Academic Articles & Research Papers

Authorization in Distributed Systems: A New Approach1

In most existing systems, authorization is specified using some low-level system-specific mechanisms, e.g., protection bits, capabilities and access control lists. We argue that authorization is an independent semantic concept that must be ...

Scheduling in distributed systems: A cloud computing perspective

Fundamentals of distributed system observation

It's difficult to determine event order in distributed systems because of the observability problem. The author discusses this problem and evaluates different strategies for determining arrival order. The author analyzed four time stamping ...

Robust Probabilistic Inference in Distributed Systems

Probabilistic inference problems arise naturally in distributed systems such as sensor networks and teams of mobile robots. Inference algorithms that use message passing are a natural fit for distributed systems, but they must be robust to ...

Building reliable distributed systems with CORBA

New classes of large-scale distributed applications will have to deal with unpredictable communication delays, with partial failures, and with networks that partition. In addition, sophisticated applications such as teleconferencing, video-...

Verdi: a framework for implementing and formally verifying distributed systems

Distributed systems are difficult to implement correctly because they must handle both concurrency and failures: machines may crash at arbitrary points and networks may reorder, drop, or duplicate packets. Further, their behavior is often t...

The right type of trust for distributed systems

Research in information security has traditionally focused on where to place or how to propagate trust.In that sense, a cyptographic algorithm or protocol is simply a mechanism to transfer trust from where it exists to where it is needed.Th...

Cybersecurity

This interactive module explores essential cybersecurity topics such as phishing, data protection, and digital identity. Learners will analyze real-life scenarios, build technical vocabulary, and practice communication skills while discover...

Cybersecurity and global regulatory challenges

Purpose This study aims to define objectively what are the elements that should be considered in the repositioning of international cooperation, less under its value, which is unquestionable, but more under the optics of the procedure, how ...

Hacking the Human: The Prevalence Paradox in Cybersecurity

OBJECTIVE: This work assesses the efficacy of the "prevalence effect" as a form of cyberattack in human-automation teaming, using an email task. BACKGROUND: Under the prevalence effect, rare signals are more difficult to detect, even when t...

Cybersecurity

"This book is the first of its kind to introduce the integration of ethics, laws, risks, and policies in cyberspace. The book will advance understanding of the ethical and legal aspects of cyberspace followed by the risks involved along wit...

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 ...

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During t...

Statistical Mechanics of Neural Networks

A neural network is a large, highly interconnected assembly of simple elements. The elements, called neurons, are usually two-state devices that switch from one state to the other when their input exceeds a specific threshold value. In this...

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 ...

Bayesian Nonparametric Federated Learning of Neural Networks

In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data...

Towards Evaluating the Robustness of Neural Networks

Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x'...

Neural Networks for Postprocessing Ensemble Weather Forecasts

Abstract Ensemble weather predictions require statistical postprocessing of systematic errors to obtain reliable and accurate probabilistic forecasts. Traditionally, this is accomplished with distributional regression models in which the pa...

Lagrangian Neural Networks

Accurate models of the world are built upon notions of its underlying symmetries. In physics, these symmetries correspond to conservation laws, such as for energy and momentum. Yet even though neural network models see increasing use in the...

Advances in Neural Networks