Digital Commons Network^™

Causal Discovery With Latent Confounders Based On Higher-Order Cumulants, Ruichu Cai, Zhiyi Huang, Wei Chen, Zhifeng Hao, Kun Zhang

Machine Learning Faculty Publications

Causal discovery with latent confounders is an important but challenging task in many scientific areas. Despite the success of some overcomplete independent component analysis (OICA) based methods in certain domains, they are computationally expensive and can easily get stuck into local optima. We notice that interestingly, by making use of higher-order cumulants, there exists a closed-form solution to OICA in specific cases, e.g., when the mixing procedure follows the One-Latent-Component structure. In light of the power of the closed-form solution to OICA corresponding to the One-Latent-Component structure, we formulate a way to estimate the mixing matrix using the higher-order cumulants, …

Feature Expansion For Graph Neural Networks, Jiaqi Sun, Lin Zhang, Guangyi Chen, Peng Xu, Kun Zhang, Yujiu Yang

Machine Learning Faculty Publications

Graph neural networks aim to learn representations for graph-structured data and show impressive performance, particularly in node classification. Recently, many methods have studied the representations of GNNs from the perspective of optimization goals and spectral graph theory. However, the feature space that dominates representation learning has not been systematically studied in graph neural networks. In this paper, we propose to fill this gap by analyzing the feature space of both spatial and spectral models. We decompose graph neural networks into determined feature spaces and trainable weights, providing the convenience of studying the feature space explicitly using matrix space analysis. In …

Team Thesyllogist At Semeval-2023 Task 3: Language-Agnostic Framing Detection In Multi-Lingual Online News: A Zero-Shot Transfer Approach, Osama Mohammed Afzal, Preslav Nakov

Natural Language Processing Faculty Publications

We describe our system for SemEval-2022 Task 3 subtask 2 which on detecting the frames used in a news article in a multi-lingual setup. We propose a multi-lingual approach based on machine translation of the input, followed by an English prediction model. Our system demonstrated good zero-shot transfer capability, achieving micro-F1 scores of 53% for Greek (4th on the leaderboard) and 56.1% for Georgian (3rd on the leaderboard), without any prior training on translated data for these languages. Moreover, our system achieved comparable performance on seven other languages, including German, English, French, Russian, Italian, Polish, and Spanish. Our results demonstrate …

Semeval-2023 Task 3: Detecting The Category, The Framing, And The Persuasion Techniques In Online News In A Multi-Lingual Setup, Jakub Piskorski, Nicolas Stefanovitch, Giovanni Da San Martino, Preslav Nakov

Natural Language Processing Faculty Publications

We describe SemEval-2023 task 3 on Detecting the Category, the Framing, and the Persuasion Techniques in Online News in a Multilingual Setup: the dataset, the task organization process, the evaluation setup, the results, and the participating systems. The task focused on news articles in nine languages (six known to the participants upfront: English, French, German, Italian, Polish, and Russian), and three additional ones revealed to the participants at the testing phase: Spanish, Greek, and Georgian). The task featured three subtasks: (1) determining the genre of the article (opinion, reporting, or satire), (2) identifying one or more frames used in an …

Multilingual Multifaceted Understanding Of Online News In Terms Of Genre, Framing And Persuasion Techniques, Jakub Piskorski, Nicolas Stefanovitch, Nikolaos Nikolaidis, Giovanni Da San Martino, Preslav Nakov

Natural Language Processing Faculty Publications

We present a new multilingual multifacet dataset of news articles, each annotated for genre (objective news reporting vs. opinion vs. satire), framing (what key aspects are highlighted), and persuasion techniques (logical fallacies, emotional appeals, ad hominem attacks, etc.). The persuasion techniques are annotated at the span level, using a taxonomy of 23 fine-grained techniques grouped into 6 coarse categories. The dataset contains 1,612 news articles covering recent news on current topics of public interest in six European languages (English, French, German, Italian, Polish, and Russian), with more than 37k annotated spans of persuasion techniques. We describe the dataset and the …

Truhsh: Developing Ntru Cryptosystem In Terms Of Security And Performance, Hiba Shakir Salman, Hassan Rashed Yassein

Applied Mathematics & Information Sciences

The rapid advancement of technology, the creation of new communication channels, the globalization of some aspects of society, and the reliance on networks for the distribution of different types of data have all increased the risk of information leakage and unauthorized access, making it urgent to maintain information security. In this work, we presented the improvement of the NTRU system, which is still effective, by introducing a new system called TRUHSH based on a new algebra called hexat, in addition to a new mathematical construction of the encryption stages, while giving high security and efficient performance.

