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Articles 31 - 43 of 43

Full-Text Articles in Theory and Algorithms

Stochastic Multiple Gradient Decent For Inferring Action-Based Network Generators, Qian Wu, Viplove Arora, Mario Ventresca Aug 2016

Stochastic Multiple Gradient Decent For Inferring Action-Based Network Generators, Qian Wu, Viplove Arora, Mario Ventresca

The Summer Undergraduate Research Fellowship (SURF) Symposium

Networked systems, like the internet, social networks etc., have in recent years attracted the attention of researchers, specifically to develop models that can help us understand or predict the behavior of these systems. A way of achieving this is through network generators, which are algorithms that can synthesize networks with statistically similar properties to a given target network. Action-based Network Generators (ABNG)is one of these algorithms that defines actions as strategies for nodes to form connections with other nodes, hence generating networks. ABNG is parametrized using an action matrix that assigns an empirical probability distribution to vertices for choosing specific …

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Detection Of Cyberbullying In Sms Messaging, Bryan W. Bradley Jul 2016

Detection Of Cyberbullying In Sms Messaging, Bryan W. Bradley

Computer Science Summer Fellows

Cyberbullying is a type of bullying that uses technology such as cell phones to harass or malign another person. To detect acts of cyberbullying, we are developing an algorithm that will detect cyberbullying in SMS (text) messages. Over 80,000 text messages have been collected by software installed on cell phones carried by participants in our study. This paper describes the development of the algorithm to detect cyberbullying messages, using the cell phone data collected previously. The algorithm works by first separating the messages into conversations in an automated way. The algorithm then analyzes the conversations and scores the severity and …

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On Processing Reverse K-Skyband And Ranked Reverse Skyline Queries, Yunjun Gao, Qing Liu, Baihua Zheng, Mou Li, Gang Chen, Qing Li Feb 2015

On Processing Reverse K-Skyband And Ranked Reverse Skyline Queries, Yunjun Gao, Qing Liu, Baihua Zheng, Mou Li, Gang Chen, Qing Li

Research Collection School Of Computing and Information Systems

In this paper, for the first time, we identify and solve the problem of efficient reverse k-skyband (RkSB) query processing. Given a set P of multi-dimensional points and a query point q, an RkSB query returns all the points in P whose dynamic k-skyband contains q. We formalize RkSB retrieval, and then propose five algorithms for computing the RkSB of an arbitrary query point efficiently. Our methods utilize a conventional data-partitioning index (e.g., R-tree) on the dataset, and employ pre-computation, reuse and pruning techniques to boost the query efficiency. In addition, we extend our solutions to tackle an interesting variant …

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Beyond Search: Event-Driven Summarization For Web Videos, Richard Hong, Jinhui Tang, Hung-Khoon Tan, Chong-Wah Ngo, Shuicheng Yan, Tat-Seng Chua Nov 2011

Beyond Search: Event-Driven Summarization For Web Videos, Richard Hong, Jinhui Tang, Hung-Khoon Tan, Chong-Wah Ngo, Shuicheng Yan, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

The explosive growth of Web videos brings out the challenge of how to efficiently browse hundreds or even thousands of videos at a glance. Given an event-driven query, social media Web sites usually return a large number of videos that are diverse and noisy in a ranking list. Exploring such results will be time-consuming and thus degrades user experience. This article presents a novel scheme that is able to summarize the content of video search results by mining and threading "key" shots, such that users can get an overview of main content of these videos at a glance. The proposed …

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Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.) Jan 2011

Eeg Artifact Removal Using A Wavelet Neural Network, Hoang-Anh T. Nguyen, John Musson, Jiang Li, Frederick Mckenzie, Guangfan Zhang, Roger Xu, Carl Richey, Tom Schnell, Thomas E. Pinelli (Ed.)

Electrical & Computer Engineering Faculty Publications

In this paper we developed a wavelet neural network. (WNN) algorithm for Electroencephalogram (EEG) artifact removal without electrooculographic (EOG) recordings. The algorithm combines the universal approximation characteristics of neural network and the time/frequency property of wavelet. We compared the WNN algorithm with the ICA technique and a wavelet thresholding method, which was realized by using the Stein's unbiased risk estimate (SURE) with an adaptive gradient-based optimal threshold. Experimental results on a driving test data set show that WNN can remove EEG artifacts effectively without diminishing useful EEG information even for very noisy data.

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Reed-Solomon Codes Construction & Decoding, Samuel J. Parsons Dec 2007

Reed-Solomon Codes Construction & Decoding, Samuel J. Parsons

Honors Capstones

Capstone submitted as a graduation requirement for the BSU Honors Program.

