Minimizing the number of reshuffling operations at maritime container terminals incorporates the Pre-Marshalling Problem (PMP) as an important problem. Based on an analysis of existing solution approaches we develop new heuristics utilizing specific properties of problem instances of the PMP. We show that the heuristic performance is highly dependent on these properties. We introduce a new method that exploits a greedy heuristic of four stages, where for each of these stages several different heuristics may be applied. Instead of using randomization to improve the performance of the heuristic, we repetitively generate a number of solutions by using a combination of different heuristics for each stage. In doing so, only a small number of solutions is generated for which we intend that they do not have undesirable properties, contrary to the case when simple randomization is used. Our experiments show that such a deterministic algorithm significantly outperforms the original nondeterministic method when the quality of found solutions is observed, with a much lower number of generated solutions.

The increased use of large corpora in narrative research has created new opportunities for empirical research and intelligent narrative technologies. To best exploit the value of these corpora, several research groups are eschewing complex discourse analysis techniques in favor of high-level minimalist narrative annotation schemes that can be quickly applied, achieve high inter-rater agreement, and are amenable to automation using machine-learning techniques. In this paper we compare different annotation schemes that have been employed by two groups of researchers to annotate large corpora of narrative text. Using a dual-annotation methodology, we investigate the correlation between narrative clauses distinguished by their structural role (orientation, action, evaluation), their subjectivity, and their narrative level within the discourse. We find that each simple narrative annotation scheme captures a structurally distinct characteristic of real-world narratives, and each combination of labels is evident in a corpus of 19 weblog narratives (951 narrative clauses). We discuss several potential applications of minimalist narrative annotation schemes, noting the combination of label across these two annotation schemes that best support each task.

Adaptation is a critical component of collaboration. Nevertheless, online learning is not yet used in most successful human-robot interactions, especially when the human's and robot's goals are not fully aligned. There are at least two barriers to the successful application of online learning in HRI. First, typical machine-learning algorithms do not learn at time scales that support effective interactions with people. Algorithms that learn at sufficiently fast time scales often produce myopic strategies that do not lead to good long-term collaborations. Second, random exploration, a core component of most online-learning algorithms, can be problematic for developing collaborative relationships with a human partner. We anticipate that a new genre of online-learning algorithms can overcome these two barriers when paired with (cheap-talk) communication. In this paper, we overview our efforts in these two areas to produce a situation-independent, learning system that quickly learns to collaborate with a human partner.

The tight long-run fit of the learning curve has led to its use as a tool to predict the future cost of solar panels. Nemet (2006) is skeptical of the view that learning has been an important driver of cost reduction, and uses data during 1975-2002 to show that increases in plant size has been the most important driver of reduction in cost per watt.

Wikipedia provides an enormous amount of background knowledge to reason about the semantic relatedness between two entities. We propose Wikipedia-based Distributional Semantics for Entity Relatedness (DiSER), which represents the semantics of an entity by its distribution in the high dimensional concept space derived from Wikipedia. DiSER measures the semantic relatedness between two entities by quantifying the distance between the corresponding high-dimensional vectors. DiSER builds the model by taking the annotated entities only, therefore it improves over existing approaches, which do not distinguish between an entity and its surface form. We evaluate the approach on a benchmark that contains the relative entity relatedness scores for 420 entity pairs. Our approach improves the accuracy by 12% on state of the art methods for computing entity relatedness. We also show an evaluation of DiSER in the Entity Disambiguation task on a dataset of 50 sentences with highly ambiguous entity mentions. It shows an improvement of 10% in precision over the best performing methods. In order to provide the resource that can be used to find out all the related entities for a given entity, a graph is constructed, where the nodes represent Wikipedia entities and the relatedness scores are reflected by the edges. Wikipedia contains more than 4.1 millions entities, which required efficient computation of the relatedness scores between the corresponding 17 trillions of entity-pairs.

One of the most popular uses of crowdsourcing is to provide training data for supervised machine learning algorithms. Since human annotators often make errors, requesters commonly ask multiple workers to label each example.  But is this strategy always the most cost effective use of crowdsourced workers? We argue "No" --- often classifiers can achieve higher accuracies when trained with noisy "unilabeled" data. However, in some cases relabeling is extremely important.  We discuss three factors that may make relabeling an effective strategy: classifier expressiveness, worker accuracy, and budget.

An ideal crowdsourcing or citizen-science system would route tasks to the most appropriate workers, but the best assignment is unclear because workers have varying skill, tasks have varying difficulty, and assigning several workers to a single task may significantly improve output quality. This paper defines a space of task routing problems, proves that even the simplest is NP-hard, and develops several approximation algorithms for parallel routing problems. We show that an intuitive class of requesters' utility functions is submodular, which lets us provide iterative methods for dynamically allocating batches of tasks that make near-optimal use of available workers in each round. Experiments with live oDesk workers show that our task routing algorithm uses only 48% of the human labor compared to the commonly used round-robin strategy. Further, we provide versions of our task routing algorithm which enable it to scale to large numbers of workers and questions and to handle workers with variable response times while still providing significant benefit over common baselines.

We describe a system which monitors the performance of labor channels within a crowdsourcing platform in an online manner. This allows us to automatically determine if and when to switch between labor channels in order to improve overall performance of crowd tasks.

Task starvation leads to huge variation in the completion times of the tasks posted on to the crowd. The price offered to a given task together with the dynamics of the crowd at the time of posting affect its completion time. Large organizations/requesters who frequent the crowd at regular intervals in order to get their tasks done desire predictability in completion times of the tasks. Thus, such requesters have to take into account the crowd dynamics at the time of posting the tasks and price them accordingly. In this work, we study an instance of the pricing problem and propose a solution based on the framework of Markov Decision Processes (MDPs).

In this talk, I will describe efforts being undertaken in a collaboration between human rights advocates and Social media and mobile phones with good cameras and computer scientists at Carnegie Mellon University to Internet access are dramatically changing the nature of develop tools, methods and algorithms that will make it human rights documentation, reporting and advocacy. Key to this process, and like YouTube, Live Leak, Vimeo, and Facebook every apropos of this session, is the development of mechanisms week. In Syria, more than 650,000 videos have been to enable "the crowd" (i.e., those individuals around the uploaded to social media sites since the conflict started world who care about human rights and have relevant three years ago. This trove of interest dies down or moves on to new issues or places. In presenting this relevant in the long-term, what is irrelevant to the project, I hope to get feedback from other participants in situation or repetitive, and what is patently false or the workshop on how to achieve this goal, particularly by misleading.