In today's communication-centric world, users generate and exchange a huge amount of data. The Internet helps user data to travel from one part of the world to another via a complex setting of network systems. These systems are intelligent, heterogeneous, and non-transparent to users. In this paper, we present an extensive trace-driven study of user data traffic covering five years of observations, six large ISPs, 21 different autonomous systems, and a total of 13 countries. The aim of this work is to make users aware about how their data travels in the Internet, as the data traffic path is majorly influenced by the interests of ISPs. We showed that shortest land distance between the two countries does not impact data path selection, while data traffic prefers to travel even though country do not share land borders.

Many companies seek to gain reputation by going through a certification process, and the standard of choice for many companies will be the ISO 27001:2013 standard [1]. A thorough literature review on ISO27001 research [8] found that only 26% of the studies have cited outcomes of the ISO27001 certification, and only 3 papers focus explicitly on the impact of the standard. Neither of these focus on availability and risk, and no research provides real data from a risk registry showing the standard’s effect. In this research we analyse the controls of the standard from the point of view of a Network Operations Centre. We further analyse a global network operator’s risk registry over a 5-year period and show how the risks and risk improvements relate to the ISO27001 information security objectives. The results show that the implementation of ISO27001 caused a significant reduction of risk in all areas, and argues that the ISO27001 standard is well suited to reducing the operator’s risk related to the objects of confidentiality, integrity and availability.

Colonoscopy is a gold standard procedure but is highly operator-dependent. Efforts have been made to automate the detection and segmentation of polyps, a precancerous precursor, to effectively minimize missed rate. Widely used computer-aided polyp segmentation systems actuated by encoder-decoder have achieved high performance in terms of accuracy. However, polyp segmentation datasets collected from varied centers can follow different imaging protocols leading to difference in data distribution. As a result, most methods suffer from performance drop when trained and tested on different distributions and therefore, require re-training for each specific dataset. We address this generalizability issue by proposing a global multi-scale residual fusion network (GMSRF-Net). Our proposed network maintains high-resolution representations by performing multi-scale fusion operations across all resolution scales through dense connections while preserving low-level information. To further leverage scale information, we design cross multi-scale attention (CMSA) module that uses multi-scale features to identify, keep, and propagate informative features. Additionally, we introduce multi-scale feature selection (MSFS) modules to perform channel-wise attention that gates irrelevant features gathered through global multi-scale fusion within the GMSRF-Net. The repeated fusion operations gated by CMSA and MSFS demonstrate improved generalizability of our network. Experiments conducted on two different polyp segmentation datasets show that our proposed GMSRF-Net outperforms the previous top-performing state-of-the-art method by 8.34% and 10.31% on unseen CVC-ClinicDB and on unseen Kvasir-SEG, in terms of dice coefficient. Additionally, when tested on unseen CVC-ColonDB, we surpass the state-of-the-art method by 9.38% and 4.04% in terms of dice coefficient, when source dataset is Kvasir-SEG and CVC-ClinicDB, respectively.