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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Graph-Based Intrusion Detection System for Controller Area Networks
The controller area network (CAN) is the most widely used intra-vehicular communication network in the automotive industry. Because of its simplicity in design, it lacks most of the requirements needed for a security-proven ... -
The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data
With the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has ... -
Practical Cross-modal Manifold Alignment for Grounded Language
We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items. Our approach learns these embeddings ... -
Heterogeneous Federated Learning
Federated learning learns from scattered data by fusing collaborative models from local nodes. However, due to chaotic information distribution, the model fusion may suffer from structural misalignment with regard to ... -
Quantum Approximate Optimization for Hard Problems in Linear Algebra
The Quantum Approximate Optimization Algorithm (QAOA) by Farhi et al. is a quantum computational framework for solving quantum or classical optimization tasks. Here, we explore using QAOA for Binary Linear Least Squares ... -
Green-PoW: An Energy-Efficient Blockchain Proof-of-Work Consensus Algorithm
This paper opts to mitigate the energy-inefficiency of the Blockchain Proof-of-Work (PoW) consensus algorithm by rationally repurposing the power spent during the mining process. The original PoW mining scheme is designed ... -
Evolving schema representations in orbitofrontal ensembles during learning
(Nature, 2020-12-23)How do we learn about what to learn about? Specifically, how do the neural elements in our brain generalize what has been learned in one situation to recognize the common structure of—and speed learning in—other, similar ... -
No Silver Bullet: Fighting Russian Disinformation Requires Multiple Actions
(National Cryptologic Museum Foundation, 2020-08) -
COVID-19 Literature Clustering
(2020-04-03) -
Aspect Based Abusive Sentiment Detection in Nepali Social Media Texts
With the increase in internet access and the ease of writing comments in the Nepali language, fine-grained sentiment analysis of social media comments is becoming more and more pertinent. There are a number of benchmarked ... -
Nanocomposites for low dose gamma-ray sensor: Effect of matrix and oxidizer on the performance
(SPIE, 2019-05-14)Synthesis and crystal growth of scintillators and semiconductor materials for radiation detectors have been proven to be time consuming and very costly. Several alternative crystals such as Tl3ASSe3, TlGaSe2, Tl4HgI6, ... -
Gaia Early Data Release 3. Acceleration of the solar system from Gaia astrometry
(European Space Agency, 2020-12-02)Context. Gaia Early Data Release 3 (Gaia EDR3) provides accurate astrometry for about 1.6 million compact (QSO-like) extragalactic sources, 1.2 million of which have the best-quality five-parameter astrometric solutions. ... -
Gaia Early Data Release 3. Summary of the contents and survey properties
(EDP Sciences, 2020-11-03)Context. We present the early installment of the third Gaia data release, Gaia EDR3, consisting of astrometry and photometry for 1.8 billion sources brighter than magnitude 21, complemented with the list of radial velocities ... -
Neural Networks for Pulmonary Disease Diagnosis using Auditory and Demographic Information
(2020-11-26)Pulmonary diseases impact millions of lives globally and annually. The recent outbreak of the pandemic of the COVID-19, a novel pulmonary infection, has more than ever brought the attention of the research community to the ... -
Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization
(IEEE, 2020-12-08)As the attack surfaces of large enterprise networks grow, anomaly detection systems based on statistical user behavior analysis play a crucial role in identifying malicious activities. Previous work has shown that link ... -
Passive Encrypted IoT Device Fingerprinting with Persistent Homology
(OpenReview, 2020-12-09)Internet of things (IoT) devices are becoming increasingly prevalent. These devices can improve quality of life, but often present significant security risks to end users. In this work we present a novel persistent homology ... -
A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity
(IEEE, 2020-12-10)Named Entity Recognition (NER) is important in the cybersecurity domain. It helps researchers extract cyber threat information from unstructured text sources. The extracted cyber entities or key expressions can be used to ... -
Sampling Approach Matters: Active Learning for Robotic Language Acquisition
Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora. We present an exploration of active learning approaches applied to three grounded language ... -
Cluster Quality Analysis Using Silhouette Score
(IEEE, 2020-11-20)Clustering is an important phase in data mining. Selecting the number of clusters in a clustering algorithm, e.g. choosing the best value of k in the various k-means algorithms [1], can be difficult. We studied the use of ...
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