Search results for "data models"

A Quantum-Inspired Classifier for Early Web Bot Detection

2022

This paper introduces a novel approach, inspired by the principles of Quantum Computing, to address web bot detection in terms of real-time classification of an incoming data stream of HTTP request headers, in order to ensure the shortest decision time with the highest accuracy. The proposed approach exploits the analogy between the intrinsic correlation of two or more particles and the dependence of each HTTP request on the preceding ones. Starting from the a-posteriori probability of each request to belong to a particular class, it is possible to assign a Qubit state representing a combination of the aforementioned probabilities for all available observations of the time series. By levera…

TSVD as a Statistical Estimator in the Latent Semantic Analysis Paradigm

2015

The aim of this paper is to present a new point of view that makes it possible to give a statistical interpretation of the traditional latent semantic analysis (LSA) paradigm based on the truncated singular value decomposition (TSVD) technique. We show how the TSVD can be interpreted as a statistical estimator derived from the LSA co-occurrence relationship matrix by mapping probability distributions on Riemanian manifolds. Besides, the quality of the estimator model can be expressed by introducing a figure of merit arising from the Solomonoff approach. This figure of merit takes into account both the adherence to the sample data and the simplicity of the model. In our model, the simplicity…

A Spatial Origin-Destination Analysis of International Tourism Demand. The Case of Italian Provinces

2021

Climate and agriculture: empirical evidence for countries and agroecological zones of the Sahel

2022

International audience; ow heterogenous is the impact of climate change across space and the type of agricultural production? In this paper, we investigate the relationship between climate change and variability, measured by temperature and rainfall, and agricultural production at the country and agroecological zone levels of the Sahel. We consider a crop production index and five cereals (maize, millet, sorghum, wheat and rice). Based on an original climate database and an agricultural production function estimated for the period 1961–2016, we show that average rainfall and temperature during the growing season indeed have highly heterogeneous effects on agricultural production, depending …

Towards multi-concern software development with Everything-as-Code

2022

As software is becoming a central element in our lives, more and more stakeholders have concerns. Unlike today, when developers stop their coding activities to satisfy these stakeholder concerns, we propose dealing with them as part of the coding workflow, the central element of programmers’ daily duties. This can be achieved by extending the approach that we call Everything-as-Code (EaC) beyond software engineers and operators. peerReviewed

Practices and Infrastructures for Machine Learning Systems : An Interview Study in Finnish Organizations

2022

Using interviews, we investigated the practices and toolchains for machine learning (ML)-enabled systems from 16 organizations across various domains in Finland. We observed some well-established artificial intelligence engineering approaches, but practices and tools are still needed for the testing and monitoring of ML-enabled systems. Peer reviewed

Measuring the Task Induced Oscillatory Brain Activity Using Tensor Decomposition

2019

The characterization of dynamic electrophysiological brain activity, which form and dissolve in order to support ongoing cognitive function, is one of the most important goals in neuroscience. Here, we introduce a method with tensor decomposition for measuring the task-induced oscillations in the human brain using electroencephalography (EEG). The time frequency representation of source-reconstructed singletrail EEG data constructed a third-order tensor with three factors of time ∗ trails, frequency and source points. We then used a non-negative Canonical Polyadic decomposition (NCPD) to identify the temporal, spectral and spatial changes in electrophysiological brain activity. We validate …

Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era

2018

The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resou…