Learning to locate from demonstrated searches

Abstract—We consider the problem of learning to locate targets from demonstrated searches. In this concept, a human demonstrates tours of environments that are assumed to minimize the human’s expected time to locate the target, given the person’s latent prior over potential target locations. The latent prior is then learned as a function of environmental features, enabling a robot to search novel environments in a way that would be deemed efficient by the teacher. We present novel approaches to solve both the inference problem of planning an expected-time-optimal tour given a prior and the learning problem of deducing the prior from observed tours. Our learning algorithm is inspired by and similar to maximum margin planning (MMP), although it differs in key ways. On the inference side, we advance the state-of-the-art by proposing novel relaxations that are integrated into a heuristic-driven search algorithm. An application to a home assistant scenario is discussed, and experimental results are given validating our methods in this domain. I

Rhesus monkeys use geometric and non geometric during a reorientation task

Rhesus monkeys (Macaca mulata) were subjected to a place finding task in a rectangular room perfectly homogeneous and without distinctive featural information. Results of Experiment 1 show that monkeys rely on the large-scale geometry of the room to retrieve a food reward. Experiments 2 and 3 indicate that subjects use also nongeometric information (colored wall) to reorient. Data of Experiments 4 and 5 suggest that monkeys do not use small angular cues but that they are sensitive to the size of the cues (Experiments 6, 7, and 8). Our findings strengthen the idea that a mechanism based on the geometry of the environment is at work in several mammalian species. In addition, the present data offer new perspectives on spatial cognition in animals that are phylogenetically close to humans. Specifically, the joint use of both geometric and landmark-based cues by rhesus monkeys tends to demonstrate that spatial processing became more flexible with evolutio

PinMe: Tracking a Smartphone User around the World

With the pervasive use of smartphones that sense, collect, and process valuable information about the environment, ensuring location privacy has become one of the most important concerns in the modern age. A few recent research studies discuss the feasibility of processing data gathered by a smartphone to locate the phone's owner, even when the user does not intend to share his location information, e.g., when the Global Positioning System (GPS) is off. Previous research efforts rely on at least one of the two following fundamental requirements, which significantly limit the ability of the adversary: (i) the attacker must accurately know either the user's initial location or the set of routes through which the user travels and/or (ii) the attacker must measure a set of features, e.g., the device's acceleration, for potential routes in advance and construct a training dataset. In this paper, we demonstrate that neither of the above-mentioned requirements is essential for compromising the user's location privacy. We describe PinMe, a novel user-location mechanism that exploits non-sensory/sensory data stored on the smartphone, e.g., the environment's air pressure, along with publicly-available auxiliary information, e.g., elevation maps, to estimate the user's location when all location services, e.g., GPS, are turned off.Comment: This is the preprint version: the paper has been published in IEEE Trans. Multi-Scale Computing Systems, DOI: 0.1109/TMSCS.2017.275146

Optimization Model for Planning Precision Grasps with Multi-Fingered Hands

Precision grasps with multi-fingered hands are important for precise placement and in-hand manipulation tasks. Searching precision grasps on the object represented by point cloud, is challenging due to the complex object shape, high-dimensionality, collision and undesired properties of the sensing and positioning. This paper proposes an optimization model to search for precision grasps with multi-fingered hands. The model takes noisy point cloud of the object as input and optimizes the grasp quality by iteratively searching for the palm pose and finger joints positions. The collision between the hand and the object is approximated and penalized by a series of least-squares. The collision approximation is able to handle the point cloud representation of the objects with complex shapes. The proposed optimization model is able to locate collision-free optimal precision grasps efficiently. The average computation time is 0.50 sec/grasp. The searching is robust to the incompleteness and noise of the point cloud. The effectiveness of the algorithm is demonstrated by experiments.Comment: Submitted to IROS2019, experiment on BarrettHand, 8 page

