search for suitable objects that both reflect their interests and address their knowledge gaps. Recommender Systems for Technology Enhanced Learning (RecSysTEL) offer a potential solution to this problem by providing sophisticated filtering techniques to help students to find the resources that they need in a timely manner. Here, a new RecSysTEL for Recommendation in Peer-Learning Environments (RiPLE) is presented. The approach uses a collaborative filtering algorithm based upon matrix factorization to create personalized recommendations for individual students that address their interests and their current knowledge gaps. The approach is validated using both synthetic and real data sets. The results are promising, indicating RiPLE is able to provide sensible personalized recommendations for both regular and cold-start users under reasonable assumptions about parameters and user behavior.Comment: 25 pages, 7 figures. The paper is accepted for publication in the Journal of Educational Data Minin