UMBC Computer Science and Electrical Engineering Department
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The Computer Science and Electrical Engineering Department aims to maintain a program of excellence in teaching, research, and service for all of its programs. At the undergraduate level, we will provide students with a firm foundation of both the theory and practice of Computer Science and Computer Engineering. Our curricula also give students the social, ethical, and liberal education needed to make significant contributions to society. Students receiving a bachelor’s degree are ready to enter the work force as productive computer scientists or computer engineers, or to continue their education at the graduate or professional level.
At the graduate level, we are committed to developing the research and professional capabilities of students in Computer Science, Computer Engineering, Electrical Engineering and Cybersecurity. Our programs provide a deeper mastery of the basics of these fields, as well as opportunities to collaborate on leading-edge research with our faculty. Our faculty are engaged in both practical and theoretical research, often in partnership with government agencies, private industry and non-governmental organizations. The aim of this research is to advance knowledge within our disciplines and also to contribute to solving problems faced by our society.
Recent Submissions
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Gathering and Managing Facts for Intelligence Analysis
(2005-05)This paper presents a novel method, based on the Cyc Knowledge Base and Inference Engine, of gathering, organizing and sharing information about entities of interest (be they people, organizations, events or some other ... -
Knowledge Begets Knowledge: Steps towards Assisted Knowledge Acquisition in Cyc
(AAAI, 2005)The Cyc project is predicated on the idea that, in order to be effective and flexible, computer software must have an understanding of the context in which its tasks are performed. We believe this context is what is known ... -
Learning to Parse Natural Language Commands to a Robot Control System
(Springer Nature Switzerland AG., 2012-06)As robots become more ubiquitous and capable of performing complex tasks, the importance of enabling untrained users to interact with them has increased. In response, unconstrained natural-language interaction with robots ... -
A Joint Model of Language and Perception for Grounded Attribute Learning
(2012-06-27)As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them. Recently, this has led to study of the language grounding problem, where the goal is to ... -
On the Ability to Provide Demonstrations on a UAS: Observing 90 Untrained Participants Abusing a Flying Robot
(AAAI, 2015)This paper presents an exploratory study where participants piloted a commercial UAS (unmanned aerial system) through an obstacle course. The goal was to determine how varying the instructions given to participants affected ... -
Inferring Robot Morphology from Observation of Unscripted Movement
(IEEE, 2019-05)Task sharing between heterogeneous robots currently requires a priori capability knowledge, a shared communication protocol, or a centralized planner. However, in practice, when two robots are brought together, the effort ... -
¿Es un platano? Exploring the Application of a Physically Grounded Language Acquisition System to Spanish
(Association for Computational Linguistics, 2019-06)In this paper we describe a multilingual grounded language learning system adapted from an English-only system. This system learns the meaning of words used in crowd-sourced descriptions by grounding them in the physical ... -
Deep Learning for Category-Free Grounded Language Acquisition
(Association for Computational Linguistics (ACL), 2019-06)We propose a learning system in which language is grounded in visual percepts without pre-defined category constraints. We present a unified generative method to acquire a shared semantic/visual embedding that enables a ... -
Towards Hiding Adversarial Examples from Network Interpretation
(2018-12-06)Deep networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these ... -
Automated Detection of Substance Use-Related Social Media Posts Based on Image and Text Analysis
(IEEE, 2018-06-07)Nowadays, teens and young adults spend a significant amount of time on social media. According to the national survey of American attitudes on substance abuse, American teens who spend time on social media sites are at ... -
Shared Visualization Spaces for Environment to Environment Communication
(2009-01)In this paper, we will describe the creation of shared visualization spaces which provides a natural means of sharing, interacting and working with the collection of media artifacts, which may not exist in totality in any ... -
A Robust Free Size OCR for Omni-font Persian/Arabic Printed Document using Combined MLP/SVM
(Springer, Berlin, Heidelberg, 2005)Optical character recognition of cursive scripts present a number of challenging problems in both segmentation and recognition processes and this attracts many researches in the field of machine learning. This paper presents ... -
Learning visual biases from human imagination
(2015)Although the human visual system can recognize many concepts under challengingconditions, it still has some biases. In this paper, we investigate whether wecan extract these biases and transfer them into a machine recognition ... -
Visualizing Object Detection Features
(Springer US, 2016-03-01)We introduce algorithms to visualize feature spaces used by object detectors. Our method works by inverting a visual feature back to multiple natural images. We found that these visualizations allow us to analyze object ... -
Boosting Self-Supervised Learning via Knowledge Transfer
(IEEE, 2018-12-17)In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer this model to a target domain and task. ... -
DeepCAMP: Deep Convolutional Action & Attribute Mid-Level Patterns
(IEEE, 2016-12-12)The recognition of human actions and the determination of human attributes are two tasks that call for fine-grained classification. Indeed, often rather small and inconspicuous objects and features have to be detected to ... -
Opti-Acoustic Stereo Imaging, System Calibration and 3-D Reconstruction
(IEEE, 2007-07-16)Utilization of an acoustic camera for range measurements is a key advantage for 3-D shape recovery of underwater targets by opti-acoustic stereo imaging, where the associated epipolar geometry of optical and acoustic image ... -
Cross-Modal Scene Networks
(IEEE, 2017-09-18)People can recognize scenes across many different modalities beyond natural images. In this paper, we investigate how to learn cross-modal scene representations that transfer across modalities. To study this problem, we ... -
Are all training examples equally valuable
(2013-11-25)When learning a new concept, not all training examples may prove equally useful for training: some may have higher or lower training value than others. The goal of this paper is to bring to the attention of the vision ... -
Predicting Motivations of Actions by Leveraging Text
(IEEE, 2016-12-12)Understanding human actions is a key problem in computer vision. However, recognizing actions is only the first step of understanding what a person is doing. In this paper, we introduce the problem of predicting why a ...
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