Model Transferability With Responsive Decision Subjects, Yatong Chen, Zeyu Tang, Kun Zhang, Yang Liu

Machine Learning Faculty Publications

Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it? In our setting, an agent or a user corresponds to a sample (X, Y ) drawn from a distribution D and will face a model h and its classification result h(X). Agents can modify X to adapt to h, which will incur a distribution shift on (X, Y ). Our formulation is motivated by applications where the deployed machine learning models are subjected to human agents, and will ultimately face responsive and interactive …

Detecting Out-Of-Distribution Data Through In-Distribution Class Prior, Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, Bo Han

Machine Learning Faculty Publications

Given a pre-trained in-distribution (ID) model, the inference-time out-of-distribution (OOD) detection aims to recognize OOD data during the inference stage. However, some representative methods share an unproven assumption that the probability that OOD data belong to every ID class should be the same, i.e., these OOD-to-ID probabilities actually form a uniform distribution. In this paper, we show that this assumption makes the above methods incapable when the ID model is trained with class-imbalanced data. Fortunately, by analyzing the causal relations between ID/OOD classes and features, we identify several common scenarios where the OOD-to-ID probabilities should be the ID-class-prior distribution and …

Orgsp Quarterly: An Orgsp Newsletter From Lincoln Memorial University, Vol 8.2, Lincoln Memorial University, Office Of Research Grants And Sponsored Projects

ORGSP Quarterly

This quarter's issue focuses on summer research. Learn about the excellent work of LMU's faculty, staff, and students.

How To Make Decision Under Interval Uncertainty: Description Of All Reasonable Partial Orders On The Set Of All Intervals, Tiago M. Costa, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we need to make a decision while for each alternative, we only know the corresponding value of the objective function with interval uncertainty. To help a decision maker in this situation, we need to know the (in general, partial) order on the set of all intervals that corresponds to the preferences of the decision maker. For this purpose, in this paper, we provide a description of all such partial orders -- under some reasonable conditions. It turns out that each such order is characterized by two linear inequalities relating the endpoints of the corresponding intervals, and …

Which Fuzzy Implications Operations Are Polynomial? A Theorem Proves That This Can Be Determined By A Finite Set Of Inequalities, Sebastia Massanet, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

To adequately represent human reasoning in a computer-based systems, it is desirable to select fuzzy operations that are as close to human reasoning as possible. In general, every real-valued function can be approximated, with any desired accuracy, by polynomials; it is therefore reasonable to use polynomial fuzzy operations as the appropriate approximations. We thus need to select, among all polynomial operations that satisfy corresponding properties -- like associativity -- the ones that best fit the empirical data. The challenge here is that properties like associativity mean satisfying infinitely many constraints (corresponding to infinitely many possible triples of values), while most …

Fuzzy Mathematics Under Non-Minimal "And"-Operations (T-Norms): Equivalence Leads To Metric, Order Leads To Kinematic Metric, Topology Leads To Area Or Volume, Purbita Jana, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Most formulas analyzed in fuzzy mathematics assume -- explicitly or implicitly -- that the corresponding "and"-operation (t-norm) is the simplest minimum operation. In this paper, we analyze what happens if instead, we use other "and"-operations. It turns out that for such operations, a fuzzification of a mathematical theory naturally leads to a more complex mathematical setting: fuzzification of equivalence relation leads to metric, fuzzification of order leads to kinematic metric, and fuzzification of topology leads to area or volume.

Why Deep Learning Is Under-Determined? Why Usual Numerical Methods For Solving Partial Differential Equations Do Not Preserve Energy? The Answers May Be Related To Chevalley-Warning Theorem (And Thus To Fermat Last Theorem), Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

In this paper, we provide a possible explanation to two seemingly unrelated phenomena: (1) that in deep learning, under-determined systems of equations perform much better than the over-determined one -- which are typical in data processing, and that (2) usual numerical methods for solving partial differential equations do not preserve energy. Our explanation is related to the intuition of Fermat behind his Last Theorem and of Euler about more general statements, intuition that led to the proof of Chevalley-Warning Theorem in number theory.

