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Developing Grounded Goals Through Instant Replay Learning, Lisa Meeden, Douglas S. Blank 2017 Swarthmore College

Developing Grounded Goals Through Instant Replay Learning, Lisa Meeden, Douglas S. Blank

Computer Science Faculty Research and Scholarship

This paper describes and tests a developmental architecture that enables a robot to explore its world, to find and remember interesting states, to associate these states with grounded goal representations, and to generate action sequences so that it can re-visit these states of interest. The model is composed of feed-forward neural networks that learn to make predictions at two levels through a dual mechanism of motor babbling for discovering the interesting goal states and instant replay learning for developing the grounded goal representations. We compare the performance of the model with grounded goal representations versus random goal representations, and find ...


Designing An Ai That Cares, 2017 Vocational Training Council

Designing An Ai That Cares

SIGNED: The Magazine of The Hong Kong Design Institute

The breakthrough that could change AI from being a plaything to being a playmate with which humans can have meaningful interations may be about to come from a seemingly unlikely source.


3½ Problems For Digital Assistants, 2017 Vocational Training Council

3½ Problems For Digital Assistants

SIGNED: The Magazine of The Hong Kong Design Institute

Digital home assistants promise to make life easier and happier. But a few problems stand in their way


Ai Centaurs, 2017 Vocational Training Council

Ai Centaurs

SIGNED: The Magazine of The Hong Kong Design Institute

As artificial intelligence outstrips human intelligence, AIs can now beat humans at any game. A pessimist might claim that this marks the end of the line for play. But a mythical beast has come to save playtime from the robots.


A Developmental Robotics Manifesto, Douglas Blank, James Marshall, Lisa Meeden 2017 Bryn Mawr College

A Developmental Robotics Manifesto, Douglas Blank, James Marshall, Lisa Meeden

Douglas Blank

We are largely in agreement with Tani’s approach to developmental robotics as elucidated in this dialog and his recent book. The basic assumptions inherent in his approach, such as that agents are embodied in the world and that neural systems are capable of complex learning, are now established wisdom. Although this has been a relatively recent shift in AI and Cognitive Science, we consider these underlying assumptions to be a given and thus do not address them further. Here we expand on Tani’s questions and offer a broader set of principles for guiding developmental robotics research.


Improving Speech Recognition For Interviews With Both Clean And Telephone Speech, Sung Woo Choi 2017 Minnesota State University, Mankato

Improving Speech Recognition For Interviews With Both Clean And Telephone Speech, Sung Woo Choi

Journal of Undergraduate Research at Minnesota State University, Mankato

High quality automatic speech recognition (ASR) depends on the context of the speech. Cleanly recorded speech has better results than speech recorded over telephone lines. In telephone speech, the signal is band-pass filtered which limits frequencies available for computation. Consequently, the transmitted speech signal may be distorted by noise, causing higher word error rates (WER). The main goal of this research project is to examine approaches to improve recognition of telephone speech while maintaining or improving results for clean speech in mixed telephone-clean speech recordings, by reducing mismatches between the test data and the available models. The test data includes ...


Comparing And Improving Facial Recognition Method, Brandon Luis Sierra 2017 California State University – San Bernardino

Comparing And Improving Facial Recognition Method, Brandon Luis Sierra

Electronic Theses, Projects, and Dissertations

Facial recognition is the process in which a sample face can be correctly identified by a machine amongst a group of different faces. With the never-ending need for improvement in the fields of security, surveillance, and identification, facial recognition is becoming increasingly important. Considering this importance, it is imperative that the correct faces are recognized and the error rate is as minimal as possible. Despite the wide variety of current methods for facial recognition, there is no clear cut best method. This project reviews and examines three different methods for facial recognition: Eigenfaces, Fisherfaces, and Local Binary Patterns to determine ...


Computer Vision Problems In 3d Plant Phenotyping, Ayan Chaudhury 2017 The University of Western Ontario

Computer Vision Problems In 3d Plant Phenotyping, Ayan Chaudhury

Electronic Thesis and Dissertation Repository

In recent years, there has been significant progress in Computer Vision based plant phenotyping (quantitative analysis of biological properties of plants) technologies. Traditional methods of plant phenotyping are destructive, manual and error prone. Due to non-invasiveness and non-contact properties as well as increased accuracy, imaging techniques are becoming state-of-the-art in plant phenotyping. Among several parameters of plant phenotyping, growth analysis is very important for biological inference. Automating the growth analysis can result in accelerating the throughput in crop production. This thesis contributes to the automation of plant growth analysis.

