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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Self-Stabilizing Token Distribution With Constant-Space For Trees, Yuichi Sudo, Ajoy K. Datta, Lawrence L. Larmore, Toshimitsu Masuzawa
Self-Stabilizing Token Distribution With Constant-Space For Trees, Yuichi Sudo, Ajoy K. Datta, Lawrence L. Larmore, Toshimitsu Masuzawa
Computer Science Faculty Research
Self-stabilizing and silent distributed algorithms for token distribution in rooted tree networks are given. Initially, each process of a graph holds at most l tokens. Our goal is to distribute the tokens in the whole network so that every process holds exactly k tokens. In the initial configuration, the total number of tokens in the network may not be equal to nk where n is the number of processes in the network. The root process is given the ability to create a new token or remove a token from the network. We aim to minimize the convergence time, the number …
Loosely-Stabilizing Leader Election With Polylogarithmic Convergence Time, Yuichi Sudo, Fukuhito Ooshita, Hirotsugu Kukugawa, Toshimitsu Masuzawa, Ajoy K. Datta, Lawrence L. Larmore
Loosely-Stabilizing Leader Election With Polylogarithmic Convergence Time, Yuichi Sudo, Fukuhito Ooshita, Hirotsugu Kukugawa, Toshimitsu Masuzawa, Ajoy K. Datta, Lawrence L. Larmore
Computer Science Faculty Research
A loosely-stabilizing leader election protocol with polylogarithmic convergence time in the population protocol model is presented in this paper. In the population protocol model, which is a common abstract model of mobile sensor networks, it is known to be impossible to design a self-stabilizing leader election protocol. Thus, in our prior work, we introduced the concept of loose-stabilization, which is weaker than self-stabilization but has similar advantage as self-stabilization in practice. Following this work, several loosely-stabilizing leader election protocols are presented. The loosely-stabilizing leader election guarantees that, starting from an arbitrary configuration, the system reaches a safe configuration with a …
Mining Association Rules For Low-Frequency Itemsets, Jimmy Ming-Tai Wu, Justin Zhan, Sanket Chobe
Mining Association Rules For Low-Frequency Itemsets, Jimmy Ming-Tai Wu, Justin Zhan, Sanket Chobe
Computer Science Faculty Research
High utility itemset mining has become an important and critical operation in the Data Mining field. High utility itemset mining generates more profitable itemsets and the association among these itemsets, to make business decisions and strategies. Although, high utility is important, it is not the sole measure to decide efficient business strategies such as discount offers. It is very important to consider the pattern of itemsets based on the frequency as well as utility to predict more profitable itemsets. For example, in a supermarket or restaurant, beverages like champagne or wine might generate high utility (profit), but also sell less …
Deep Learning For Link Prediction In Dynamic Networks Using Weak Estimators, Carter Chiu, Justin Zhan
Deep Learning For Link Prediction In Dynamic Networks Using Weak Estimators, Carter Chiu, Justin Zhan
Computer Science Faculty Research
Link prediction is the task of evaluating the probability that an edge exists in a network, and it has useful applications in many domains. Traditional approaches rely on measuring the similarity between two nodes in a static context. Recent research has focused on extending link prediction to a dynamic setting, predicting the creation and destruction of links in networks that evolve over time. Though a difficult task, the employment of deep learning techniques have shown to make notable improvements to the accuracy of predictions. To this end, we propose the novel application of weak estimators in addition to the utilization …
Using Empirical Recurrence Rates Ratio For Time Series Data Similarity, Moinak Bhaduri, Justin Zhan
Using Empirical Recurrence Rates Ratio For Time Series Data Similarity, Moinak Bhaduri, Justin Zhan
Computer Science Faculty Research
Several methods exist in classification literature to quantify the similarity between two time series data sets. Applications of these methods range from the traditional Euclidean type metric to the more advanced Dynamic Time Warping metric. Most of these adequately address structural similarity but fail in meeting goals outside it. For example, a tool that could be excellent to identify the seasonal similarity between two time series vectors might prove inadequate in the presence of outliers. In this paper, we have proposed a unifying measure for binary classification that performed well while embracing several aspects of dissimilarity. This statistic is gaining …
Energy Saving In Data Centers, Wolfgang W. Bein
Energy Saving In Data Centers, Wolfgang W. Bein
Computer Science Faculty Research
Globally CO2 emissions attributable to Information Technology are on par with those resulting from aviation. Recent growth in cloud service demand has elevated energy efficiency of data centers to a critical area within green computing. Cloud computing represents a backbone of IT services and recently there has been an increase in high-definition multimedia delivery, which has placed new burdens on energy resources. Hardware innovations together with energy-efficient techniques and algorithms are key to controlling power usage in an ever-expanding IT landscape. This special issue contains a number of contributions that show that data center energy efficiency should be addressed from …
Dynamically Adjusting The Mining Capacity In Cryptocurrency With Binary Blockchain, Yoohwan Kim, Ju-Yeon Jo
Dynamically Adjusting The Mining Capacity In Cryptocurrency With Binary Blockchain, Yoohwan Kim, Ju-Yeon Jo
Computer Science Faculty Research
Many cryptocurrencies rely on Blockchain for its operation. Blockchain serves as a public ledger where all the completed transactions can be looked up. To place transactions in the Blockchain, a mining operation must be performed. However, due to a limited mining capacity, the transaction confirmation time is increasing. To mitigate this problem many ideas have been proposed, but they all come with own challenges. We propose a novel parallel mining method that can adjust the mining capacity dynamically depending on the congestion level. It does not require an increase in the block size or a reduction of the block confirmation …