Methodological Lesson Of Pythagorean Triples, Julio C. Urenda, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

There are many right triangles in which all three sides a, b, and c have integer lengths. The triples (a,b,c) formed by such lengths are known as Pythagorean triples. Since ancient times, it is known how to generate all Pythagorean triples: we can enumerate primitive Pythagorean triples -- in which the three numbers have no common divisors -- by considering all pairs of natural numbers m>n in which m and n have no common divisors, and taking a =m2 − n2, b = 2mn, and c = m2 + n2. Multiplying all elements of a triple by the same …

Why Resilient Modulus Is Proportional To The Square Root Of Unconfined Compressive Strength (Ucs): A Qualitative Explanation, Edgar Daniel Rodriguez Velasquez, Vladik Kreinovich

Departmental Technical Reports (CS)

The strength of the pavement is determine by its resilient modulus, i.e., by its ability to withstand (practically) instantaneous stresses caused by the passing traffic. However, the resilient modulus is not easy to measure: its measurement requires a special expensive equipment that many labs do not have. So, instead of measuring it, practitioners often measure easier-to-measure Unconfined Compressive Strength (UCS) -- that describes the effect of a continuously applied force -- and estimate the resilient modulus based on the result of this measurement. An empirical formula shows that the resilient modulus is proportional to the square root of the Unconfined …

How To Estimate Unknown Unknowns: From Cosmic Light To Election Polls, Talha Azfar, Vignesh Ponraj, Vladik Kreinovich, Nguyen Hoang Phuong

Departmental Technical Reports (CS)

In two different areas of research -- in the study of space light and in the study of voting -- the observed value of the quantity of interest is twice larger than what we would expect. That the observed value is larger makes perfect sense: there are phenomena that we do not take into account in our estimations. However, the fact that the observed value is exactly twice larger deserves explanation. In this paper, we show that Laplace Indeterminacy Principle leads to such an explanation.

Norfolk Integrated Waste-Energy Framework, Emma Kurtz, Abigail Schroeder, Jonah Mcdowell

Project Energy Nebraska: Student Projects

Environmental Engagement in the Community projects, spring 2023 A poster titled Norfolk Integrated Waste-Energy Framework for client City of Norfolk, Nebraska and Nebraska Public Power District.

Faculty sponsor: David Gosselin

Improved Burned Area Mapping Using Monotemporal Landsat-9 Imagery And Convolutional Shift-Transformer, Seyd Teymoor Seydi, Mojtaba Sadegh

Civil Engineering Faculty Publications and Presentations

Satellite imagery, specifically Landsat, have been widely used for mapping and monitoring wildfire burned areas. The new Landsat-9 satellite – with higher radiometric resolution compared to its predecessors, and improved temporal resolution when combined with Landsat-8 (∼8 days) – enables a wide range of applications, particularly burned area mapping (BAM). We propose a novel deep learning BAM model that leverages the strengths of the convolutional layers for deep feature generation from Landsat-9 imagery and shift-transformer block for burned area classification. The performance of the model is evaluated in five large fire case studies across the globe. BAM results are also …

Longwood Magazine 2023 Summer, Longwood University

Alumni Newsletters & Bulletins

No abstract provided.

Olli Fall 2023 Catalog, Toniann Leavitt

Osher Lifelong Learning Institute

No abstract provided.

Long Island Sound Reef Gis Data, Robert M. Cerrato, Matthew Sclafani

SoMAS Research Data

High-resolution backscatter and bathymetric maps created by multibeam sonar surveys were used to identify different seafloor bottom types within existing, potentially expanded, and newly proposed reef areas in New York waters. Existing sites included Smithtown in Long Island Sound (LIS), and Rockaway, Atlantic Beach, Hempstead, Yellowbar, Kismet, Fire Island, Twelve Mile along the South Shore. Potential expansions are proposed on the South Shore for McAllister, Moriches, and Shinnecock reefs in addition to a new site called Sixteen Fathom. In Long Island Sound, new sites are proposed for Huntington/Oyster Bay, Port Jefferson/Mount Sinai, and Mattituck. Grab samples were collected within these …

Ua64/2 The Challenge: Magazine For The Center For Gifted Studies (No. 54, Summer 2023), Elizabeth Joyce Editor, Western Kentucky University

Gifted Studies Publications

Magazine created by and about the WKU Center for Gifted Studies.

