Seeking Sustainability for Computing

The talk will provide two perspectives on how sustainability is considered in computing. First, the impact computing has on energy consumption and on the environment will be discussed through the prism of past and prior research projects. Computing currently drives advances in all areas of science and engineering, generates efficiencies in industries, and dominates the creation and delivery of entertainment. Computing is also a significant consumer of energy accounting for 3% of the global usage. Data centers account of a third of this consumption, yet also provide a case where efficiencies in system design have limited the energy use increase despite considerable growth in computational efficiency. Second, the sustainability of scientific software and data will be discussed. Scientific computing is often driven by applications and libraries created by small research groups that aim to share their work, improve the replicability of the results and provide a tool for a larger research community. Faced with limited funding, lack of academic recognition, and waning interest, such efforts however are often unsuccessful in creating, maintaining and sustaining quality software. Aspects on how software and data products can be sustained will be discussed

Automated machine learning for analysis and prediction of vehicle crashes

This work discusses the study and development of a graphical interface and implementation of a machine learning model for vehicle traffic injury and fatality prediction for a specified date range and for a certain zip (US postal) code based on the New York City's (NYC) vehicle crash data set. While previous studies focused on accident causes, little insight has been offered into how such data may be utilized to forecast future incidents, Studies that have historically concentrated on certain road segment types, such as highways and other streets, and a specific geographic region, this study offers a citywide review of collisions. Using cutting-edge database and networking technology, a user-friendly interface was created to display vehicle crash series. Following this, a support vector machine learning model was built to evaluate the likelihood of an accident and the consequent injuries and deaths at the zip code level for all of NYC and to better mitigate such events. Using the visualization and prediction approach, the findings show that it is efficient and accurate. Aside from transportation experts and government policymakers, the machine learning approach deliver useful insights to the insurance business since it quantifies collision risk data collected at specific places

Band reduction for hyperspectral imagery processing

ABSTRACT Feature reduction denotes the group of techniques that reduce high dimensional data to a smaller set of components. In remote sensing feature reduction is a preprocessing step to many algorithms intended as a way to reduce the computational complexity and get a better data representation. Reduction can be done by either identifying bands from the original subset (selection), or by employing various transforms that produce new features (extraction). Research has noted challenges in both directions. In feature selection, identifying an "ideal" spectral band subset is a hard problem as the number of bands is increasingly large, rendering any exhaustive search unfeasible. To counter this, various approaches have been proposed that combine a search algorithm with a criterion function. However, the main drawback of feature selection remains the rather narrow bandwidths covered by the selected bands resulting in possible information loss. In feature extraction, some of the most popular techniques include Principal Component Analysis, Independent Component Analysis, Orthogonal Subspace Projection, etc. While they have been used with success in some instances, the resulting bands lack a physical relationship to the data and are mostly produced using statistical strategies. We propose a new technique for feature reduction that exploits search strategies for feature selection to extract a set of spectral bands from a given imagery. The search strategy uses dynamic programming techniques to identify 'the best set" of features

Statistical Steganalyis of Images Using Open Source Software

Abstract-In this paper we present a novel steganalytic tool based on statistical pattern recognition. The main aim of our project was to design and implement a system able to classify the images into ones with no hidden message and steganographic images using classic pattern classification techniques such as Bayesian classification and decision trees. Experiments are conducted on a large data set of images to determine the classification algorithm that performs better by comparing classification success and error rates in each case. We have employed Weka, a data-mining tool developed in java for this purpose. We have also developed an application using Weka Java library for loading the data of the Images and classify the images into normal images and steganographic images. This application runs a GUI(Graphical User Interface) that enables the user to choose the classifier and other options required for the classification. Our results are aligned with current state of the art research and have the advantage of using open source software

Board 326: K-12 Teachers and Data Science: Learning Interdiscplinary Science Through Research Experiences

Data science is now pervasive across STEM, and early exposure and education in its basics will be important for the future workforce, academic programs, and scholarly research in engineering, technology, and the formal and natural sciences, and in fact, across the full spectrum of disciplines. When combined with an emphasis on soft skills and an interdisciplinary focus, such educational experiences have deeper and more meaningful effects. Our Montclair State University NSF Research Experience for Teachers (RET) grant (NSF Award Number: #2206885, IRB Number: 22-23-3003) exposed teachers to a program integrating solar weather, data science, computer science and artificial intelligence, and STEM pedagogy. The cohort comprised nine middle- and high-school teachers with diverse academic backgrounds and demographics from northern and central New Jersey. The teachers interacted with and were advised by faculty from Montclair and two other institutions, and by outside experts, to learn the basics, develop lesson plans, and present these to and interact with a learning-intensive summer camp. As a capstone, teachers developed research projects synthesizing this interdisciplinary content with their own interests and background. As a result, the teachers have submitted several posters with abstracts to the 2024 ACM SIGCSE and IEEE ISEC conferences and will be delivering grant-related lessons in their classes during the current academic year

Professional and Capacity Building in K-12 Computer Science Education: A Multi-Faceted Approach

States are moving to adopt Computer Science (CS) education standards to help K-12 teachers adapt and integrate computing /computational thinking (CT) concepts into the curriculum. These approaches also rely heavily on training current and pre-service teachers and creating opportunities to learn CS while also managing the rigors of their education career. This poster presents elements of the collaboration between the Department of CS and Department of Teaching and Learning at Montclair State University (MSU) to bring CS to pre-and in-service educators. Here we will highlight our curriculum work and professional development (PD) series. The New Jersey (NJ) Department of Education has adopted CS Education standards for K-12 and distinctly funded curriculum development and faculty formation programs. MSU has built programs that support teachers through PD experiences in CT and CS. In the 10-month period ending March 2023, we will offer 30 PD opportunities for a CS and CS education. To date, more than 200 educators have received PD to address their educational needs regarding CS curricula

An analysis of spectral metrics for hyperspectral image processing

Abstract-This paper investigates the efficiency of spectral metrics when used in spectral screening of hyperspectral imagery. Spectral screening is the technique of selecting from the data a subset of spectra such that any two spectra in the subset are dissimilar and, for any spectra in the original image cube, there is a similar spectra in the subset. The method can use various spectral metrics to characterize the similarity and can be seen as a data reduction step if the resulting subset is used in further computations instead of the full data. The investigation has focused on the comparison between spectral angle and spectral correlation angle in terms of efficiency of the results and speedup obtained as well as in empirically identifying the best distance threshold to be used when reducing the data. The techniques were tested on Hyperion imagery when using PCA and show promising speedup

Stefan Robila

Stefan Robila, Montclair State Universityhttps://digitalcommons.montclair.edu/sust-seminar-headshots/1085/thumbnail.jp