We are witnessing a paradigm shift in human language technology that may well have an impact on the field comparable to the statistical revolution: acquiring large-scale resources by exploiting collective intelligence. An illustration of this approach is Phrase Detectives, an interactive online game-with-a-purpose for creating anaphorically annotated resources that makes use of a highly distributed population of contributors with different levels of expertise. The paper gives an overview of all aspects of Phrase Detectives, from the design of the game and the methods used, to the results obtained so far. It furthermore summarises the lessons that have been learnt in developing the game to help other researchers assess and implement the approach.

We research and develop autonomous mobile service robots as Collaborative Robots, i.e., CoBots. For the last three years, our four CoBots have autonomously navigated in our multi-floor office buildings for more than 1,000km, as the result of the integration of multiple perceptual, cognitive, and actuations representations and algorithms. In this paper, we identify a few core aspects of our CoBots underlying their robust functionality. The reliable mobility in the varying indoor environments comes from a novel episodic non-Markov localization. Service tasks requested by users are the input to a scheduler that can consider different types of constraints, including transfers among multiple robots. With symbiotic autonomy, the CoBots proactively seek external sources of help to fill-in for their inevitable occasional limitations. We present sampled results from a deployment and conclude with a brief review of other features of our service robots.

Distributed word representations have a rising interest in NLP community. Most of existing models assume only one vector for each individual word, which ignores polysemy and thus degrades their effectiveness for downstream tasks. To address this problem, some recent work adopts multi-prototype models to learn multiple embeddings per word type. In this paper, we distinguish the different senses of each word by their latent topics. We present a general architecture to learn the word and topic embeddings efficiently, which is an extension to the Skip-Gram model and can model the interaction between words and topics simultaneously. The experiments on the word similarity and text classification tasks show our model outperforms state-of-the-art methods.

Motivated by a radically new peer review system that the National Science Foundation recently experimented with, we study peer review systems in which proposals are reviewed by PIs who have submitted proposals themselves. An (m,k)-selection mechanism asks each PI to review m proposals, and uses these reviews to select (at most) k proposals. We are interested in impartial mechanisms, which guarantee that the ratings given by a PI to others' proposals do not affect the likelihood of the PI's own proposal being selected. We design an impartial mechanism that selects a k-subset of proposals that is nearly as highly rated as the one selected by the non-impartial (abstract version of) the NSF pilot mechanism, even when the latter mechanism has the "unfair" advantage of eliciting honest reviews.

We propose a graph-based method for extractive single-document summarization which considers importance, non-redundancy and local coherence simultaneously. We represent input documents by means of a bipartite graph consisting of sentence and entity nodes. We rank sentences on the basis of importance by applying a graph-based ranking algorithm to this graph and ensure non-redundancy and local coherence of the summary by means of an optimization step. Our graph based method is applied to scientific articles from the journal PLOS Medicine. We use human judgements to evaluate the coherence of our summaries. We compare ROUGE scores and human judgements for coherence of different systems on scientific articles. Our method performs considerably better than other systems on this data. Also, our graph-based summarization technique achieves state-of-the-art results on DUC 2002 data. Incorporating our local coherence measure always achieves the best results.

Information Aesthetics is an emerging sub-field of Data Visualization that aims to engage the viewers and lure them into decode the visualization. The introduction of self-organising systems can aid the creation of these visual representations through the exploration of emergent patterns. In this paper, we apply a swarm based system as a method to create emergent visualizations of data that convey meaningful information in an inciting way, exploring the boundaries between Data Visualization and Information Aesthetics. The approach is used to visually convey the consumption patterns in 729 Portuguese hypermarkets over the course of two years. The analysis of the experimental results focuses on the ability of the emergent visualizations to communicate information while engaging the viewer with organic visuals.

Qualitative spatial reasoning deals with relational spatial knowledge and with how this knowledge can be processed efficiently. Identifying suitable representations for spatial knowledge and checking whether the given knowledge is consistent has been the main research focus in the past two decades. However, where the spatial information comes from, what kind of information can be obtained and how it can be obtained has been largely ignored. This paper is an attempt to start filling this gap. We present a method for extracting detailed spatial information from sensor measurements of regions. We analyse how different sparse sensor measurements can be integrated and what spatial information can be extracted from sensor measurements. Different from previous approaches to qualitative spatial reasoning, our method allows us to obtain detailed information about the internal structure of regions. The result has practical implications, for example, in disaster management scenarios, which include identifying the safe zones in bushfire and flood regions.

In many POMDP applications in computational sustainability, it is important that the computed policy have a simple description, so that it can be easily interpreted by stakeholders and decision makers.  One measure of simplicity for POMDP value functions is the number of alpha-vectors required to represent the value function. Existing POMDP methods seek to optimize the accuracy of the value function, which can require a very large number of alpha-vectors. This paper studies methods that allow the user to explore the tradeoff between the accuracy of the value function and the number of alpha-vectors.  Building on previous point-based POMDP solvers, this paper introduces a new algorithm (alpha-min) that formulates a Mixed Integer Linear Program (MILP) to calculate approximate solutions for finite-horizon POMDP problems with limited numbers of alpha-vectors. At each time-step, alpha-min calculates alpha-vectors to greedily minimize the gap between current upper and lower bounds of the value function. In doing so, good upper and lower bounds are quickly reached allowing a good approximation of the problem with few alpha-vectors . Experimental results show that alpha-min provides good approximate solutions given a fixed number of alpha-vectors on small benchmark problems, on a larger randomly generated problem, as well as on a computational sustainability problem to best manage the endangered Sumatran tiger.

Many e-commerce systems allow users to express their opinions towards products through user reviews systems. The user generated reviews not only help other users to gain a more insightful view of the products, but also help online businesses to make targeted improvements on the products or services. Besides, they compose the key component of various personalized recommender systems. However, the existence of spam user accounts in the review systems introduce unfavourable disturbances into personalized recommendation by promoting or degrading targeted items intentionally through fraudulent reviews. Previous shilling attack detection algorithms usually deal with a specific kind of attacking strategy, and are exhausted to handle with the continuously emerging new cheating methods. In this work, we propose to conduct shilling attack detection for more informed recommendation by fraudulent action propagation on the reviews themselves, without caring about the specific underlying cheating strategy, which allows us a unified and flexible framework to detect the spam users.

This paper studies a new renewable energy investment model through crowdfunding, which is motivated by emerging community solar farms. In this paper we develop a sequential game theory model to capture the interactions among crowdfunders, the solar farm owner, and an electricity company who purchases renewable energy generated by the solar farm in a multi-period framework. By characterizing a unique subgame-perfect equilibrium, andcomparing it with a benchmark model without crowdfunding, we find that under crowdfunding although the farm owner reduces its investment level, the overall green energy investment level is increased due to the contribution of crowdfunders. We also find that crowdfunding can increase the penetration of green energy in consumption and thus reduce the energy procurement cost of the electricity company. Finally, the numerical results based on real data indicates crowdfunding is a simple but effective way to boost green generation.