NMFS: Network Multimedia File System Protocol

We describe an on-going project to develop a Network Multimedia File System (NMFS) protocol. The protocol allows "transparent access of shared files across networks" as Sun's NFS protocol does, but attempts to meet a real-time delivery schedule. NMFS is designed to provide ubiquitous service over networks both designed and not designed to carry multimedia traffic

Optical and Hybrid Imaging and Processing for Big Data Problems

(First paragraph) The scientific community has been dealing with big data for a long time. Due to advancement in sensing, networking, and storage technology, other domains such as business, health, and social media followed. Data are considered the gold of the 21st century and are being collected, stored, and analyzed at a rapid pace. The amount of data being collected creates a compelling case for investing in hardware and software research to support generating even more data from new sensors and with better quality. It also creates a compelling case for investing in research and development of new hardware and software for data analytics. This special section of Optical Engineering explores the optical and hybrid imaging and processing technology that will enable capturing and analyzing large amounts of data or help stream the data for further exploration and analysis

Designing a Multi-Petabyte Database for LSST

The 3.2 giga-pixel LSST camera will produce approximately half a petabyte of archive images every month. These data need to be reduced in under a minute to produce real-time transient alerts, and then added to the cumulative catalog for further analysis. The catalog is expected to grow about three hundred terabytes per year. The data volume, the real-time transient alerting requirements of the LSST, and its spatio-temporal aspects require innovative techniques to build an efficient data access system at reasonable cost. As currently envisioned, the system will rely on a database for catalogs and metadata. Several database systems are being evaluated to understand how they perform at these data rates, data volumes, and access patterns. This paper describes the LSST requirements, the challenges they impose, the data access philosophy, results to date from evaluating available database technologies against LSST requirements, and the proposed database architecture to meet the data challenges

Feature Selection by Multiobjective Optimization: Application to Spam Detection System by Neural Networks and Grasshopper Optimization Algorithm

Networks are strained by spam, which also overloads email servers and blocks mailboxes with unwanted messages and files. Setting the protective level for spam filtering might become even more crucial for email users when malicious steps are taken since they must deal with an increase in the number of valid communications being marked as spam. By finding patterns in email communications, spam detection systems (SDS) have been developed to keep track of spammers and filter email activity. SDS has also enhanced the tool for detecting spam by reducing the rate of false positives and increasing the accuracy of detection. The difficulty with spam classifiers is the abundance of features. The importance of feature selection (FS) comes from its role in directing the feature selection algorithm’s search for ways to improve the SDS’s classification performance and accuracy. As a means of enhancing the performance of the SDS, we use a wrapper technique in this study that is based on the multi-objective grasshopper optimization algorithm (MOGOA) for feature extraction and the recently revised EGOA algorithm for multilayer perceptron (MLP) training. The suggested system’s performance was verified using the SpamBase, SpamAssassin, and UK-2011 datasets. Our research showed that our novel approach outperformed a variety of established practices in the literature by as much as 97.5%, 98.3%, and 96.4% respectively.©2022 the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/fi=vertaisarvioitu|en=peerReviewed