Ensemble Of Handcrafted Features Of Environment Sound Classification Using A Deep Convolutional Neural Network To Enhance Accuracy And Reduce Computational Complexity, Ibrahim Abdulrahman Aljubayri

Theses and Dissertations

Environmental sound classification (ESC) is an area of active research in signal and image processing that has made significant strides over the past several years. The goal of ESC is to classify environmental sounds by extracting and analyzing handcrafted and deep features from various acoustic events. The task is complex because environmental sounds are typically unstructured, nonstationary, and overlapping. Multiple deep learning (DL) approaches have successfully tackled the ESC problem and outperformed conventional classifiers like k-nearest neighbors (kNN) or support vector machine (SVM). However, most DL approaches have high computational costs, making them unsuitable for use in embedded systems applications. …

Tuning Oxo Formation Energies Using Spectator Ligands In The Mil-100 Metal Organic Framework, Victoria E. Lee, Stephen P. Vicchio, Rachel B. Getman

Chemistry Summer Research Program

No abstract provided.

Effects Of Low-Energy X-Rays And Uv Radiation On Fibroblast Cells, Michelle Gorodisky, Congyue Peng, Arianna Csiszer, Delphine Dean

Chemistry Summer Research Program

No abstract provided.

Understanding The Leaders Of Tomorrow: The Need To Study Leadership In Adolescence, Jennifer L. Tackett, Kathleen W. Reardon, Nathanael J. Fast, Lars Johnson

Psychology Faculty Publications

Leadership traits and behaviors are observed early in human development, and although an improved understanding of youth leadership would usefully inform many real-world contexts (e.g., education, parenting, policy), most empirical work on leadership has been limited to adult populations. The purpose of the current article is to add a developmental perspective to leadership research that has so far been absent. Here, we (a) highlight adolescence as a critical developmental period for leadership emergence and development, (b) argue that leadership among youths is poorly understood and critically understudied, (c) provide exemplars of synergy between research on leadership and adolescent development that …

2023-2024 Catalog, Governors State University

Catalogs

The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:
Degree Requirements
Course Offerings
Undergraduate and Graduate Rules and Regulations

Summer 2023, Augustana College, Rock Island Illinois

Augustana Magazine

No abstract provided.

Changemakers Share Their Why, Collaborate As Critical Friends, And Highlight Leadership Skills​, Rebekka Jez, Christopher Dennis, Michelle Coleman, Corné Conradie, Abongile Matyaleni, Diana Ramirez, Chanté Rezandt, Kayla Wilkins, Cassie Herndon

School of Leadership and Education Sciences: Faculty Scholarship

Background: Changemakers are justice-minded individuals working toward implementing positive and sustainable change within their communities.

Aim: The project created a space for Changemakers to connect, engage in critical dialogue, and leave with shared knowledge and skills to dismantle oppressive, deficit-based educational systems in the aftermath of COVID-19.

Setting: From October 2021 to June 2022, 18 American and South African Changemakers from primary, secondary, and higher education institutions exchanged messages about their regional context and attended the Changemaker Symposium in South Africa.

Method: This qualitative study examined how the Changemaker framework guided educators to frame [ubuntu], convene [masikhule …

Identification Of Dialect For Eastern And Southwestern Ojibwe Words Using A Small Corpus, Kalvin Hartwig, Evan Lucas, Timothy C. Havens

Michigan Tech Publications

The Ojibwe language has several dialects that vary to some degree in both spoken and written form. We present a method of using support vector machines to classify two different dialects (Eastern and Southwestern Ojibwe) using a very small corpus of text. Classification accuracy at the sentence level is 90% across a five-fold cross validation and 72% when the sentence-trained model is applied to a data set of individual words. Our code and the word level data set are released openly at https://github.com/evanperson/OjibweDialect.