Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Precision medicine and artificial intelligence : a pilot study on deep learning for hypoglycemic events detection based on ECG

Tracking the fluctuations in blood glucose levels is important for healthy subjects and crucial diabetic patients. Tight glucose monitoring reduces the risk of hypoglycemia, which can result in a series of complications, especially in diabetic patients, such as confusion, irritability, seizure and can even be fatal in specific conditions. Hypoglycemia affects the electrophysiology of the heart. However, due to strong inter-subject heterogeneity, previous studies based on a cohort of subjects failed to deploy electrocardiogram (ECG)-based hypoglycemic detection systems reliably. The current study used personalised medicine approach and Artificial Intelligence (AI) to automatically detect nocturnal hypoglycemia using a few heartbeats of raw ECG signal recorded with non-invasive, wearable devices, in healthy individuals, monitored 24 hours for 14 consecutive days. Additionally, we present a visualisation method enabling clinicians to visualise which part of the ECG signal (e.g., T-wave, ST-interval) is significantly associated with the hypoglycemic event in each subject, overcoming the intelligibility problem of deep-learning methods. These results advance the feasibility of a real-time, non-invasive hypoglycemia alarming system using short excerpts of ECG signal

Reviewing Traffic ClassificationData Traffic Monitoring and Analysis

Traffic classification has received increasing attention in the last years. It aims at offering the ability to automatically recognize the application that has generated a given stream of packets from the direct and passive observation of the individual packets, or stream of packets, flowing in the network. This ability is instrumental to a number of activities that are of extreme interest to carriers, Internet service providers and network administrators in general. Indeed, traffic classification is the basic block that is required to enable any traffic management operations, from differentiating traffic pricing and treatment (e.g., policing, shaping, etc.), to security operations (e.g., firewalling, filtering, anomaly detection, etc.). Up to few years ago, almost any Internet application was using well-known transport layer protocol ports that easily allowed its identification. More recently, the number of applications using random or non-standard ports has dramatically increased (e.g. Skype, BitTorrent, VPNs, etc.). Moreover, often network applications are configured to use well-known protocol ports assigned to other applications (e.g. TCP port 80 originally reserved for Web traffic) attempting to disguise their presence. For these reasons, and for the importance of correctly classifying traffic flows, novel approaches based respectively on packet inspection, statistical and machine learning techniques, and behavioral methods have been investigated and are becoming standard practice. In this chapter, we discuss the main trend in the field of traffic classification and we describe some of the main proposals of the research community. We complete this chapter by developing two examples of behavioral classifiers: both use supervised machine learning algorithms for classifications, but each is based on different features to describe the traffic. After presenting them, we compare their performance using a large dataset, showing the benefits and drawback of each approac

On the Integration of Blockchain and SDN: Overview, Applications, and Future Perspectives

Blockchain (BC) and Software-Defined Networking (SDN) are leading technologies which have recently found applications in several network-related scenarios and have consequently experienced a growing interest in the research community. Indeed, current networks connect a massive number of objects over the Internet and in this complex scenario, to ensure security, privacy, confidentiality, and programmability, the utilization of BC and SDN have been successfully proposed. In this work, we provide a comprehensive survey regarding these two recent research trends and review the related state-of-the-art literature. We first describe the main features of each technology and discuss their most common and used variants. Furthermore, we envision the integration of such technologies to jointly take advantage of these latter efficiently. Indeed, we consider their group-wise utilization -- named BC-SDN -- based on the need for stronger security and privacy. Additionally, we cover the application fields of these technologies both individually and combined. Finally, we discuss the open issues of reviewed research and describe potential directions for future avenues regarding the integration of BC and SDN. To summarize, the contribution of the present survey spans from an overview of the literature background on BC and SDN to the discussion of the benefits and limitations of BC-SDN integration in different fields, which also raises open challenges and possible future avenues examined herein. To the best of our knowledge, compared to existing surveys, this is the first work that analyzes the aforementioned aspects in light of a broad BC-SDN integration, with a specific focus on security and privacy issues in actual utilization scenarios.Comment: 42 pages, 14 figures, to be published in Journal of Network and Systems Management - Special Issue on Blockchains and Distributed Ledgers in Network and Service Managemen

Sibyl:A Practical Internet Route Oracle

Network operators measure Internet routes to troubleshoot problems, and researchers measure routes to characterize the Internet. However, they still rely on decades-old tools like traceroute, BGP route collectors, and Looking Glasses, all of which permit only a single query about Internet routes—what is the path from here to there? This limited interface complicates answering queries about routes such as "find routes traversing the Level3/AT&T peering in Atlanta," to understand the scope of a reported problem there. This paper presents Sibyl, a system that takes rich queries that researchers and operators express as regular expressions, then issues and returns traceroutes that match even if it has never measured a matching path in the past. Sibyl achieves this goal in three steps. First, to maximize its coverage of Internet routing, Sibyl integrates together diverse sets of traceroute vantage points that provide complementary views, measuring from thousands of networks in total. Second, because users may not know which measurements will traverse paths of interest, and because vantage point resource constraints keep Sibyl from tracing to all destinations from all sources, Sibyl uses historical measurements to predict which new ones are likely to match a query. Finally, based on these predictions, Sibyl optimizes across concurrent queries to decide which measurements to issue given resource constraints. We show that Sibyl provides researchers and operators with the routing information they need—in fact, it matches 76% of the queries that it could match if an oracle told it which measurements to issue

The Art of Detecting Forwarding Detours

The full Internet feed, reaching ~867K prefixes as of March 2021, has been growing at ≈50K prefixes/year over the last 10 years. To counterbalance this sustained increase, Autonomous Systems (ASes) may filter prefixes, perform prefix aggregation and use default routes. Despite being effective, such workarounds may result in routing inconsistencies, i.e., in routers along a forwarding route mapping the same IP addresses to different IP prefixes. In turn, the exit AS border routers associated with these distinct prefixes may potentially differ. For some prefixes, forwarding detours (FDs) may occur, i.e., traffic may deviate from best IGP paths. In this work we investigate the phenomenon of FDs and derive a methodology to detect them. In particular, our tool is able to pinpoint cases where multiple prefixes are subject to FDs. We run measurements from 100 vantage points of the NLNOG RING monitoring infrastructure and find FDs in 25 out of 54 ASes. We see that FDs are heterogeneous, i.e., the number of prefixes and AS border routers in between which we detect FDs strongly depend on the studied AS. Finally, we discover a remarkable binary effect such that either all transit traffic traversing between two border routers of an AS detours, or none does

ARTICLE IN PRESS Simulation Modelling Practice and Theory xxx (2004) xxx–xxx

A simulation environment for GPRS traffic in an advanced travellers information syste

Do you know what you are generating?

Software-based traffic generators are commonly used in ex-perimental research on computer networks. However, there are no much studies focusing on how such instruments are accurate. Here we start a discussion reviewing the prob-lem of using software-based traffic generators over common hardware/software, highlighting interesting issues that pose some threats to common beliefs. We started comparing the operator-requested traffic profile against the real behavior of commonly used software-based traffic generators. We aim at performing tests under different conditions and looking both at packet/bit rate and inter-packet time distribution. Prelimi-nary results show notable differences in some cases, opening the way to interesting discussions and further investigations