Misinformation vs. Situational Awareness: The Art of Deception and the Need for Cross-Domain Detection.

The world has been afflicted by the rise of misinformation. The sheer volume of news produced daily necessitates the development of automated methods for separating fact from fiction. To tackle this issue, the computer science community has produced a plethora of approaches, documented in a number of surveys. However, these surveys primarily rely on one-dimensional solutions, i.e., deception detection approaches that focus on a specific aspect of misinformation, such as a particular topic, language, or source. Misinformation is considered a major obstacle for situational awareness, including cyber, both from a company and a societal point of view. This paper explores the evolving field of misinformation detection and analytics on information published in news articles, with an emphasis on methodologies that handle multiple dimensions of the fake news detection conundrum. We analyze and compare existing research on cross-dimensional methodologies. Our evaluation process is based on a set of criteria, including a predefined set of performance metrics, data pre-processing features, and domains of implementation. Furthermore, we assess the adaptability of each methodology in detecting misinformation in real-world news and thoroughly analyze our findings. Specifically, survey insights demonstrate that when a detection approach focuses on several dimensions (e.g., languages and topics, languages and sources, etc.), its performance improves, and it becomes more flexible in detecting false information across different contexts. Finally, we propose a set of research directions that could aid in furthering the development of more advanced and accurate models in this field.

Security in IoMT Communications: A Survey.

The Internet of Medical Things (IoMT) couples IoT technologies with healthcare services in order to support real-time, remote patient monitoring and treatment. However, the interconnectivity of critical medical devices with other systems in various network layers creates new opportunities for remote adversaries. Since most of the communication protocols have not been specifically designed for the needs of connected medical devices, there is a need to classify the available IoT communication technologies in terms of security. In this paper we classify IoT communication protocols, with respect to their application in IoMT. Then we describe the main characteristics of IoT communication protocols used at the perception, network and application layer of medical devices. We examine the inherent security characteristics and limitations of IoMT-specific communication protocols. Based on realistic attacks we identify available mitigation controls that may be applied to secure IoMT communications, as well as existing research and implementation gaps.

Evaluation of the Icare-ONE rebound tonometer as a self-measuring intraocular pressure device in normal subjects.

PURPOSE: To compare Icare ONE rebound self-tonometer (ICRBT) measurements with Goldman applanation tonometry (GAT). METHODS: A trained examiner instructed each of 60 normal subjects on use of the ICRBT. Each subject then took two measurements of his/her own pressure using the ICRBT. Finally, a different examiner, who was masked to the earlier readings, measured IOP by GAT. Bland­Altman limits of agreement (LOA), intraclass correlation coefficients (ICCs), Kappa values, and paired t-test were used to assess the agreement between the two methods. Pearson's correlation coefficient was used for correlation analysis. RESULTS: All of the subjects were able to obtain correct measurements with ICRBT after three attempts. The mean intraocular pressure with ICRBT and GAT measurements were 16.0 ± 3.3 mmHg and 13.7 ± 2.5 mmHg respectively. The mean difference between patient's ICRBT and technician's GAT measurements was 2.3 mmHg (p < 0.001). In 63% (38/60) of the cases the IOP difference (ICRBT − GAT) was within ± 3 mmHg. The weighted Kappa for the IOP measurements of the two methods was 0.49 (95% CI: 0.30­0.68, p < 0.001), indicating acceptable agreement. A significantly positive correlation was found between ICRBT IOP measurements and central corneal thickness (CCT) (r = 0.48, p < 0.001). In addition, the difference in IOP measurements (ICRBT − GAT) between the two methods was positively correlated with CCT (r = 0.31, p = 0.015), indicating that greater thickness is associated with greater differences between the two methods. CONCLUSION: The ICRBT was reliable in the hands of normal subjects, and may be used for self-monitoring of IOP. ICRBT measurements generally overestimated GAT measurements.