Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning

Federated learning, as a distributed learning that conducts the training on the local devices without accessing to the training data, is vulnerable to Byzatine poisoning adversarial attacks. We argue that the federated learning model has to avoid those kind of adversarial attacks through filtering out the adversarial clients by means of the federated aggregation operator. We propose a dynamic federated aggregation operator that dynamically discards those adversarial clients and allows to prevent the corruption of the global learning model. We assess it as a defense against adversarial attacks deploying a deep learning classification model in a federated learning setting on the Fed-EMNIST Digits, Fashion MNIST and CIFAR-10 image datasets. The results show that the dynamic selection of the clients to aggregate enhances the performance of the global learning model and discards the adversarial and poor (with low quality models) clients.R&D&I grants - MCIN/AEI, Spain PID-2020-119478GB-I00 PID2020-116118GA-I00 EQC2018-005-084-PERDF A way of making EuropeMCIN/AEI FPU18/04475 IJC2018-036092-

A multi-criteria fuzzy method for selecting the location of a solid waste disposal facility

Facility location is a multicriteria decision process that has important operational and economic impacts and that typically involves uncertainty and vagueness of evaluations. A fuzzy-based method supporting preliminary decision-making about siting solid waste incinerators is proposed building on a structured classification of criteria for location selection developed from the existing literature. The application to a case study revealed the advantages of the methodology. The work intends to provide a general and comprehensive taxonomy of decision criteria that may be adapted to various facility location problems together with a fuzzy inference process that is useful for companies and public administration institutions looking for rigorous but relatively simple decision-making tools in uncertain environments. Future research will compare the developed method with the most common tools for making location decisions. The approach will be then extended to different kinds of facilitie

Natural Language Interfaces to Data

Recent advances in NLU and NLP have resulted in renewed interest in natural language interfaces to data, which provide an easy mechanism for non-technical users to access and query the data. While early systems evolved from keyword search and focused on simple factual queries, the complexity of both the input sentences as well as the generated SQL queries has evolved over time. More recently, there has also been a lot of focus on using conversational interfaces for data analytics, empowering a line of non-technical users with quick insights into the data. There are three main challenges in natural language querying (NLQ): (1) identifying the entities involved in the user utterance, (2) connecting the different entities in a meaningful way over the underlying data source to interpret user intents, and (3) generating a structured query in the form of SQL or SPARQL. There are two main approaches for interpreting a user's NLQ. Rule-based systems make use of semantic indices, ontologies, and KGs to identify the entities in the query, understand the intended relationships between those entities, and utilize grammars to generate the target queries. With the advances in deep learning (DL)-based language models, there have been many text-to-SQL approaches that try to interpret the query holistically using DL models. Hybrid approaches that utilize both rule-based techniques as well as DL models are also emerging by combining the strengths of both approaches. Conversational interfaces are the next natural step to one-shot NLQ by exploiting query context between multiple turns of conversation for disambiguation. In this article, we review the background technologies that are used in natural language interfaces, and survey the different approaches to NLQ. We also describe conversational interfaces for data analytics and discuss several benchmarks used for NLQ research and evaluation.Comment: The full version of this manuscript, as published by Foundations and Trends in Databases, is available at http://dx.doi.org/10.1561/190000007

Managing corporate sustainability and responsibility efficiently: A review of existing literature on business groups and networks

Given the global relevance of business groups (BG) and networks as efficient organizational forms for corporate sustainability and responsibility systems (CSR), and seeing that management control systems (MCS) play a pivotal role in transmitting authority to CSR and formalizing a sustainability organizational culture, this paper aims to review the available literature in order to investigate efficient adoptions of CSR by BGs or networks. Both organizational forms have positive effects on CSR development, on three levels: (a) setting industry standards (macro-external environment); (b) stimulating sustainability-oriented innovations (mezzo-member firms); (c) reputational gains, CSR expenses mitigation, and optimization of organizational capabilities (micro-individual SMEs). The studies on SMEs were useful in identifying current sustainability practices: both partial (social, environmental) and complete sustainability systems were susceptible to being integrated with management accounting, making them an almost implicit tool for proper CSR. Finally, by gathering the empirical literature on sustainability transitions of networks and groups, it was possible to trace a comprehensive introductory plan that operators could resort to for initial guidance. The six steps of this process are (1) project initiation, (2) preliminary actions, (3) change management decision, (4) firm-level activities, (5) auditing, (6) transition to territorial social responsibility (optional)