Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Government (3)
- Technology (3)
- Digital Libraries (2)
- Active learning (1)
- Artificial Intelligence (1)
-
- Book Alignment (1)
- Centrality (1)
- Clustering (1)
- Content distribution network (1)
- Data mining (1)
- Database Applications (1)
- Database Management (1)
- DieHard (1)
- Document Quality (1)
- Document and Text processing (1)
- Electronic Publishing (1)
- Find-Similar (1)
- Graphical Models (1)
- Graphical models (1)
- Hash chain (1)
- Information Retrieval (1)
- Information Storage and Retrieval (1)
- Information extraction (1)
- Key distribution (1)
- Key regression (1)
- Key rotation (1)
- Knowledge discovery in graphs (1)
- Language Models (1)
- Lazy revocation (1)
- Manifolds (1)
- Publication
- Publication Type
Articles 31 - 40 of 40
Full-Text Articles in Entire DC Network
A Survey Of Practical Issues In Underwater Networks, Jim Partan
A Survey Of Practical Issues In Underwater Networks, Jim Partan
Computer Science Department Faculty Publication Series
No abstract provided.
Capacity Enhancement Using Throwboxes In Dtns, Wenrui Zhao, Yang Chen, Mostafa Ammar, Mark Corner, Brain Levine, Ellen Zegura
Capacity Enhancement Using Throwboxes In Dtns, Wenrui Zhao, Yang Chen, Mostafa Ammar, Mark Corner, Brain Levine, Ellen Zegura
Computer Science Department Faculty Publication Series
Disruption Tolerant Networks (DTNs) are designed to overcome limitations in connectivity due to conditions such as mobility, poor infrastructure, and short range radios. DTNs rely on the inherent mobility in the network to deliver packets around frequent and extended network partitions using a store-carry-andforward paradigm. However, missed contact opportunities decrease throughput and increase delay in the network.We propose the use of throwboxes in mobile DTNs to create a greater number of contact opportunities, consequently improving the performance of the network. Throwboxes are wireless nodes that act as relays, creating additional contact opportunities in the DTN. We propose algorithms to deploy …
Fast Direct Policy Evaluation Using Multiscale Analysis Of Markov Diffusion Processes, Mauro Maggioni, Sridhar Mahadevan
Fast Direct Policy Evaluation Using Multiscale Analysis Of Markov Diffusion Processes, Mauro Maggioni, Sridhar Mahadevan
Computer Science Department Faculty Publication Series
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3) to directly solve the Bellman system of |S| linear equations (where |S| is the state space size in the discrete case, and the sample size in the continuous case). In this paper we apply a recently introduced multiscale framework for analysis on graphs to design a faster algorithm for policy evaluation. For a fixed policy π, this framework efficiently constructs a multiscale decomposition of the random walk P¼ associated with the policy π. This enables efficiently computing medium and long term …
Socially Guided Machine Learning, Andrea Lockerd Thomaz, Cynthia Breazeal, Andrew G. Barto, Rosalind Picard
Socially Guided Machine Learning, Andrea Lockerd Thomaz, Cynthia Breazeal, Andrew G. Barto, Rosalind Picard
Computer Science Department Faculty Publication Series
Social interaction will be key to enabling robots and machines in general to learn new tasks from ordinary people (not experts in robotics or machine learning). Everyday people who need to teach their machines new things will find it natural for to rely on their interpersonal interaction skills. This thesis provides several contributions towards the understanding of this Socially GuidedMachine Learning scenario. While the topic of human input to machine learning algorithms has been explored to some extent, prior works have not gone far enough to understand what people will try to communicate when teaching a machine and how algorithms …
Oasis: An Overlayaware, Harsha V. Madhyastha
Oasis: An Overlayaware, Harsha V. Madhyastha
Computer Science Department Faculty Publication Series
Overlays have enabled several new and popular distributed applications such as Akamai, Kazaa, and Bittorrent. However, the lack of an overlay-aware network stack has hindered the widespread use of general purpose overlay packet delivery services [16, 29, 26]. In this paper, we describe the design and implementation of Oasis, a system and toolkit that enables legacy operating systems to access overlay-based packet delivery services. Oasis combines a set of ideas – network address translation, name resolution, packet capture, dynamic code execution – to provide greater user choice. We are in the process of making the Oasis toolkit available for public …