Towards memory supporting personal information management tools

In this article we discuss re-retrieving personal information objects and relate the task to recovering from lapse(s) in memory. We propose that fundamentally it is lapses in memory that impede users from successfully re-finding the information they need. Our hypothesis is that by learning more about memory lapses in non-computing contexts and how people cope and recover from these lapses, we can better inform the design of PIM tools and improve the user's ability to re-access and re-use objects. We describe a diary study that investigates the everyday memory problems of 25 people from a wide range of backgrounds. Based on the findings, we present a series of principles that we hypothesize will improve the design of personal information management tools. This hypothesis is validated by an evaluation of a tool for managing personal photographs, which was designed with respect to our findings. The evaluation suggests that users' performance when re-finding objects can be improved by building personal information management tools to support characteristics of human memory

Coping Skills for Students with ADHD

Students diagnosed with Attention Deficit/Hyperactivity Disorder (ADHD) are often seen as problem students with little hope for academic success. It is common for these students to be medicated with a daily dosage of stimulants to help them function more appropriately in the classroom. This meta-synthesis identifies multiple ways to work with students with ADHD; effective interventions can help students with ADHD cope with their disorder and become more successful students

Automatic document classification of biological literature

Background: Document classification is a wide-spread problem with many applications, from organizing search engine snippets to spam filtering. We previously described Textpresso, a text-mining system for biological literature, which marks up full text according to a shallow ontology that includes terms of biological interest. This project investigates document classification in the context of biological literature, making use of the Textpresso markup of a corpus of Caenorhabditis elegans literature. Results: We present a two-step text categorization algorithm to classify a corpus of C. elegans papers. Our classification method first uses a support vector machine-trained classifier, followed by a novel, phrase-based clustering algorithm. This clustering step autonomously creates cluster labels that are descriptive and understandable by humans. This clustering engine performed better on a standard test-set (Reuters 21578) compared to previously published results (F-value of 0.55 vs. 0.49), while producing cluster descriptions that appear more useful. A web interface allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept. Conclusions: We have demonstrated a simple method to classify biological documents that embodies an improvement over current methods. While the classification results are currently optimized for Caenorhabditis elegans papers by human-created rules, the classification engine can be adapted to different types of documents. We have demonstrated this by presenting a web interface that allows researchers to quickly navigate through the hierarchy and look for documents that belong to a specific concept

Embedding a curriculum-based information literacy programme at the University of Bedfordshire

This article describes the development of an information literacy programme that was embedded into the Psychology curriculum during 2007-2008. The programme was a collaboration between a faculty librarian and the Department of Psychology and utilized a blended learning approach along with a variety of teaching and assessment methods. This paper also reports on the initial findings from an ongoing evaluation assessing the impact of the programme on students' learning and information skills development. There had been an acknowledgement within the Department of Psychology and at broader University level of the importance of supporting students' and graduates' employability. Indeed, when the University undertook a curriculum redesign in 2008 (known as CRe8) the University recognized that 'there are four core skills areas at the core of 'graduateness' and employability that the University expects all courses to emphasise: communication; Information literacy; Research and evaluation; and creativity and critical thinking' (University of Bedfordshire, 2009). The development and implementation of an information literacy programme was therefore aligned closely with the University's goals at that time

Emancipated Youth Connections Project Final Report/Toolkit

This report presents findings and recommendations from the Emancipated Youth Connections Project, a model program designed to seek and sustain permanent lifelong connections for older youth who have already emancipated from foster care without a permanent connection to a caring adult. See "Part 3: Project Results" for project evaluation

Measuring the impact of temporal context on video retrieval

In this paper we describe the findings from the K-Space interactive video search experiments in TRECVid 2007, which examined the effects of including temporal context in video retrieval. The traditional approach to presenting video search results is to maximise recall by offering a user as many potentially relevant shots as possible within a limited amount of time. ‘Context’-oriented systems opt to allocate a portion of theresults presentation space to providing additional contextual cues about the returned results. In video retrieval these cues often include temporal information such as a shot’s location within the overall video broadcast and/or its neighbouring shots. We developed two interfaces with identical retrieval functionality in order to measure the effects of such context on user performance. The first system had a ‘recall-oriented’ interface, where results from a query were presented as a ranked list of shots. The second was ‘contextoriented’, with results presented as a ranked list of broadcasts. 10 users participated in the experiments, of which 8 were novices and 2 experts. Participants completed a number of retrieval topics using both the recall-oriented and context-oriented systems