We present DIP, a deep learning based framework to learn structural properties of the Internet, such as node clustering or distance between nodes. Existing embedding-based approaches use linear algorithms on a single source of data, such as latency or hop count information, to approximate the position of a node in the Internet. In contrast, DIP computes low-dimensional representations of nodes that preserve structural properties and non-linear relationships across multiple, heterogeneous sources of structural in-formation, such as IP, routing, and distance information. Using a large real-world data set, we show that DIP learns representations that preserve the real-world clustering of the associated nodes and predicts distance between them more than 30% better than a mean-based approach. Furthermore, DIP accurately imputes hop count distance to unknown hosts (i.e., not used in training) given only their IP addresses and routable prefixes. Our framework is extensible to new data sources and applicable to a wide range of problems in network monitoring and security. Read More
#cyberDaily Archives: August 27, 2020
Data driven financial news.
University of Florida students have created an app to help investors spend less time analyzing stocks and newsfeeds in order to make investment decisions. The app analyzes news and social media to help better understand what’s happening in the world around you.
String uses AI to analyze strings of text which include millions of news and social media threads in order to better understand the fabric of society and the events happening in the world. Read More
Rise of Modern NLP and the Need of Interpretability!
Modern NLP is at the forefront of computational linguistics, which is concerned with computational modelling of natural language.
Chomsky’s apprehension on the potential of Computational Linguistics during the 1950s, specifically on the theoretical foundation of those statistical models, was something analogous to Einstein’s reaction to Quantum Physics, “God does not play dice”. These are pivotal moments when the world witnessed the rise of alternative theories. However, by all means, the foundation laid by Chomsky for linguistics theory still remains relevant and aid in progress, analysis, and understanding of the computational linguistics. Read More
Quality of Service Optimization in Mobile Edge Computing Networks via Deep Reinforcement Learning
Mobile edge computing (MEC) is an emerging paradigm that integrates computing resources in wireless access networks to process computational tasks in close proximity to mobile users with low latency. In this paper, we propose an online double deep Q networks ( DDQN) based learning scheme for task assignment in dynamic MEC networks, which enables multiple distributed edge nodes and a cloud data center to jointly process user tasks to achieve optimal long-term quality of service (QoS). The proposed scheme captures a wide range of dynamic network parameters including non-stationary node computing capabilities, network delay statistics, and task arrivals. It learns the optimal task assignment policy with no assumption on the knowledge of the underlying dynamics.In addition, the proposed algorithm accounts for both performance and complexity, and addresses the state and action space explosion problem in conventional Q learning.The evaluation results show that the proposed DDQN-based task assignment scheme significantly improves the QoS performance, compared to the existing schemes that do not consider the effects of network dynamics on the expected long-term rewards,while scaling reasonably well as the network size increases. Read More
#iot, #wifiYouTube removes record number of videos as human moderators replaced by AI
YouTube’s automated filters were less reliable than human moderators, but the company ‘accepted a lower level of accuracy; to ensure harmful content was removed.
YouTube has removed more videos in the second quarter of 2020 than ever before.
During the coronavirus pandemic, when the video sharing site could not rely on its human moderators as much as previously, YouTube increased its use of automated filters in order to take down videos that could potentially violate its policies. Read More
Rick's Cafe AI 