In this paper, we study the one-shot and lifelong versions of the Target Assignment and Path Finding problem in automated sortation centers, where each agent needs to constantly assign itself a sorting station, move to its assigned station without colliding with obstacles or other agents, wait in the queue of that station to obtain a parcel for delivery, and then deliver the parcel to a sorting bin. The throughput of such centers is largely determined by the total idle time of all stations since their queues can frequently become empty. To address this problem, we first formalize and study the one-shot version that assigns stations to a set of agents and finds collision-free paths for the agents to their assigned stations. We present efficient algorithms for this task based on a novel min-cost max-flow formulation that minimizes the total idle time of all stations in a fixed time window. We then demonstrate how our algorithms for solving the one-shot problem can be applied to solving the lifelong problem as well. Experimentally, we believe to be the first researchers to consider real-world automated sortation centers using an industrial simulator with realistic data and a kinodynamic model of real robots. On this simulator, we showcase the benefits of our algorithms by demonstrating their efficiency and effectiveness for up to 350 agents.

Crowdsourced query processing is an emerging processing technique that tackles computationally challenging problems by human intelligence. The basic idea is to decompose a computationally challenging problem into a set of human friendly microtasks (e.g., pairwise comparisons) that are distributed to and answered by the crowd. The solution of the problem is then computed (e.g., by aggregation) based on the crowdsourced answers to the microtasks. In this work, we attempt to revisit the crowdsourced processing of the top-k queries, aiming at (1) securing the quality of crowdsourced comparisons by a certain confidence level and (2) minimizing the total monetary cost. To secure the quality of each paired comparison, we employ two statistical tools, Student's t-distribution estimation and Stein's estimation, to estimate the confidence interval of the underlying mean value, which is then used to draw a conclusion to the comparison. Based on the pairwise comparison process, we attempt to minimize the monetary cost of the top-k processing within a Select-Partition-Rank framework. Our experiments, conducted on four real datasets, demonstrate that our stochastic method outperforms other existing top-k processing techniques by a visible difference.

Ranking techniques have been widely used in ubiquitous applications like recommendation, information retrieval, etc. For ranking computation hostile but human friendly items, crowdsourcing is considered as an emerging technique to process the ranking by human power. However, there is a lack of an easy-to-use toolkit for answering crowdsourced top- k query with minimal effort. In this work, we demonstrate an interactive programming toolkit that is a unified solution for answering the crowd-sourced top- k queries. The toolkit employs a new confidence-aware crowdsourced top- k algorithm, SPR. The whole progress of the algorithm is monitored and visualized to end users in a timely manner. Besides the visualized result and the statistics, the system also reports the estimation of the monetary cost and the breakdown of each phase. Based on the estimation, end users can strike a balance between the budget and the quality through the interface of this toolkit.

Shortest path distance retrieval is a core component in many important applications. For a decade, hub labeling (HL) techniques have been considered as a practical solution with fast query response time (e.g., 1--3 orders of magnitude faster), competitive indexing time, and slightly larger storage overhead (e.g., several times larger). These techniques enhance query throughput up to hundred thousands queries per second, which is particularly helpful in large user environment. Despite the importance of HL techniques, we are not aware of any comprehensive experimental study on HL techniques. Thus it is difficult for a practitioner to adopt HL techniques for her applications. To address the above issues, we provide a comprehensive experimental study on the state-of-the-art HL technique with analysis of their efficiency, effectiveness and applicability. From insightful summary of different HL techniques, we further develop a simple yet effective HL techniques called Significant path based Hub Pushing (SHP) which greatly improves indexing time of previous techniques while retains good query performance. We also complement extensive comparisons between HL techniques and other shortest path solutions to demonstrate robustness and efficiency of HL techniques.

Peer reviewing is a standard process for assessing the quality of submissions at academic conferences and journals. A very important task in this process is the assignment of reviewers to papers. However, achieving an appropriate assignment is not easy, because all reviewers should have similar load and the subjects of the assigned papers should be consistent with the reviewers' expertise. In this paper, we propose a generalized framework for fair reviewer assignment. We first extract the domain knowledge from the reviewers' published papers and model this knowledge as a set of topics. Then, we perform a group assignment of reviewers to papers, which is a generalization of the classic Reviewer Assignment Problem (RAP), considering the relevance of the papers to topics as weights. We study a special case of the problem, where reviewers are to be found for just one paper (Journal Assignment Problem) and propose an exact algorithm which is fast in practice, as opposed to brute-force solutions. For the general case of having to assign multiple papers, which is too hard to be solved exactly, we propose a greedy algorithm that achieves a 1/2-approximation ratio compared to the exact solution. This is a great improvement compared to the 1/3-approximation solution proposed in previous work for the simpler coverage-based reviewer assignment problem, where there are no weights on topics. We theoretically prove the approximation bound of our solution and experimentally show that it is superior to the current state-of-the-art.

Peer reviewing is a widely accepted mechanism for assessing the quality of submitted articles to scientific conferences or journals. Conference management systems (CMS) are used by conference organizers to invite appropriate reviewers and assign them to submitted papers. Typical CMS rely on paper bids entered by the reviewers and apply simple matching algorithms to compute the paper assignment. In this paper, we demonstrate our Reviewer Assignment System (RAS), which has advanced features compared to broadly used CMSs. First, RAS automatically extracts the profiles of reviewers and submissions in the form of topic vectors. These profiles can be used to automatically assign reviewers to papers without relying on a bidding process, which can be tedious and error-prone. Second, besides supporting classic assignment models (e.g., stable marriage and optimal assignment), RAS includes a recently published assignment model by our research group, which maximizes, for each paper, the coverage of its topics by the profiles of its reviewers. The features of the demonstration include (1) automatic extraction of paper and reviewer profiles, (2) assignment computation by different models, and (3) visualization of the results by different models, in order to assess their effectiveness.