International audienceAs a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by [Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1–29.]. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total).In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP’s variant); (iv) Chang’s extent analysis method, which is used for FPCMs’ weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature)

Named Entity Recognition (NER) is a key building block of any Natural Language Processing (NLP) system, making possible the detection and classification of entities (e.g., Person, Location) in any given text. While a large number of NER software exist today, it remains difficult for NLP and NER practitioners to clearly and objectively identify what software perform(s) the best. One of the reasons is the difference in results across the literature and the lack of information needed to be able to fully reproduce the experiment. To overcome this problem, this paper presents a comprehensive and replicable study to assess the performance of NER software, thus laying the groundwork for future benchmarking and meaningful comparison studies. As part of our experiments, the latest version of five well-known NER software were selected, along with two distinct corpora. We observe a discrepancy between the result we get and the result found in the literature being around 50% in certain cases. We also found that StanfordNLP usually performs the best.

International audienceThe quality of metadata in open data portals plays a crucial role for the success of open data. E-government, for example, have to manage accurate and complete metadata information to guarantee the reliability and foster the reputation of e-government to the public. Measuring and comparing the quality of open data is not a straightforward process because it implies to take into consideration multiple quality dimensions whose quality may vary from one another, as well as various open data stakeholders who – depending on their role/needs – may have different preferences regarding the dimensions' importance. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing e-government models, this paper develops an Open Data Portal Quality (ODPQ) framework that enables end-users to easily and in real-time assess/rank open data portals. From a theoretical standpoint, the Analytic Hierarchy Process (AHP) is used to integrate various data quality dimensions and end-user preferences. From a practical standpoint, the proposed framework is used to compare over 250 open data portals, powered by organizations across 43 different countries. The findings of our study reveals that today's organizations do not pay sufficient heed to the management of datasets, resources and associated metadata that they are currently publishing on their portal

This paper proposes a decentralized scheduling framework for peer-to-peer (P2P) transactive energy trading between prosumers and the local electricity market (LEM). In this approach, prosumers are modeled in the form of networked microgrids (NMG) and smart parking lots (SPL), which can exchange energy in a P2P framework. At the same time, each of the prosumers exchanges information and energy with the LEM in a decentralized approach based on the distribution locational marginal price (DLMP). The LEM is modeled by an active radial electricity distribution network (EDN), considering a high penetration of renewable energy sources (RES) and the feeder power losses in a convex optimization model. In proposed P2P transactive energy model, the alternating direction method of multipliers (ADMM) has been used to achieve the equilibrium point between peers. The distributionally robust optimization (DRO) method has been used to model the uncertain behavior of RES in NMGs, SPLs, and the EDN. In addition, the impact of applying smart charging strategies for electric vehicles (EV) in the proposed P2P transactive energy model has been investigated. To confirm the functionality of the model, 12-bus, 6-bus, and 4-bus networks have been used to model NMGs, while the EDN is embodied by a standard IEEE 33-bus and 123-bus test systems.