Computer Science, Dept. of

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https://jscholarship.library.jhu.edu/handle/1774.2/881

The JHU Department of Computer Science is a department within the Whiting School of Engineering. While studies and research cover a very wide area, there are five main research interests:

Algorithms – A core area and long-standing strength of the department, dating from before the department’s formation.
Robotics, Vision, and Graphics – Much of the research in these areas, which involve 3-D computer modeling, is done within the Center for Computer-Integrated Surgical Systems and Technology (CISST). Related research includes human-computer interaction, and shape recognition and shape matching.
Security – This is an incredibly broad area and research, focused within the JHU Information Security Institute, involves many aspects of computer and network security.
Systems – This core research area grapples with improving operating systems and data storage and defining higher standards for security evaluation.
Natural Language Processing – This concerns enabling computers to work more effectively with human languages, identifying input strings and corresponding output, defining correlations between text and speech, form and content, syntax and translations. The Center for Language and Speech Processing (CLSP) is centrally involved with this work.

Please see http://www.cs.jhu.edu/ for more information

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Now showing 1 - 20 of 30

Algorithms for Computing the Center of Area of a Convex Polygon
(Department of Computer Science, The Johns Hopkins University, 1988-10-17) Diaz, Matthew; O'Rourke, Joseph
Cascading Divide-and-Conquer: A Technique for Designing Parallel Algorithms
(Department of Computer Science, The Johns Hopkins University, 1988) Atallah, Mikhail J.; Cole, Richard; Goodrich, Michael T.
Classifying Network Protocol Implementation Versions: An OpenSSL Case Study
(2013-12-11T20:33:59Z) Rubin, Aviel D.; Green, Matthew; Checkoway, Stephen; Rushanan, Michael; Martin, Paul D.
A new technique is presented for identifying the implementation version number of software that is used for Internet communications. While many programs may exchange version numbers, oftentimes only a small subset of them send any information at all. Furthermore, they usually do not provide accurate details about which implementation is used. We use machine learning techniques to build a feature database and then apply this to network traffic to try to identify specific implementations on servers. We apply our technique to OpenSSL and report our results.
Design and Implementation of Views: Isolated Perspectives of a File System
(2010-04-20T12:45:01Z) Pagano, Matthew W.; Peterson, Zachary N.J.
We present Views, a file system architecture that provides isolation between system components for the purposes of access control, regulatory compliance, and sandboxing. Views allows for discrete I/O entities, such as users, groups, or processes, to have a logically complete yet fully isolated perspective (view) of the file system. This ensures that each entity’s file system activities only modify that entity’s view of the file system, but in a transparent fashion that does not limit or restrict the entity’s functionality. Views can therefore be used to monitor system activity based on user accounts for access control (as required by federal regulations such as HIPAA), provide a reliable sandbox for arbitrary applications without inducing any noticeable loss in performance, and enable traditional snapshotting functionality by manipulating and transplanting views as snapshots in time. Views’ architecture is designed to be file system independent, extremely easy to use and manage, and flexible in defining isolation and sharing polices. Our implementation of Views is built on ext3cow, which additionally provides versioning capabilities to all entities. Benchmarking results show that the performance of Views is nearly identical to other traditional file systems such as ext3.
Design and Implementation of Views: Isolated Perspectives of a File System for Regulatory Compliance
(2009-08-24T13:15:00Z) Pagano, Matthew W.; Peterson, Zachary N. J.
We present Views, a ﬁle system architecture designed to meet the role-based access control (RBAC) requirement of federal regulations, such as those in HIPAA. Views allows for discrete IO entities, such as users, groups or processes, to have a logically complete but isolated perspective of the ﬁle system. Entities may perform IO using the standard system call interface without affecting the views of other entities. Views is designed to be ﬁle system independent, extremely easy to use and manage, and ﬂexible in deﬁning isolation and sharing polices. Our implementation of Views is built on ext3cow, which additionally provides versioning capabilities to all entities. Preliminary results show the performance of Views is comparable with other traditional disk ﬁle systems.