Proto-Transfer Learning In Markov Decision Processes Using Spectral Methods, Kimberly Ferguson, Sridhar Mahadevan
Proto-Transfer Learning In Markov Decision Processes Using Spectral Methods, Kimberly Ferguson, Sridhar Mahadevan
Computer Science Department Faculty Publication Series
In this paper we introduce proto-transfer leaning, a new framework for transfer learning. We explore solutions to transfer learning within reinforcement learning through the use of spectral methods. Proto-value functions (PVFs) are basis functions computed from a spectral analysis of random walks on the state space graph. They naturally lead to the ability to transfer knowledge and representation between related tasks or domains. We investigate task transfer by using the same PVFs in Markov decision processes (MDPs) with different rewards functions. Additionally, our experiments in domain transfer explore applying the Nyström method for interpolation of PVFs between MDPs of different …
Find-Similar: Similarity Browsing As A Search Tool, Mark D. Smucker, James Allan
Find-Similar: Similarity Browsing As A Search Tool, Mark D. Smucker, James Allan
Computer Science Department Faculty Publication Series
Search systems have for some time provided users with the ability to request documents similar to a given document. Interfaces provide this feature via a link or button for each document in the search results. We call this feature findsimilar or similarity browsing. We examined find-similar as a search tool, like relevance feedback, for improving retrieval performance. Our investigation focused on find-similar’s document-to-document similarity, the reexamination of documents during a search, and the user’s browsing pattern. Find-similar with a query-biased similarity, avoiding the reexamination of documents, and a breadth-like browsing pattern achieved a 23% increase in the arithmetic mean average …
An Intrinsic Reward Mechanism For Efficient Exploration, Özgür Şimşek, Andrew G. Barto
An Intrinsic Reward Mechanism For Efficient Exploration, Özgür Şimşek, Andrew G. Barto
Computer Science Department Faculty Publication Series
How should a reinforcement learning agent act if its sole purpose is to efficiently learn an optimal policy for later use? In other words, how should it explore, to be able to exploit later? We formulate this problem as a Markov Decision Process by explicitly modeling the internal state of the agent and propose a principled heuristic for its solution. We present experimental results in a number of domains, also exploring the algorithm’s use for learning a policy for a skill given its reward function—an important but neglected component of skill discovery.
Key Regression: Enabling Efficient Key Distribution For Secure Distributed Storage, Kevin Fu, Seny Kamara, Tadayoshi Kohno
Key Regression: Enabling Efficient Key Distribution For Secure Distributed Storage, Kevin Fu, Seny Kamara, Tadayoshi Kohno
Computer Science Department Faculty Publication Series
The Plutus file system introduced the notion of key rotation as a means to derive a sequence of temporally-related keys from the most recent key. In this paper we show that, despite natural intuition to the contrary, key rotation schemes cannot generically be used to key other crypto- graphic objects; in fact, keying an encryption scheme with the output of a key rotation scheme can yield a composite system that is insecure. To address these shortcomings, we introduce a new cryptographic object called a key regression scheme, and we propose three constructions that are provably secure under standard cryptographic assumptions. …
Diehard: Probabilistic Memory Safety For Unsafe Languages, Emery D. Berger
Diehard: Probabilistic Memory Safety For Unsafe Languages, Emery D. Berger
Computer Science Department Faculty Publication Series
Applications written in unsafe languages like C and C++ are vulnerable to memory errors such as buffer overflows, dangling pointers, and reads of uninitialized data. Such errors can lead to program crashes, security vulnerabilities, and unpredictable behavior. We present DieHard, a runtime system that tolerates these errors while probabilistically maintaining soundness. DieHard uses randomization and replication to achieve probabilistic memory safety by approximating an infinite-sized heap. DieHard’s memory manager randomizes the location of objects in a heap that is at least twice as large as required. This algorithm prevents heap corruption and provides a probabilistic guarantee of avoiding memory errors. …