Data mining in large audio collections of dolphin signals

The study of dolphin cognition involves intensive research of animal vocal- izations recorded in the field. In this dissertation I address the automated analysis of audible dolphin communication. I propose a system called the signal imager that automatically discovers patterns in dolphin signals. These patterns are invariant to frequency shifts and time warping transformations. The discovery algorithm is based on feature learning and unsupervised time series segmentation using hidden Markov models. Researchers can inspect the patterns visually and interactively run com- parative statistics between the distribution of dolphin signals in different behavioral contexts. The required statistics for the comparison describe dolphin communication as a combination of the following models: a bag-of-words model, an n-gram model and an algorithm to learn a set of regular expressions. Furthermore, the system can use the patterns to automatically tag dolphin signals with behavior annotations. My results indicate that the signal imager provides meaningful patterns to the marine biologist and that the comparative statistics are aligned with the biologists’ domain knowledge.Ph.D

Algorithms Aside: Recommendation as the Lens of Life

In this position paper, we take the experimental approach of putting algorithms aside, and reflect on what recommenders would be for people if they were not tied to technology. By looking at some of the shortcomings that current recommenders have fallen into and discussing their limitations from a human point of view, we ask the question: if freed from all limitations, what should, and what could, RecSys be? We then turn to the idea that life itself is the best recommender system, and that people themselves are the query. By looking at how life brings people in contact with options that suit their needs or match their preferences, we hope to shed further light on what current RecSys could be doing better. Finally, we look at the forms that RecSys could take in the future. By formulating our vision beyond the reach of usual considerations and current limitations, including business models, algorithms, data sets, and evaluation methodologies, we attempt to arrive at fresh conclusions that may inspire the next steps taken by the community of researchers working on RecSys

Learning task outcome prediction for robot control from interactive environments

Abstract — In order to manage complex tasks such as cooking, future robots need to be action-aware and posses common sense knowledge. For example flipping a pancake requires a robot to know that a spatula has to be under a pancake in order to succeed. We present a novel approach for the extraction and learning of action and common sense knowledge, and developed a game using a robot-simulator with realistic physics for data acquisition. The game environment is a virtual kitchen, in which a user has to create a pancake by pouring pancake-mix on an oven and flipping it using a spatula. The interaction is done by controlling a virtual robot hand with a 3D input sensor. We incorporate a realistic fluid simulation in order to gather appropriate data of the pouring action. Furthermore, we present a task outcome prediction algorithm for this specific system and show how to learn a failure model for the pouring and flipping action. I

RecSys Challenge 2017: Offline and Online Evaluation

The ACM Recommender Systems Challenge 20171 focused on the problem of job recommendations: given a new job advertisement, the goal was to identify those users who are both (a) interested in getting notified about the job advertisement, and (b) appropriate candidates for the given job. Participating teams had to balance between user interests and requirements for the given job as well as dealing with the cold-start situation. For the first time in the history of the conference, the RecSys challenge offered an online evaluation: teams first had to compete as part of a traditional offline evaluation and the top 25 teams were then invited to evaluate their algorithms in an online setting, where they could submit recommendations to real users. Overall, 262 teams registered for the challenge, 103 teams actively participated and submitted together more than 6100 solutions as part of the offline evaluation. Finally, 18 teams participated and rolled out recommendations to more than 900,000 users on XING2

An underwater wearable computer for two way human-dolphin communication experimentation

Copyright © 2013 ACMhttp://dx.doi.org/10.1145/2493988.2494346Research in dolphin cognition and communication in the wild is still a challenging task for marine biologists. Most problems arise from the uncontrolled nature of field studies and the challenges of building suitable underwater research equipment. We present a novel underwater wearable computer enabling researchers to engage in an audio-based interaction between humans and dolphins. The design requirements are based on a research protocol developed by a team of marine biologists associated with the Wild Dolphin Project

Data from: Diversification rates are more strongly related to microhabitat than climate in squamate reptiles (lizards and snakes)

Patterns of species richness among clades can be directly explained by the ages of clades or their rates of diversification. The factors that most strongly influence diversification rates remain highly uncertain, since most studies typically consider only a single predictor variable. Here, we explore the relative impacts of macroclimate (i.e., occurring in tropical vs. temperate regions) and microhabitat use (i.e., terrestrial, fossorial, arboreal, aquatic) on diversification rates of squamate reptile clades (lizards and snakes). We obtained data on microhabitat, macroclimatic distribution, and phylogeny for >4000 species. We estimated diversification rates of squamate clades (mostly families) from a time-calibrated tree, and used phylogenetic methods to test relationships between diversification rates and microhabitat and macroclimate. Across 72 squamate clades, the best-fitting model included microhabitat but not climatic distribution. Microhabitat explained ∼37% of the variation in diversification rates among clades, with a generally positive impact of arboreal microhabitat use on diversification, and negative impacts of fossorial and aquatic microhabitat use. Overall, our results show that the impacts of microhabitat on diversification rates can be more important than those of climate, despite much greater emphasis on climate in previous studies