Efficient Parallel Term Matching
(Department of Computer Science, The Johns Hopkins University, 1988-01) Delcher, Art; Kasif, Simon
Equivariant embeddings in euclidean space
(1957-03) Mostow, George Daniel
Evaluation of Diagonal Confidence-Weighted Learning on the KDD Cup 1999 Dataset for Network Intrusion Detection Systems
(2011-02-03T15:09:35Z) Pagano, Matthew W.
In this study, I evaluate the performance of diagonal Confidence-Weighted (CW) online linear classification on the KDD Cup 1999 dataset for network intrusion detection systems (NIDS). This is a compatible relationship due to the large number of instances in NIDS datasets, as well as the constantly changing feature distributions. CW learning achieves approximately 92% accuracy on the KDD dataset when optimized, which is higher than both Perceptron and the Passive-Aggressive algorithm. CW learning also achieves faster convergence rates than both of these algorithms. Moreover, the accuracy of CW learning on the KDD dataset is comparable to several batch-learning algorithms. This challenges the assumption that batch learning should always be used when feasible. Due to shortcomings of the KDD dataset, a full generalization of CW learning to additional NIDS environments cannot yet be made. Nonetheless, this study shows that there is great promise to applying CW learning to future NIDS research.
Extensions of representations of lie groups II
(1957-08) Mostow, George Daniel
Families of periodic solutions of systems having relatively invariant line integrals
(The Johns Hopkins University, 1954-06-20) Lewis, D.C.
Final report prepared under Contract No. AF 18(600)-1474
(1958-07) Mostow, George Daniel
iSeeYou: Disabling the MacBook Webcam Indicator LED
(2013-12-11T20:32:51Z) Brocker, Matthew; Checkoway, Stephen
The ubiquitous webcam indicator LED is an important privacy feature which provides a visual cue that the camera is turned on. We describe how to disable the LED on a class of Apple internal iSight webcams used in some versions of MacBook laptops and iMac desktops. This enables video to be captured without any visual indication to the user and can be accomplished entirely in user space by an unprivileged (non- root) application. The same technique that allows us to disable the LED, namely reprogramming the firmware that runs on the iSight, enables a virtual machine escape whereby malware running inside a virtual machine reprograms the camera to act as a USB Human Interface Device (HID) keyboard which executes code in the host operating system. We build two proofs-of-concept: (1) an OS X application, iSeeYou, which demonstrates capturing video with the LED disabled; and (2) a virtual machine escape that launches Terminal.app and runs shell commands. To defend against these and related threats, we build an OS X kernel extension, iSightDefender, which prohibits the modification of the iSight’s firmware from user space.
Locating Faults in a Constant Number of Parallel Testing Rounds (Preliminary Version)
(Department of Computer Science, The Johns Hopkins University, 1989) Beigel, Richard; Kosaraju, S. Rao; Sullivan, Gregory F.
Multi-Object Geodesic Active Contours (MOGAC): A Parallel Sparse-Field Algorithm for Image Segmentation
(2012-02-28T01:02:21Z) Taylor, Russell; Kazhdan, Michael; Lucas, Blake
An important task for computer vision systems is to segment adjacent structures in images without producing gaps or overlaps. Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel accuracy. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel accuracy. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the segmentation algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label image and unsigned distance field. The time complexity of the algorithm is shown to be O((M^d)/P) for M^d pixels and P processing units in dimension d={2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems.
A Note on the Computational Complexity of Symmetric Connectionist Networks
(Department of Computer Science, The Johns Hopkins University, 1988-01) Banerjee, Saibal; Kasif, Simon
On a conjecture of Montgomery
(1957-06) Mostow, George Daniel
On existence in the large of solutions of hyperbolic partial differential equations
(The Johns Hopkins University, 1960-07) Shanahan, John P.
On local and global properties of convex sets and hypersurfaces
(The Johns Hopkins University, 1959-11) Sacksteder, Richard
On the fundamental group of a homogeneous space
(1957-06) Mostow, George Daniel
On the perturbation of a periodic solution when the variational system has non-trivial periodic solutions
(The Johns Hopkins University, 1955-01-20) Lewis, D.C.

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