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"Gabillon, Alban"
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A kernel machine for hidden object-ranking problems (HORPs)
Hidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clustered. We propose a kernel machine that allows retaining item-related ordinal information while avoiding emphasizing class-related information. We show how this kernel machine can be implemented with standard optimization libraries provided slight modifications on the original kernel. The proposed approach is tested on Tahitian pearls quality assessment and compared with four other classical methods. It yields better results (93.6% ± 3.9% of correct predictions without feature selection, 94.3% ± 3.4% with feature selection) than the best of the other tested methods (91.3% ± 3.4% and 92.6% ± 4.3% without and with feature selection for the instance-ranking approach), this improvement being significant (p-value < 0.05). Moreover, this method exhibits no significant difference in the results with and without feature selection (p-value = 0.33), which may be a hint that its learning bias fits the problem well and can thus alleviate the data preprocessing workload.
Journal Article
A database for sea‐level monitoring in French Polynesia
This article presents a curated database of the sea‐level measurements acquired by the network of the five geodetic tide gauges managed over French Polynesia by the Geodesy Observatory of Tahiti from 13 June 2009 to 28 January 2021. A unique feature of this database, with respect to previous databases that host the same raw data, like the Intergovernmental Oceanographic Commission database (IOC, www.ioc‐sealevelmonitoring.org) and the database of ‘Réseaux de référence des observations marégraphiques’ (REFMAR, http://refmar.shom.fr) is that all the time‐tags of the raw measurements (1‐ or 2‐min sampling) have been validated and, if necessary, corrected with a precision of 2 min (time shifts of up to 1 hr can be present in the raw data). Possible outliers have also been flagged, but not removed. In addition, smoothed hourly data are also provided, along with tidal analysis results and estimations of the sea‐level trends for the five tide gauges, with respect to their local geodetic datum. The database, entitled ‘SEA LEVEL collected from TIDE STATIONS in South Pacific Ocean from 2009‐06‐13 to 2021‐01‐28’, can be accessed on the NOAA data servers as ‘NCEI Accession 0244182’ and contains two subsets: The first one is relative to the original sampling rate and the second one is relative to an hourly re‐sampling with harmonic tide models for each tide gauge station. This article presents a curated database, with corrected time‐stamps, of the Sea Level measurements acquired by the network of the five geodetic tide gauges managed over French Polynesia by the Geodesy Observatory of Tahiti from 2009‐06‐13 to 2021‐01‐28. In addition, smoothed hourly data are also provided, along with tidal analysis results, tide models and estimated sea‐level trends for the five tide gauges, with respect to their local geodetic datum. The database, entitled “SEA LEVEL collected from TIDE STATIONS in South Pacific Ocean from 2009‐06‐13 to 2021‐01‐28”, can be accessed on the NOAA data servers as “NCEI Accession 0244182”.
Journal Article
Access Controls for IoT Networks
The message queuing telemetry transport (MQTT) protocol is becoming the main protocol for the internet of things (IoT). In this paper, we define a highly expressive attribute-based access control (ABAC) security model for the MQTT protocol. Our model allows us to regulate not only publications and subscriptions, but also distribution of messages to subscribers. We can express various types of contextual security rules (temporal security rules, content-based security rules, rules based on the frequency of events, etc.).
Journal Article
A kernel machine for hidden object-ranking problems (HORPs)
Hidden Object-Ranking Problems (HORPs) are object-ranking problems stated as classification or instance-ranking problems. There exists so far no dedicated algorithm for solving them properly and HORPs are usually solved as if they were classification (multi-class or ordinal) or instance-ranking problems. In the former case, item-related ordinal information is negated and only class-related information is retained; in the latter case, item-related ordinal information is considered, but in a way that emphasizes class-related information, so that the items are not only sorted but also clustered. We propose a kernel machine that allows retaining item-related ordinal information while avoiding emphasizing class-related information. We show how this kernel machine can be implemented with standard optimization libraries provided slight modifications on the original kernel. The proposed approach is tested on Tahitian pearls quality assessment and compared with four other classical methods. It yields better results (93.6
%
± 3.9
%
of correct predictions without feature selection, 94.3
%
± 3.4
%
with feature selection) than the best of the other tested methods (91.3
%
± 3.4
%
and 92.6
%
± 4.3
%
without and with feature selection for the instance-ranking approach), this improvement being significant (p-value < 0.05). Moreover, this method exhibits no significant difference in the results with and without feature selection (p-value = 0.33), which may be a hint that its learning bias fits the problem well and can thus alleviate the data preprocessing workload.
Journal Article
Secure time-stamping schemes: a distributed point of view
Time-stamping is a technique used to prove the existence of a digital document prior to a specific point in time. Today, implemented schemes rely on a centralized server model that has to be trusted. We point out the drawbacks of these schemes, showing that the unique serveur represent a weak point for the system. We propose an alternative scheme which is based on a network of servers managed by administratively independent entities. This scheme appears to be a trusted and reliable distributed time-stamping scheme.
Journal Article
A Logical Formalization of a Secure XML Database
In this paper, we first define a logical theory representing an XML database supporting XPath as query language and XUpdate as modification language. We then extend our theory with predicates allowing us to specify the security policy protecting the database. The security policy includes rules addressing the read and write privileges. We propose axioms to derive the database view each user is permitted to see. We also propose axioms to derive the new database content after an update.
Paper
Cover Story Management
In a multilevel database, cover stories are usually managed using the ambiguous technique of polyinstantiation. In this paper, we define a new technique to manage cover stories and propose a formal representation of a multilevel database containing cover stories. Our model aims to be a generic model, that is, it can be interpreted for any kind of database (e.g. relational, object- oriented etc). We then consider the problem of updating a multilevel database containing cover stories managed with our technique.
Paper
A Comprehensive Review on Non-Neural Networks Collaborative Filtering Recommendation Systems
Over the past two decades, recommender systems have attracted a lot of interest due to the explosion in the amount of data in online applications. A particular attention has been paid to collaborative filtering, which is the most widely used in applications that involve information recommendations. Collaborative filtering (CF) uses the known preference of a group of users to make predictions and recommendations about the unknown preferences of other users (recommendations are made based on the past behavior of users). First introduced in the 1990s, a wide variety of increasingly successful models have been proposed. Due to the success of machine learning techniques in many areas, there has been a growing emphasis on the application of such algorithms in recommendation systems. In this article, we present an overview of the CF approaches for recommender systems, their two main categories, and their evaluation metrics. We focus on the application of classical Machine Learning algorithms to CF recommender systems by presenting their evolution from their first use-cases to advanced Machine Learning models. We attempt to provide a comprehensive and comparative overview of CF systems (with python implementations) that can serve as a guideline for research and practice in this area.
Paper