Abstract
The news industry has gone through seismic shifts in the past decade with digital content and social media completely redefining how people consume news. Readers check for accurate fresh news from multiple sources throughout the day using dedicated apps or social media on their smartphones and tablets. At the same time, news publishers rely more and more on social networks and citizen journalism as a frontline to breaking news. In this new era of fast-flowing instant news delivery and consumption, publishers and aggregators have to overcome a great number of challenges. These include the verification or assessment of a source’s reliability; the integration of news with other sources of information; real-time processing of both news content and social streams in multiple languages, in different formats and in high volumes; deduplication; entity detection and disambiguation; automatic summarization; and news recommendation. Although Information Retrieval (IR) applied to news has been a popular research area for decades, fresh approaches are needed due to the changing type and volume of media content available and the way people consume this content. The goal of this workshop is to stimulate discussion around new and powerful uses of IR applied to news sources and the intersection of multiple IR tasks to solve real user problems. To promote research efforts in this area, we released a new dataset consisting of one million news articles to the research community and introduced a data challenge track as part of the workshop.

Abstract

Abstract
The news industry has gone through seismic shifts in the past decade with digital content and social media completely redefining how people consume news. Readers check for accurate fresh news from multiple sources throughout the day using dedicated apps or social media on their smartphones and tablets. At the same time, news publishers rely more and more on social networks and citizen journalism as a frontline to breaking news. In this new era of fastflowing instant news delivery and consumption, publishers and aggregators have to overcomea great number of challenges. These include the verification or assessment of a source's reliability; the integration of news with other sources of information; real-time processing of both news content and social streams in multiple languages, in different formats and in high volumes; deduplication; entity detection and disambiguation; automatic summarization; and news recommendation. Although Information Retrieval (IR) applied to news has been a popular research area for decades, fresh approaches are needed due to the changing type and volume of media content available and the way people consume this content. Hence, the first international workshop on recent trends in News Information Retrieval (NewsIR) was held in conjunction with ECIR 2016. As part of the workshop, we released a new dataset consisting of one million news articles to the research community. The workshop was very well attended with around 70 registered participants. We received a healthy number of 19 submissions in total of which 12 were accepted for presentation. In addition to that, we were pleased to have two keynote talks by well-known experts in the field - on with an industry background (Jochen Leidner) and one from academia (Julio Gonzalo). The workshop also included a breakout session to discuss ideas for a future data challenge in news IR and closed with a focused panel discussion to reflect on the day. Throughout the day the workshop stimulated discussions around new and powerful uses of IR applied to news sources and the intersection of multiple IR tasks to solve real user problems. In particular, several ideas were presented on solving complex information needs for media monitoring, event detection and summarisation. Moreover, and going forward, the workshop concluded with a long list of suggestions for shared tasks, and dataset requirements.

Abstract

Abstract
This paper considers the single machine scheduling problem with weighted quadratic tardiness costs. Three metaheuristics are presented, namely iterated local search, variable greedy and steady-state genetic algorithm procedures. These address a gap in the existing literature, which includes branch-and-bound algorithms (which can provide optimal solutions for small problems only) and dispatching rules (which are efficient and capable of providing adequate solutions for even quite large instances). A simple local search procedure which incorporates problem specific information is also proposed. The computational results show that the proposed metaheuristics clearly outperform the best of the existing procedures. Also, they provide an optimal solution for all (or nearly all, in the case of the variable greedy heuristic) the smaller size problems. The metaheuristics are quite close in what regards solution quality. Nevertheless, the iterated local search method provides the best solution, though at the expense of additional computational time. The exact opposite is true for the variable greedy procedure, while the genetic algorithm is a good all-around performer.

Abstract
This paper presents CloudAnchor, a brokerage platform conceived to help Small and Medium Sized Enterprises (SME) embrace Infrastructure as a Service (IaaS) cloud computing both as providers and consumers. The platform, which transacts automatically single and federated IaaS cloud resources, is a multi-layered Multi-Agent System (MAS) where providers, consumers and virtual providers, representing provider coalitions, are modelled by dedicated agents. Federated resources are detained and negotiated by virtual providers on behalf of the corresponding coalition of providers. CloudAnchor negotiates and establishes Service Level Agreements (SLA) on behalf of SME businesses regarding the provision of brokerage services as well as the provision of single and federated IaaS resources. The discovery, invitation, acceptance and negotiation processes rely on a distributed trust model designed to select the best business partners for consumers and providers and improve runtime.