First, we present a novel system for automated and non-invasive/non-contact ...


Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal 2017 University of New Orleans, New Orleans

Machine Learning Based Protein Sequence To (Un)Structure Mapping And Interaction Prediction, Sumaiya Iqbal

University of New Orleans Theses and Dissertations

Proteins are the fundamental macromolecules within a cell that carry out most of the biological functions. The computational study of protein structure and its functions, using machine learning and data analytics, is elemental in advancing the life-science research due to the fast-growing biological data and the extensive complexities involved in their analyses towards discovering meaningful insights. Mapping of protein’s primary sequence is not only limited to its structure, we extend that to its disordered component known as Intrinsically Disordered Proteins or Regions in proteins (IDPs/IDRs), and hence the involved dynamics, which help us explain complex interaction within a ...


Comparison Of Visual Datasets For Machine Learning, Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung-Hsiang Lu, George K. Thiruvathukal, Mei-Ling Shyu, Shu-Ching Chen 2017 Purdue University

Comparison Of Visual Datasets For Machine Learning, Kent Gauen, Ryan Dailey, John Laiman, Yuxiang Zi, Nirmal Asokan, Yung-Hsiang Lu, George K. Thiruvathukal, Mei-Ling Shyu, Shu-Ching Chen

Computer Science: Faculty Publications and Other Works

One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new ...


Effects Of Anthropomorphism On Trust In Human-Robot Interaction, Keith R. MacArthur, William T. Shugars, Tracy L. Sanders, Peter A. Hancock 2017 University of Central Florida

Effects Of Anthropomorphism On Trust In Human-Robot Interaction, Keith R. Macarthur, William T. Shugars, Tracy L. Sanders, Peter A. Hancock

Keith Reid MacArthur

Robots are being integrated into everyday use, making the evaluation of trust in human-robot interactions (HRI) important to ensure their acceptance and correct usage (Lee & See, 2004; Parasuraman & Riley, 1997). Goetz, Kiesler, and Powers (2003) found that participants preferred robots with an anthropomorphic appearance appropriate for the social context of the task. This preference for robots with human-like appearance may be indicative of increased levels of trust and therefore, the present research evaluates the effects of anthropomorphism on trust.
Eighteen participants (Mage = 34.22, SDage = 10.55, n = 8 male, n =10 female) with subject matter expertise in ...


Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko 2017 Purdue University

Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko

The Summer Undergraduate Research Fellowship (SURF) Symposium

Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions using quantum-mechanical formulations. Our group has proposed previously that the effective fragment potential (EFP) method could serve as an efficient alternative to solve this problem. However, one of the computational bottlenecks of the EFP method is obtaining parameters for each molecule/fragment in the system, before the actual EFP simulations can be carried out. Here we present ...


Using Long-Short Term Memory Network To Train Machine Composing Baroque Fugue/Canon, Yihe Chen, Toshiro Kubota 2017 Susquehanna University

Using Long-Short Term Memory Network To Train Machine Composing Baroque Fugue/Canon, Yihe Chen, Toshiro Kubota

Landmark Conference Summer Research Symposium

The goal of this project is to train a machine to compose Baroque Fugue/Canon by using Long-Short Term Memory architecture (LSTM). LSTM is a type of artificial recursive neural network (RNN), which excels at learning patterns at both long and short time periods. By limiting to particular “styles” of structures and patterns, the problem becomes more tractable. In our study, we focus on the Baroque Fugue/Canons as they are polyphonic music with standard rules and structures to regulate the composing process. A 2-layer bi-directional LSTM network has been designed. With training data of midi files of Fugue/Canon ...


Synthesizing Pictures From Text Using A Dc-Gan, Anton Soloviev 2017 Susquehanna University

Synthesizing Pictures From Text Using A Dc-Gan, Anton Soloviev

Landmark Conference Summer Research Symposium

Generative Adversarial Text-to-Image Synthesis (Reed et al., 2016) is a model that can synthesize images based on given text – we have worked to try to apply to different data and to try to improve results seen in the original paper. The model performs two main tasks – it collects relevant information about the images to form a text feature representation of each of the images and it uses these learned text features to then synthesize images from given (new) text. To accomplish this, the model uses a DC-GAN (deep convolutional generative adversarial network) which has been conditioned on the text features ...


Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, Xiang Li 2017 The University of Western Ontario

Classification With Large Sparse Datasets: Convergence Analysis And Scalable Algorithms, Xiang Li

Electronic Thesis and Dissertation Repository

Large and sparse datasets, such as user ratings over a large collection of items, are common in the big data era. Many applications need to classify the users or items based on the high-dimensional and sparse data vectors, e.g., to predict the profitability of a product or the age group of a user, etc. Linear classifiers are popular choices for classifying such datasets because of their efficiency. In order to classify the large sparse data more effectively, the following important questions need to be answered.

1. Sparse data and convergence behavior. How different properties of a dataset, such as ...


An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert Ross 2017 Dublin Institute of Technology

An Analysis Of The Application Of Simplified Silhouette To The Evaluation Of K-Means Clustering Validity, Fei Wang, Hector-Hugo Franco-Penya, John D. Kelleher, John Pugh, Robert Ross

Conference papers

Silhouette is one of the most popular and effective internal measures for the evaluation of clustering validity. Simplified Silhouette is a computationally simplified version of Silhouette. However, to date Simplified Silhouette has not been systematically analysed in a specific clustering algorithm. This paper analyses the application of Simplified Silhouette to the evaluation of k-means clustering validity and compares it with the k-means Cost Function and the original Silhouette from both theoretical and empirical perspectives. The theoretical analysis shows that Simplified Silhouette has a mathematical relationship with both the k-means Cost Function and the original Silhouette, while empirically, we show that ...


Modeling Economic Systems As Locally-Constructive Sequential Games, Leigh Tesfatsion 2017 Iowa State University

Modeling Economic Systems As Locally-Constructive Sequential Games, Leigh Tesfatsion

Economics Working Papers

Real-world economies are open-ended dynamic systems consisting of heterogeneous interacting participants. Human participants are decision-makers who strategically take into account the past actions and potential future actions of other participants. All participants are forced to be locally constructive, meaning their actions at any given time must be based on their local states; and participant actions at any given time affect future local states. Taken together, these properties imply real-world economies are locally-constructive sequential games. This study discusses a modeling approach, agent-based computational economics (ACE), that permits researchers to study economic systems from this point of view. ACE modeling principles and ...


Routing And Scheduling For A Last-Mile Transportation System, Hai WANG 2017 Singapore Management University

Routing And Scheduling For A Last-Mile Transportation System, Hai Wang

Research Collection School Of Information Systems

The last-mile problem concerns the provision of travel services from the nearest public transportation node to a passenger’s home or other destination. We study the operation of an emerging last-mile transportation system (LMTS) with batch demands that result from the arrival of groups of passengers who desire last-mile service at urban metro stations or bus stops. Routes and schedules are determined for a multivehicle fleet of delivery vehicles, with the objective of minimizing passenger waiting time and riding time. An exact mixed-integer programming (MIP) model for LMTS operations is presented first, which is difficult to solve optimally within acceptable ...


Question Type Recognition Using Natural Language Input, Aishwarya Soni 2017 San Jose State University

Question Type Recognition Using Natural Language Input, Aishwarya Soni

Master's Projects

Recently, numerous specialists are concentrating on the utilization of Natural Language Processing (NLP) systems in various domains, for example, data extraction and content mining. One of the difficulties with these innovations is building up a precise Question and Answering (QA) System. Question type recognition is the most significant task in a QA system, for example, chat bots. Organization such as National Institute of Standards (NIST) hosts a conference series called as Text REtrieval Conference (TREC) series which keeps a competition every year to encourage and improve the technique of information retrieval from a large corpus of text. When a user ...


Travel Mode Identification With Smartphone Sensors, Xing Su 2017 The Graduate Center, City University of New York

Travel Mode Identification With Smartphone Sensors, Xing Su

All Graduate Works by Year: Dissertations, Theses, and Capstone Projects

Personal trips in a modern urban society typically involve multiple travel modes. Recognizing a traveller's transportation mode is not only critical to personal context-awareness in related applications, but also essential to urban traffic operations, transportation planning, and facility design. While the state of the art in travel mode recognition mainly relies on large-scale infrastructure-based fixed sensors or on individuals' GPS devices, the emergence of the smartphone provides a promising alternative with its ever-growing computing, networking, and sensing powers. In this thesis, we propose new algorithms for travel mode identification using smartphone sensors. The prototype system is built upon the ...


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