This paper reports on the linguistic analysis of a tag set of nearly 50,000 tags collected as part of the steve.museum project. The tags describe images of objects in museum collections. We present our results on morphological, part of speech and semantic analysis. We demonstrate that deeper tag processing provides valuable information for organizing and categorizing social tags. This promises to improve access to museum objects by leveraging the characteristics of tags and the relationships between them rather than treating them as individual items. The paper shows the value of using deep computational linguistic techniques in interdisciplinary projects on tagging over images of objects in museums and libraries. We compare our data and analysis to Flickr and other image tagging projects.

Using these custom tools requires have been busily converting material from paper and microfilm training and a skilled workforce. We show in this paper that into digital domain. Newspapers, books, journals and some parts of that process can be distributed to a pool of even individual letters are finding themselves inside large unskilled volunteers with good results.

Being able to detect and recognize human activities is important for making personal assistant robots useful in performing assistive tasks. The challenge is to develop a system that is low-cost, reliable in unstructured home settings, and also straightforward to use. In this paper, we use a RGBD sensor (Microsoft Kinect) as the input sensor, and present learning algorithms to infer the activities. Our algorithm is based on a hierarchical maximum entropy Markov model (MEMM). It considers a person's activity as composed of a set of sub-activities, and infers the two-layered graph structure using a dynamic programming approach. We test our algorithm on detecting and recognizing twelve different activities performed by four people in different environments, such as a kitchen, a living room, an office, etc., and achieve an average performance of 84.3% when the person was seen before in the training set (and 64.2% when the person was not seen before).

Continual learning is the unending process of learning new things on top of what has already been learned (Ring, 1994).Temporal Transition Hierarchies (TTHs) were developed to allow prediction of Markov-k sequences in a way that was consistent with the needs of a continual-learning agent (Ring, 1993).However, the algorithm could not learn arbitrary temporal contingencies.This paper describes Recurrent Transition Hierarchies (RTH), a learning method that combines several properties desirable for agents that must learn as they go.In particular, it learns online and incrementally, autonomously discovering new features as learning progresses.It requires no reset or episodes.It has a simple learning rule with update complexity linear in the number of parameters.

The motor system organization shows some interesting parallels with the language organization. Here we draw the possible communalities between Action and Language, basing our claims on neurophysiological, neuroanatomical and neuroimaging data. Furthermore, we speculate that the motor system may have furnished the basic computational capabilities for the emergence of both semantics and syntax.

While microblogging has gained popularity on the Internet, analyzing and processing short messages has become a challenging task in natural language processing. This paper analyzes the differences between Internet short messages (or “microtext”) and general articles by comparing the Plurk Corpus and the Sinica Balanced Corpus. Likelihood ratio and the tóngyìcícílín thesaurus are adopted to analyze the lexical semantics of frequent terms in each corpus. Furthermore, the NTUSD sentiment dictionary is used to compare the sentiment distribution of the two corpora. The result is also applied to sentiment transition analysis.

Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data.

In this paper we present a principled planner based approach to the active visual object search problem in unknown environments. We make use of a hierarchical planner that combines the strength of decision theory and heuristics. Furthermore, our object search approach leverages on the conceptual spatial knowledge in the form of object co-occurrences and semantic place categorisation. A hierarchical model for representing object locations is presented with which the planner is able to perform indirect search. Finally we present real world experiments to show the feasibility of the approach.

On today's human computation systems, designing tasks and workflows is a difficult and labor-intensive process. Can workers from the crowd be used to help plan workflows? We explore this question with Turkomatic, a new interface to microwork platforms that uses crowd workers to help plan workflows for complex tasks. Turkomatic uses a general-purpose divide-andconquer algorithm to solve arbitrary natural-language requests posed by end users. The interface includes a novel real-time visual workflow editor that enables requesters to observe and edit workflows while the tasks are being completed. Crowd verification of work and the division of labor among members of the crowd can be handled automatically by Turkomatic, which substantially simplifies the process of using human computation systems. These features enable a novel means of interaction with crowds of online workers to support successful execution of complex work. Figure 1: Turkomatic harnesses crowds to plan and execute complex work requested in natural language.

Stackelberg games have recently gained significant attention for resource allocation decisions in security settings. One critical assumption of traditional Stackelberg models is that all players are perfectly rational and that the followers perfectly observe the leader’s strategy. However, in real-world security settings, security agencies must deal with human adversaries who may not always follow the utility maximizing rational strategy. Accounting for these likely deviations is important since they may adversely affect the leader’s (security agency’s) utility. In fact, a number of behavioral gametheoretic models have begun to emerge for these domains. Two such models in particular are COBRA (Combined Observability and Bounded Rationality Assumption) and BRQR (Best Response to Quantal Response), which have both been shown to outperform game-theoretic optimal models against human adversaries within a security setting based on Los Angeles International Airport (LAX). Under perfect observation conditions, BRQR has been shown to be the leading contender for addressing human adversaries. In this work we explore these models under limited observation conditions. Due to human anchoring biases, BRQR’s performance may suffer under limited observation conditions. An anchoring bias is when, given no information about the occurrence of a discrete set of events, humans will tend to assign an equal weight to the occurrence of each event (a uniform distribution). This study makes three main contributions: (i) we incorporate an anchoring bias into BRQR to improve performance under limited observation; (ii) we explore finding appropriate parameter settings for BRQR under limited observation; (iii) we compare BRQR’s performance versus COBRA under limited observation conditions.