Academic Articles & Research Papers

Authorization in distributed systems: a formal approach

It is argued that authorization is an independent semantic concept that must be separated from implementation mechanisms and given a precise semantics. A logical approach to representing and evaluating authorization is proposed. Specificall...

Cybersecurity for Critical Infrastructures: Attack and Defense Modeling

Disruption of electric power operations can be catastrophic on national security and the economy. Due to the complexity of widely dispersed assets and the interdependences among computer, communication, and power infrastructures, the requir...

Organizational Learning from Cybersecurity Performance: Effects on Cybersecurity Investment Decisions

Abstract IS literature has identified various economic, performance, and environmental factors affecting cybersecurity investment decisions. However, economic modeling approaches dominate, and research on cybersecurity performance as an ant...

The protoscience of cybersecurity

I argue that marking a proper distinction between two types of research in the cybersecurity field obviates the present debate concerning a “science of cybersecurity.” Once the terminology has been properly disambiguated, the accurate descr...

Machine learning in cybersecurity: a comprehensive survey

Today’s world is highly network interconnected owing to the pervasiveness of small personal devices (e.g., smartphones) as well as large computing devices or services (e.g., cloud computing or online banking), and thereby each passing minut...

Graph Mining for Cybersecurity: A Survey

The explosive growth of cyber attacks today, such as malware, spam, and intrusions, has caused severe consequences on society. Securing cyberspace has become a great concern for organizations and governments. Traditional machine learning ba...

Quantifying Uncertainties in Natural Language Processing Tasks

Reliable uncertainty quantification is a first step towards building explainable, transparent, and accountable artificial intelligent systems. Recent progress in Bayesian deep learning has made such quantification realizable. In this paper,...

Natural Language Processing and User Modeling: Synergies and Limitations

Neural Network Methods for Natural Language Processing

Deep learning has attracted dramatic attention in recent years, both in academia and industry. The popular term deep learning generally refers to neural network methods. Indeed, many core ideas and methods were born years ago in the era of ...

Using natural language processing to understand people and culture.

Language can provide important insights into people, and culture more generally. Further, the digitization of information has made more and more textual data available. But by itself, all that data are just that: data. Realizing its potenti...

Natural Language Processing

In the modern age of information and analytics, natural language processing (NLP) is one of the most important technologies out there. Making sense of complex structures in language and deriving insights and actions from it are crucial from...

A survey of graphs in natural language processing

Abstract Graphs are a powerful representation formalism that can be applied to a variety of aspects related to language processing. We provide an overview of how Natural Language Processing problems have been projected into the graph framew...

A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Due to the increasing spread, confidence in neural network predictions has become more a...

Neural Networks for Statistical Modeling

Neural Networks in Structural Engineering

In the past few years literature on computational civil engineering has concentrated primarily on artificial intelligence (Al) applications involving expert system technology. This article discusses a different Al approach involving neural ...

Neural Networks in Business Forecasting

Neural Network is defined as the ability of a group to solve more problems than its individual members.The idea brings that a group of people can solve problems efficiently and offer greater insight and a better answer than any one individu...

A renaissance of neural networks in drug discovery

INTRODUCTION: Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to cho...

LSTM neural networks for language modeling

Neural networks have become increasingly popular for the task of language modeling.Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks ca...

Neural networks in ocean engineering

The soft computing technique of neural network is being extensively used across all disciplines of ocean engineering, namely, offshore, coastal, and deep-ocean engineering including marine engineering. This paper takes a stock of the resear...

Nonlinear Flight Control Using Neural Networks

The theoretical developmentofa direct adaptivetracking controlarchitectureusingneuralnetworks ispresented. Emphasis is placed on utilization of neural networks in a  ight control architecture based on feedback linearization of the aircraft...