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Modular Exponentiation Via The Explicit Chinese Remainder Theorem, Daniel J. Bernstein, Jonathan P. Sorenson Jan 2007

Modular Exponentiation Via The Explicit Chinese Remainder Theorem, Daniel J. Bernstein, Jonathan P. Sorenson

Scholarship and Professional Work - LAS

In this paper we consider the problem of computing xe mod m for large integers x, e, and m. This is the bottleneck in Rabin’s algorithm for testing primality, the Diffie-Hellman algorithm for exchanging cryptographic keys, and many other common algorithms.

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Fast Bounds On The Distribution Of Smooth Numbers, Scott T. Parsell, Jonathan P. Sorenson Jan 2006

Fast Bounds On The Distribution Of Smooth Numbers, Scott T. Parsell, Jonathan P. Sorenson

Scholarship and Professional Work - LAS

In this paper we present improvements to Bernstein’s algorithm, which finds rigorous upper and lower bounds for (x, y).

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The Pseudosquares Prime Sieve, Jonathan P. Sorenson Jan 2006

The Pseudosquares Prime Sieve, Jonathan P. Sorenson

Scholarship and Professional Work - LAS

We present the pseudosquares prime sieve, which finds all primes up to n.

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Predictive Adaptive Resonance Theory And Knowledge Discovery In Databases, Ah-Hwee Tan, Hui-Shin Vivien Soon May 2000

Predictive Adaptive Resonance Theory And Knowledge Discovery In Databases, Ah-Hwee Tan, Hui-Shin Vivien Soon

Research Collection School Of Computing and Information Systems

This paper investigates the scalability of predictive Adaptive Resonance Theory (ART) networks for knowledge discovery in very large databases. Although predictive ART performs fast and incremental learning, the number of recognition categories or rules that it creates during learning may become substantially large and cause the learning speed to slow down. To tackle this problem, we introduce an on-line algorithm for evaluating and pruning categories during learning. Benchmark experiments on a large scale data set show that on-line pruning has been effective in reducing the number of the recognition categories and the time for convergence. Interestingly, the pruned networks also …

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A Topology-Aware Collision Resolution Algorithm, Lewis Barnett Iii Mar 1994

A Topology-Aware Collision Resolution Algorithm, Lewis Barnett Iii

Department of Math & Statistics Technical Report Series

A new collision resolution algorithm called the Space Division Multiple Access protocol (SDMA) is presented. SDMA gains a performance advantage over similar protocols by using information about the positions of stations on the network. The protocol can operate asynchrononsly on a broadcast bus, allowing variable sized packet traffic. Through simulation the protocol is demonstrated to have better performance than Ethernet and the Capetanakis Tree protocol, a similar collision resolution protocol, under some traffic conditions. In particular, under heavy loads, SDMA displays better average throughput and lower variance of delay than Ethernet. The protocol demonstrates a performance bias based on the …

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A Greedy Hypercube-Labeling Algorithm, D. Bhagavathi, C. E. Grosch, S. Olariu Jan 1994

A Greedy Hypercube-Labeling Algorithm, D. Bhagavathi, C. E. Grosch, S. Olariu

Computer Science Faculty Publications

Due to its attractive topological properties, the hypercube multiprocessor has emerged as one of the architectures of choice when it comes to implementing a large number of computational problems. In many such applications, Gray-code labelings of the hypercube are a crucial prerequisite for obtaining efficient algorithms. We propose a greedy algorithm that, given an n-dimensional hypercube H with N=22 nodes, returns a Gray-code labeling of H, that is, a labeling of the nodes with binary strings of length n such that two nodes are neighbors in the hypercube if, and only if, their labels differ in exactly …

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A Fast Serial Algorithm For The Finite Temperature Quenched Potts Model, Gregory N. Hassold, Elizabeth A. Holm Jan 1993

A Fast Serial Algorithm For The Finite Temperature Quenched Potts Model, Gregory N. Hassold, Elizabeth A. Holm

Physics Publications

An efficient serial algorithm for finite temperature, quenched Potts model simulations of domain evolution has been developed. This ''n‐fold way'' algorithm eliminates unsuccessful spin flip attempts a priori by flipping sites with a frequency proportional to their site activity, defined as the sum of the probability of success for every possible spin flip at that site. Finite temperature efficiency for high‐spin degeneracy systems is achieved by utilizing a new, analytical expression for the portion of the site activity due to flips to non-neighbor spin values. Hence, to determine the activity of a site, only flips to the …

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