59,311 research outputs found
Learning to rank music tracks using triplet loss
Most music streaming services rely on automatic recommendation algorithms to
exploit their large music catalogs. These algorithms aim at retrieving a ranked
list of music tracks based on their similarity with a target music track. In
this work, we propose a method for direct recommendation based on the audio
content without explicitly tagging the music tracks. To that aim, we propose
several strategies to perform triplet mining from ranked lists. We train a
Convolutional Neural Network to learn the similarity via triplet loss. These
different strategies are compared and validated on a large-scale experiment
against an auto-tagging based approach. The results obtained highlight the
efficiency of our system, especially when associated with an Auto-pooling
layer
Learning to Customize Network Security Rules
Security is a major concern for organizations who wish to leverage cloud
computing. In order to reduce security vulnerabilities, public cloud providers
offer firewall functionalities. When properly configured, a firewall protects
cloud networks from cyber-attacks. However, proper firewall configuration
requires intimate knowledge of the protected system, high expertise and
on-going maintenance.
As a result, many organizations do not use firewalls effectively, leaving
their cloud resources vulnerable. In this paper, we present a novel supervised
learning method, and prototype, which compute recommendations for firewall
rules. Recommendations are based on sampled network traffic meta-data (NetFlow)
collected from a public cloud provider. Labels are extracted from firewall
configurations deemed to be authored by experts. NetFlow is collected from
network routers, avoiding expensive collection from cloud VMs, as well as
relieving privacy concerns.
The proposed method captures network routines and dependencies between
resources and firewall configuration. The method predicts IPs to be allowed by
the firewall. A grouping algorithm is subsequently used to generate a
manageable number of IP ranges. Each range is a parameter for a firewall rule.
We present results of experiments on real data, showing ROC AUC of 0.92,
compared to 0.58 for an unsupervised baseline. The results prove the hypothesis
that firewall rules can be automatically generated based on router data, and
that an automated method can be effective in blocking a high percentage of
malicious traffic.Comment: 5 pages, 5 figures, one tabl
Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design
Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design
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