We present a graph-based semi-supervised learning (SSL) method for learning edge flows defined on a graph. Specifically, given flow measurements on a subset of edges, we want to predict the flows on the remaining edges. To this end, we develop a computational framework that imposes certain constraints on the overall flows, such as (approximate) flow conservation. These constraints render our approach different from classical graph-based SSL for vertex labels, which posits that tightly connected nodes share similar labels and leverages the graph structure accordingly to extrapolate from a few vertex labels to the unlabeled vertices. We derive bounds for our method's reconstruction error and demonstrate its strong performance on synthetic and real-world flow networks from transportation, physical infrastructure, and the Web. Furthermore, we provide two active learning algorithms for selecting informative edges on which to measure flow, which has applications for optimal sensor deployment. The first strategy selects edges to minimize the reconstruction error bound and works well on flows that are approximately divergence-free. The second approach clusters the graph and selects bottleneck edges that cross cluster-boundaries, which works well on flows with global trends.

The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans in their everyday life. While the concept of CIL has already been carved out in detail (including the fields of dedicated CIL and opportunistic CIL) and many research objectives have been stated, there is still the need to clarify some terms such as information, knowledge, and experience in the context of CIL and to differentiate CIL from recent and ongoing research in related fields such as active learning, collaborative learning, and others. Both aspects are addressed in this paper.

Argument mining is a rising area of Natural Language Pro- cessing (NLP) concerned with the automatic recognition and interpretation of argument components and their relations. Neural models are by now mature technologies to be ex- ploited for automating the argument mining tasks, despite the issue of data sparseness. This could ease much of the man- ual effort involved in these tasks, taking into account hetero- geneous types of texts and topics. In this work, we evaluate different attention mechanisms applied over a state-of-the-art architecture for sequence labeling. We assess the impact of different flavors of attention in the task of argument compo- nent detection over two datasets: essays and legal domain. We show that attention not models the problem better but also supports interpretability.

In a multiagent system or MAS, due to agent interactions, the agents as a group may make decisions that none of them would make alone; this phenomenon is called emergence. Emergence is characterized by an unanticipated system behavior caused by nonlinear interactions. This paper detects such emergence in a MAS by analyzing agent behaviors across two simple strategies. In the first strategy, agents make decisions based on the local information; in the second strategy, agents make decisions based on global information provided via communication. The proposed method identifies when and how nonlinear interactions cause behavior change, and quantitatively defines emergence based on the change in team performance. It then proves several theorems about emergence in a MAS. It also explores several emergence-related factors like the communication cost and the reward gap quantitatively. Experimental results on several benchmarks demonstrate the promising performance of the proposed framework in detecting emergence in a MAS.

Two-sided marketplaces such as eBay, Etsy and Taobao have two distinct groups of customers: buyers who use the platform to seek the most relevant and interesting item to purchase and sellers who view the same platform as a tool to reach out to their audience and grow their business. Additionally, platforms have their own objectives ranging from growing both buyer and seller user bases to revenue maximization. It is not difficult to see that it would be challenging to obtain a globally favorable outcome for all parties. Taking the search experience as an example, any interventions are likely to impact either buyers or sellers unfairly to course correct for a greater perceived need. In this paper, we address how a company-aligned search experience can be provided with competing business metrics that E-commerce companies typically tackle. As far as we know, this is a pioneering work to consider multiple different aspects of business indicators in two-sided marketplaces to optimize a search experience. We demonstrate that many problems are difficult or impossible to decompose down to credit assigned scores on individual documents, rendering traditional methods inadequate. Instead, we express market-level metrics as constraints and discuss to what degree multiple potentially conflicting metrics can be tuned to business needs. We further explore the use of policy learners in the form of Evolutionary Strategies to jointly optimize both group-level and market-level metrics simultaneously, side-stepping traditional cascading methods and manual interventions. We empirically evaluate the effectiveness of the proposed method on Etsy data and demonstrate its potential with insights.

The FLAIRS poster track is designed to promote discussion of emerging ideas and work in order to encourage and help guide researchers — especially new researchers — who are able to present a full poster in the conference poster session and receive that critical work-shaping feedback that helps guide good work into great work. Abstracts of those posters appear here, which we hope to see fully developed into future FLAIRS papers..

This paper presents the first results of the research into AI-based support of the room configuration process during the early design phases in architecture. Room configuration (also: room layout or space layout) is an essential stage of the initial design phase: its results are crucial for user-friendliness and success of the planned utilization of the architectural object. Our approach takes into account different possible actions of the configuration process, such as adding, removing, or (re)assigning of the room type. Its mode of operation is based on specific process chain clusters, where each cluster represents a contextual subset of previous configuration steps and provides a recurrent neural network trained on this cluster data only to suggest the next step, and a case base that is used to determine if the current process chain belongs to this cluster. The most similar cluster then tries to suggest the next step of the process. The approach is implemented in a distributed CBR framework for support of early conceptual design in architecture and was evaluated with a high number of process chain queries to prove its general suitability.

Semantic labeling of texts allows people and computing devices to more easily understand the meaning of a natural language sentence as a whole. It is very often one of the steps taken of procedures related to natural language processing. However, this step is often done manually, which is very expensive and time-consuming. When automatic labeling systems are employed, methods such as maximum entropy models are used, which receive as input features specified by specialists that also make the development of the system more expensive. In this article we present a model of the deep recurrent network that semantically annotates texts in English using as labels the top categories of an ontology. The tests showed that it is possible to obtain better results than the models that need the features to be made explicit.

The increasing use of social networks has made opinion mining an important field in the area of Natural Language Processing. The analysis of texts from the reader perspective tends to generate multi-label data since one can interpret the text using different contexts. In this paper, a new method for multi-label classification is proposed to identify reactions or emotions in texts. The new method uses data correlation to improve the class ensemble process used to create the classifiers. In addition to the new method, a new corpus of news written in Brazilian Portuguese labeled with user reactions is presented. Experiments performed with the new corpus and with two existing corpora have demonstrated that the proposed method generates statistically superior or equivalent results, requiring fewer classifiers or classes than traditional problem transformation methods.

A crucial point for ensemble learning systems is the capacity of making different errors on any given sample, which highlights the importance of diversity for ensemble-based decision systems. A usual way of increasing diversity is to combine traditional ensemble methods. Based on this context, we propose a novel combining-based algorithm of pool generation using a merging of bagging, random patches, and boosting techniques for ensemble regression problems. Numerical results indicate that, depending on both the dataset and the diversity measurement, our proposal generates a pool of regressors with more diversity when compared to single ensemble generator approaches.