Automatic elicitation of semantic information from natural language texts is an important research problem with many practical application areas. Especially after the recent proliferation of online content through channels such as social media sites, news portals, and forums; solutions to problems such as sentiment analysis, sarcasm/controversy/veracity/rumour/fake news detection, and argument mining gained increasing impact and significance, revealed with large volumes of related scientific publications. In this article, we tackle an important problem from the same family and present a survey of stance detection in social media posts and (online) regular texts. Although stance detection is defined in different ways in different application settings, the most common definition is “automatic classification of the stance of the producer of a piece of text, towards a target, into one of these three classes: {Favor, Against, Neither}.” Our survey includes definitions of related problems and concepts, classifications of the proposed approaches so far, descriptions of the relevant datasets and tools, and related outstanding issues. Stance detection is a recent natural language processing topic with diverse application areas, and our survey article on this newly emerging topic will act as a significant resource for interested researchers and practitioners.

Stance Detection: A Survey

Recently, many manufacturing enterprises pay closer attention to energy efficiency due to increasing energy cost and environmental awareness. Energy-efficient scheduling of production systems is an effective way to improve energy efficiency and to reduce energy cost. During the past 10 years, a large amount of literature has been published about energy-efficient scheduling, in which more than 50% employed swarm intelligence and evolutionary algorithms to solve the complex scheduling problems. This paper aims to provide a comprehensive literature review of production scheduling for intelligent manufacturing systems with the energy-related constraints and objectives. The main goals are to summarize, analyze, discuss, and synthesize the existing achievements, current research status, and ongoing studies, and to give useful insight into future research, especially intelligent strategies for solving the energy-efficient scheduling problems. The scope of this review is focused on the journal publications of the Web of Science database. The energy efficiency-related publications are classified and analyzed according to five criteria. Then, the research trends of energy efficiency are discussed. Finally, some directions are pointed out for future studies.

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A review of energy-efficient scheduling in intelligent production systems

Stance and Gender Detection in Tweets on Catalan Independence (StanceCat) is a new shared task proposed for the first time at the IberEval 2017 evaluation campaign. The automatic natural language systems presented must detect the tweeter stance (in favor, against or neutral) towards the target independence of Catalonia in Twitter messages written in Spanish or Catalan, as weel as the author’s gender if possible. We have received a total of 31 submitted runs from 10 different teams from 5 countries. We present here the datasets, which include annotations for dealing with stance and gender, the evaluation methodology, and discuss results and participating systems.

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Overview of the Task on Stance and Gender Detection in Tweets on Catalan Independence.

Rescheduling in job-shop problems for sustainable manufacturing systems

A holonic multi-agent methodology to design sustainable intelligent manufacturing control systems

Assessment of mathematical programming and agent-based modelling for off-line scheduling: application to energy aware manufacturing

Advance in applied scheduling is a source of innovation in the manufacturing field, where new results help industrial practitioners in production management. The literature of rescheduling problems for single-objective optimization is well study, while there is a lack of extensive studies for the case of rescheduling for multi-objective optimization, especially for energy aware scheduling. This paper extends a previous work over an energy aware scheduling problem, modelled from a real industrial case study, extending its manufacturing environment to a dynamic one. To this end, two rescheduling techniques are developed to tackle machines disruptions (greedy-heuristic and meta-heuristic). They are compared to existing approach thought manufacturing environment simulations. The results give insight to improve the production management in terms of rescheduling quality and computational time.

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Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems.

This paper describes our participation in the Classification Of Spanish Election Tweets (COSET) task at IberEval 2017. During the searching process for the best classification system, we developed a comparative study over possible combinations of corpus preprocessing, text representations and classification models. After an initial models exploration, we focus our attention over specific neural models. Interesting insight can be drawn from the comparative study helping future practitioners tackling tweets classification problems to create system baseline for their work.

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Comparative Study of Neural Models for the COSET Shared Task at IberEval 2017.

Nowadays,the manufacturing industry consumes almost half of the energy produced on the planet. Within this industry, sustainable scheduling represents a main issue due to the energy efficiency that it is able to achieve. Centralized or decentralized approaches to sustainable scheduling can be found in state-of- the-art techniques using both optimal or heuristic methods. In this paper, a distributed system modelled as a multi-agent framework is proposed to solve energy-aware scheduling problems by applying it to a real case of the injection air moulding industry, which has intense energy consumption. In this proposal, a set of agents collaborate to reach an agreement in order to minimize several objective functions based on: total job weighted tardiness, total setup time, and energy consumption. The proposed system is compared to centralized and distributed approaches, highlighting advantages and drawbacks and providing an interesting point of view about how to tackle energy-aware scheduling with new agreement techniques in a distributed environment.

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A Multi-Agent Framework to Solve Energy-Aware Unrelated Parallel Machine Scheduling Problems with Machine-Dependent Energy Consumption and Sequence-Dependent Setup Time

This paper describes our participation in the Stance and Gender Detection in Tweets on Catalan Independence (StanceCat) task at IberEval 2017. Our approach was focused on neural models, firstly using classical and specific model from state of the art, then we introduce a new topology of convolutional network for text classification.

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Giancarlo Nicolò

Papers

Assessment of mathematical programming and agent-based modelling for off-line scheduling: application to energy aware manufacturing

Dynamic Rescheduling in Energy-Aware Unrelated Parallel Machine Problems.

Comparative Study of Neural Models for the COSET Shared Task at IberEval 2017.

A Multi-Agent Framework to Solve Energy-Aware Unrelated Parallel Machine Scheduling Problems with Machine-Dependent Energy Consumption and Sequence-Dependent Setup Time

Neural Models for StanceCat Shared Task at IberEval 2017.