Abstract
Over the last few years, an increasing number of user's and enterprises on the internet has generated a global marketplace for both employers and job seekers. Despite the fact that online job search is now more preferable than traditional methods - leading to better matches between the job seekers and the employer's intents - there is still little insight into how online job searches are different from general web searches. In this paper, we explore the different characteristics of online job search and their differences with general searches, by leveraging search engine query logs. Our experimental results show that job searches have specific attributes which can be used by search engines to increase the quality of the search results.

Abstract
Human language constantly evolves due to the changing world and the need for easier forms of expression and communication. Our knowledge of language evolution is however still fragmentary despite significant interest of both researchers as well as wider public in the evolution of language. In this paper, we present an interactive framework that permits users study the evolution of words and concepts. The system we propose offers a rich online interface allowing arbitrary queries and complex analytics over large scale historical textual data, letting users investigate changes in meaning, context and word relationships across time.

Abstract
Nowadays, humanity generates and contributes to form large and complex datasets, going from documents published on media outlets, posts on social media or location-based information. The generated information tends to be complex, heterogeneous (texts, images, videos, etc.) and is growing at an incredible pace, with much of this data having a strong spatial and temporal focus. This steady increase in the availability of such a volume of information, forces the development of more effective user interfaces that would assist users in efficient visualization, analysis and exploration of the data. This half-day workshop on User Interfaces for Spatial and Temporal Data Analysis (UISTDA) held in conjunction with the IUI2018 conference on March 11th, aimed at sharing the latest progress and developments, current challenges and potential applications for exploiting large amounts of spatial and temporal data. In this paper we provide an overview of the workshop goals together with its main contributions. © 2018 Copyright for the individual papers remains with the authors.

Abstract
The permutation flow shop scheduling problem is one of the most studied operations research related problems. Literally, hundreds of exact and approximate algorithms have been proposed to optimise several objective functions. In this paper we address the total tardiness criterion, which is aimed towards the satisfaction of customers in a make-to-order scenario. Although several approximate algorithms have been proposed for this problem in the literature, recent contributions for related problems suggest that there is room for improving the current available algorithms. Thus, our contribution is twofold: First, we propose a fast beam-search-based constructive heuristic that estimates the quality of partial sequences without a complete evaluation of their objective function. Second, using this constructive heuristic as initial solution, eight variations of an iterated-greedy-based algorithm are proposed. A comprehensive computational evaluation is performed to establish the efficiency of our proposals against the existing heuristics and metaheuristics for the problem.

Abstract
Scheduling jobs on a set of identical parallel machines using efficient heuristics when the objective is to minimize total weighted squared tardiness is considered. Two efficient heuristics and an improvement procedure are presented for the problem. These heuristics and other heuristics are tested using problem sets that represent a variety of conditions. The results show that one of the heuristics consistently performs better than the other heuristics tested. It is also shown how these heuristics can be incorporated into other procedures such as the existing Lagrangian relaxation procedure or meta-heuristics to obtain improved solutions for medium sized problems.

Abstract
This paper presents a platform for real and simulated autonomous sailing competitions, which can also be used as a research tool to test and assess navigation algorithms. The platform provides back-end services – competition server, boat modelling and data storage – and supports external browsers and software agents as front-end clients. The back-end adopts the Multi-Agent System (MAS) paradigm for the internal modelling of sailing boats and offers a Web Service Application Programming Interface (API) for the external software agents and a Web application for Web browsers. As a whole, the platform offers tracking (real competitions) and simulation (simulated competitions) modes. The testing and assessment of navigation algorithms and boat models correspond to private simulated competitions. In simulation mode, the back-end internal boat agent implements a simplified physical model, including the weight, sail area, angle of the sail and rudder, velocity and direction of the wind and position and velocity of the hull, whereas the front-end external boat agent implements the navigation algorithm on the team side, ensuring the privacy of strategic knowledge. The Web application allows the configuration and launching of competitions, the registration of teams and researchers, the uploading of boat physical features for simulation as well as the live or playback viewing of real and simulated competitions. The simulation mode is illustrated with the help of a case study. The proposed platform, which is open, scalable, modular and distributed, was designed for the research community to prepare, run and gather data from real and simulated autonomous sailing competitions.