Carnegie Mellon University

About: Carnegie Mellon University is a education organization based out in Pittsburgh, Pennsylvania, United States. It is known for research contribution in the topics: Computer science & Robot. The organization has 36317 authors who have published 104359 publications receiving 5975734 citations. The organization is also known as: CMU & Carnegie Mellon.

The kindness of strangers: The usefulness of electronic weak ties for technical advice.

Abstract: People use weak ties—relationships with acquaintances or strangers—to seek help unavailable from friends or colleagues. Yet in the absence of personal relationships or the expectation of direct reciprocity, help from weak ties might not be forthcoming or could be of low quality. We examined the practice of distant employees (strangers) exchanging technical advice through a large organizational computer network. A survey of advice seekers and those who replied was conducted to test hypotheses about the viability and usefulness of such electronic weak tie exchanges. Theories of organizational motivation suggest that positive regard for the larger organization can substitute for direct incentives or personal relationships in motivating people to help others. Theories of weak ties suggest that the usefulness of this help may depend on the number of ties, the diversity of ties, or the resources of help providers. We hypothesized that, in an organizational context, the firm-specific resources and organizational...

Automated generation and analysis of attack graphs

Abstract: An integral part of modeling the global view of network security is constructing attack graphs. Manual attack graph construction is tedious, error-prone, and impractical for attack graphs larger than a hundred nodes. In this paper we present an automated technique for generating and analyzing attack graphs. We base our technique on symbolic model checking algorithms, letting us construct attack graphs automatically and efficiently. We also describe two analyses to help decide which attacks would be most cost-effective to guard against. We implemented our technique in a tool suite and tested it on a small network example, which includes models of a firewall and an intrusion detection system.

KenLM: Faster and Smaller Language Model Queries

Abstract: We present KenLM, a library that implements two data structures for efficient language model queries, reducing both time and memory costs. The Probing data structure uses linear probing hash tables and is designed for speed. Compared with the widely-used SRILM, our Probing model is 2.4 times as fast while using 57% of the memory. The Trie data structure is a trie with bit-level packing, sorted records, interpolation search, and optional quantization aimed at lower memory consumption. Trie simultaneously uses less memory than the smallest lossless baseline and less CPU than the fastest baseline. Our code is open-source, thread-safe, and integrated into the Moses, cdec, and Joshua translation systems. This paper describes the several performance techniques used and presents benchmarks against alternative implementations.

Sampling from large graphs

Abstract: Given a huge real graph, how can we derive a representative sample? There are many known algorithms to compute interesting measures (shortest paths, centrality, betweenness, etc.), but several of them become impractical for large graphs. Thus graph sampling is essential.The natural questions to ask are (a) which sampling method to use, (b) how small can the sample size be, and (c) how to scale up the measurements of the sample (e.g., the diameter), to get estimates for the large graph. The deeper, underlying question is subtle: how do we measure success?.We answer the above questions, and test our answers by thorough experiments on several, diverse datasets, spanning thousands nodes and edges. We consider several sampling methods, propose novel methods to check the goodness of sampling, and develop a set of scaling laws that describe relations between the properties of the original and the sample.In addition to the theoretical contributions, the practical conclusions from our work are: Sampling strategies based on edge selection do not perform well; simple uniform random node selection performs surprisingly well. Overall, best performing methods are the ones based on random-walks and "forest fire"; they match very accurately both static as well as evolutionary graph patterns, with sample sizes down to about 15% of the original graph.