Web Scraping Scientific Repositories for Augmented Relevant Literature Search Using CRISP-DM

Scientific web repositories are central cyber locations where academic papers are stored and maintained. With the nature of the unstructured and semi-structured information/metadata within these repositories, literature analysis for scholar writing becomes a challenge. Correspondingly, applying CRISP-DM poses a stance to address this challenge through formulating a rather augmented process for a relevant literature search. However, almost all repositories do not have a straight forward method where metadata could be extracted for preliminary data processing being applied as part of the CRISP-DM process. Additionally, most repositories do not follow open access standards. Until the time this paper was published, the topic of the augmented, relevant literature search had seen a methodological progress only, with the inability to apply the underlying methods on a larger scale, given data access constraints to open access repositories. The aim of this paper is to propose CRISP-DM as an augmented research methodology with a focus on web scraping as part of the data processing step. To substantiate the proposed methodology, a play role case study is conducted. This then works on alleviating these restrictions, as well as encouraging the wider adoption of the augmented analysis process for a relevant literature search within the research community.

Extending Smart Phone Based Techniques to Provide AI Flavored Interaction with DIY Robots, over Wi-Fi and LoRa interfaces

Inspired by the mobile phone market boost, several low cost credit card-sized computers have made the scene, able to support educational applications with artificial intelligence features, intended for students of various levels. This paper describes the learning experience and highlights the technologies used to improve the function of DIY robots. The paper also reports on the students’ perceptions of this experience. The students participating in this problem based learning activity, despite having a weak programming background and a confined time schedule, tried to find efficient ways to improve the DIY robotic vehicle construction and better interact with it. Scenario cases under investigation, mainly via smart phones or tablets, involved from touch button to gesture and voice recognition methods exploiting modern AI techniques. The robotic platform used generic hardware, namely arduino and raspberry pi units, and incorporated basic automatic control functionality. Several programming environments, from MIT app inventor to C and python, were used. Apart from cloud based methods to tackle the voice recognition issues, locally running software alternatives were assessed to provide better autonomy. Typically, scenarios were performed through Wi-Fi interfaces, while the whole functionality was extended by using LoRa interfaces, to improve the robot’s controlling distance. Through experimentation, students were able to apply cutting edge technologies, to construct, integrate, evaluate and improve interaction with custom robotic vehicle solutions. The whole activity involved technologies similar to the ones making the scene in the modern agriculture era that students need to be familiar with, as future professionals.