Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.
The rapid evolution of the Internet of Medical Things (IoMT) introduces the healthcare ecosystem into a new reality consisting of smart medical devices and applications that provide multiple benefits, such as remote medical assistance, timely administration of medication and real-time monitoring. However, despite the valuable advantages, this new reality increases the cybersecurity and privacy concerns since vulnerable IoMT devices can access and handle autonomously patients' data. Furthermore, the continuous evolution of cyberattacks, malware and zero-day vulnerabilities require the development of the appropriate countermeasures. In the light of the aforementioned remarks, in this paper, we present an Intrusion Detection and Prevention System (IDPS), which can protect the healthcare communications that rely on the Hypertext Transfer Protocol (HTTP) and the Modbus/Transmission Control Protocol (TCP). HTTP is commonly adopted by conventional healthcare-related services, such as web-based Electronic Health Record (EHR) applications, while Modbus/TCP is an industrial protocol adopted by IoMT. Although the Machine Learning (ML) and Deep Learning (DL) methods have already demonstrated their efficacy in detecting intrusions, the rarely available intrusion detection datasets (especially in the healthcare sector) complicate their global application. The main contribution of this work lies in the fact that an active learning approach is modelled and adopted in order to re-train dynamically the supervised classifiers behind the proposed IDPS. The evaluation analysis demonstrates the efficiency of this work against HTTP and Modbus/TCP cyberattacks, showing also how the entire accuracy is increased in the various re-training phases.
This paper describes the online dynamic examination application plugin named "ODES", developed according to the open source software philosophy, where the CMS Wordpress is used as programmers/coders are given the potential to develop applications from scratch with safety and ease. In ODES application there exists two types of users: admin/teacher and student. The admin/teacher can create/edit/delete/view questions, categories of questions and examination papers. The questions are divided in two types, multiple choice questions (including true/false) and long answer questions (essays). The teacher can create exams choosing the number of questions and the types of questions that exist in the pool of questions that are previously created. The selection is done randomly by the application and the teacher just determines the total number of both multiple choice or long answer questions as well as the importance (weight) of each one of them (including negative grades also). The student takes the random generated exam and receives his/hers grades. The grades of the multiple choice questions are done automatically, whereas for the long answer questions the teacher is responsible to put grades on. After the completion of the exam the teacher can view the student's final score via the control panel or a report.
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