The number of documents is growing exponentially with the rapid expansion of the Web. The new challenge for Internet users is now to rapidly find appropriate data to their requests. Thus information retrieval, automatic classification and detection of opinions appear as major issues in our information society. Many efficient tools have already been proposed to Internet users to ease their search over the web and support them in their choices. Nowadays, users would like genuine decision tools that would efficiently support them when focusing on relevant information according to specific criteria in their area of interest. In this paper, we propose a new approach for automatic characterization of such criteria. We bring out that this approach is able to automatically build a relevant lexicon for each criterion. We then show how this lexicon can be useful for documents classification or segmentation tasks. Experiments have been carried out with real datasets and show the efficiency of our proposal.

The nationwide lockdown inflicted by the global COVID-19 disease epidemic and imposed during 57 days in France was not immune to fluctuations in atmospheric pollutant concentrations. A whole range of human activities has been suspended Monday 17 March 2020 in all French regions. Since then experiments are progressing to reflect the effectiveness of reduced emissions. In this paper, we looked at variations of pollutants prior to, during and after containment period. In a first step, we proved through experiments on eight air pollutants, how all daily maximum pollutants concentration have decreased during containment phase, apart from the ozone pollutant O 3. This Ozone pollutant has indeed increased by 27.19% during lockdown period and kept growing by 21.35% as well right after deconfinement. Indeed, the maximum daily concentrations detected in different regions of France, have decreased by 18.18%, 37.14%, 20.36%, 9.28%, 44.38%, 5.1% and 44.38%, respectively, for the pollutants SO 2 , NO 2 , CO, C 6 H 6 , NOX, PM 2.5 and PM 10. Declining levels of other pollutants, however, were not sustained after deconfinement for NO 2 , NOX and PM 10. We have reinforced these findings by classifying each pollutant according to the ATMO and AQI indexes, to better visualize their criticality throughout the three lockdown phases (Pre/During/Post). The family of air pollutant variables with their associated geographical sources was thereafter exploited to justify their approximate contribution to the daily mortality rates associated to COVID-19 across all French regions. However, more thorough study is still in progress to validate this finding. Finally, coming up to the abrupt changes in airborne pollutants experienced in this period, a question about future climate crisis was raised again. Whereby a weighting study has shown the current and very shortterm French scenario (Status-Quo) in view of its current environmental path, the political responses made towards future climate change crisis and French investments done in this sense.

Given the perpetual surging of cloud services' requests, energy consumption of cloud data centers with their related CO 2 emissions still represents major issues. Efficient use of cloud's resources becomes then the driven force ensuring both, the satisfaction of service-level agreements and the sobriety of cloud's energy consumption. This paper purports to survey for the first time, a comprehensive literature of some actual challenges facing various cloud's resources utilization scheduling approaches dealing with energy conservation. Indeed, cloud resources involve not only computing servers, but also a wide set of intra-and inter-cloud network's resources to be considered. These resources are mainly provisioned through two wellknown technologies: virtualization and/or containerization. As existing studies in the area of cloud resources provisioning are generally categorized into different groups, this research depicts a complete taxonomy of energy-efficient cloud resources scheduling. In this same perspective, we survey some recent efforts made in energy-efficient virtual and containerized resources, by emphasizing first the most used reactive then proactive techniques for managing the whole cloud resources energy efficiency scheduling.

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Abstract. The success of Information technologies and associated services (e.g., blogs, forums,...) eases the way to express massive opinion on various topics. Recently new techniques known as opinion mining have emerged. One of their main goals is to automatically extract a global trend from expressed opinions. While it is easy to get this overall assessment, a more detailed analysis will highlight that the opinions are expressed on more specific topics: one will acclaim a movie for its soundtrack and another will criticize it for its scenario. Opinion mining approaches have little explored this multicriteria aspect. In this paper we propose an automatic extraction of text segments related to a set of criteria. The opinion expressed in each text segment is then automatically extracted. From a small set of opinion keywords, our approach automatically builds a training set of texts from the web. A lexicon reflecting the polarity of words is then extracted from this training corpus. This lexicon is then used to compute the polarity of extracted text segments. Experiments show the efficiency of our approach.

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With a view to overcome internet ossification problem, various Virtual Network Embedding (VNE) approaches has been proposed in last few years. Nevertheless, most of prior approaches neglect some major operational requirements implied by the inherent virtualization platforms. In the case of SD-ODCN architecture, a crucial operational requirement instance is the ability to route application-specific flows or wavelengths dynamically and efficiently across multi-tenant network providers. With the perpetual bursting of clouds and IP traffics, efficient dynamic VNE not only consists on maximizing ISPs and cloud Service Provider (SP) revenues, but also involves a strong need to reduce carbon emissions. In this paper, we introduce a new Parallel, Proactive and Power Efficient VNE in a Green and Distributed SD-ODCN architecture. We first formulate a Mixed Integer Linear Programming (MILP) model purposing to maximize total intra Data Center (DC)' servers and inter networking resources power efficiency as a function of users' request rates. Afterward, we proposed a new green location-aware, Parallel Global resource Topology Ranking (PGTR) method, prioritizing first the greenest server and network nodes. Depending on resulted ranking process classes, a Parallel and Proactive VNE (PPVNE) is therefore proposed to effectively maximize total DC's and networking resources power efficiency. After implementing the whole proposed algorithms namely (PGTR-PPVNE) under NSFNET network topology related data, extensive simulations results proved the improvement of the proposed (PGTR-PPVNE) approach over four other benchmark methods. More precisely, the proposed (PGTR-PPVNE) achieved 6.87% decrease, 10.77% increase, 58.54 % decrease and a 1.15% increase over the proactive ACO-VNE benchmark approach, respectively in terms of total power consumption, total power efficiency, requests response times and acceptance ratio.

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