Digital Commons Network^™

Augmented Creativity: Leveraging Natural Language Processing For Creative Writing, Daniel Plate, James Hutson

Faculty Scholarship

Recent advances have moved natural language processing (NLP) capabilities with artificial intelligence beyond mere grammar and spell-checking functionality. One such new use that has arisen is the ability to suggest new content to writers to inspire new ideas by using “machine-in-the-loop” strategies in creative writing. In order to explore the possibilities of such a strategy, this study provides a model to be adopted in creative writing courses in higher education. An NLP application was created using Python and spaCy and deployed via Streamlit. The AI allowed students to see if their grammar aligned with those principles and techniques taught in …

Asian Hate Speech Detection On Twitter During Covid-19, Amir Toliyat, Sarah Ita Levitan, Zeng Peng, Ronak Etemadpour

Publications and Research

Coronavirus disease 2019 (COVID-19) started in Wuhan, China, in late 2019, and after being utterly contagious in Asian countries, it rapidly spread to other countries. This disease caused governments worldwide to declare a public health crisis with severe measures taken to reduce the speed of the spread of the disease. This pandemic affected the lives of millions of people. Many citizens that lost their loved ones and jobs experienced a wide range of emotions, such as disbelief, shock, concerns about health, fear about food supplies, anxiety, and panic. All of the aforementioned phenomena led to the spread of racism and …

C60 Hydrofullerene Induced Autophagy And Ameliorated Gfap In H2o2 Treated Human Malignant Glioblastoma U-373 Cell Line, Aryan M. Faraj, Can A. Agca, Victor S. Nedzvetsky, Artem A. Tykhomyrov

Karbala International Journal of Modern Science

Glioblastoma is one of the most combative astrocytoma that is resistant to chemotherapy and radiotherapy. This resistance makes it very difficult to treat. However, researches have shown that nanoparticles especially C60 hydrofullerene have antioxidant and anticancer activity. The effect of C60 hydrofullerene in cancer has been extensively studied; however, the potential regulation of autophagy and modulation of the Glial Fibrillary Acidic Protein (GFAP) gene has not been addressed in glioblastomas. Glioblastoma U-373 cell was treated with 0.5 µM of C60 hydrofullerene and/or 1 mM of hydrogen peroxide (H2O2) for 24 hours. This study demonstrated that C60 hydrofullerene and H2O2 significantly …

Leveraging Large Language Models Trained On Code For Symbol Binding, Joshua Robinson

Theses and Dissertations

While large language models like GPT-3 have achieved impressive results in the zero-, one-, and few-shot settings, they still significantly underperform on some tasks relative to the state of the art (SOTA). For many tasks it would be useful to have answer options explicitly listed out in a multiple choice format, decreasing computational cost and allowing the model to reason about the relative merits of possible answers. We argue that the reason this hasn't helped models like GPT-3 close the gap with the SOTA is that these models struggle with symbol binding - associating each answer option with a symbol …

Cyberbullying Detection Using Weakly Supervised And Fully Supervised Learning, Abhinav Abhishek

ETD Archive

Machine learning is a very useful tool to solve issues in multiple domains such as sentiment analysis, fake news detection, facial recognition, and cyberbullying. In this work, we have leveraged its ability to understand the nuances of natural language to detect cyberbullying. We have further utilized it to detect the subject of cyberbullying such as age, gender, ethnicity, and religion. Further, we have built another layer to detect the cases of misogyny in cyberbullying. In one of our experiments, we created a three-layered architecture to detect cyberbullying , then to detect if it is gender based and finally if it …

Holistic Combination Of Structural And Textual Code Information For Context Based Api Recommendation, Chi Chen, Xin Peng, Zhengchang Xing, Jun Sun, Xin Wang, Yifan Zhao, Wenyun Zhao

Research Collection School Of Computing and Information Systems

Context based API recommendation is an important way to help developers find the needed APIs effectively and efficiently. For effective API recommendation, we need not only a joint view of both structural and textual code information, but also a holistic view of correlated API usage in control and data flow graph as a whole. Unfortunately, existing API recommendation methods exploit structural or textual code information separately. In this work, we propose a novel API recommendation approach called APIRec-CST (API Recommendation by Combining Structural and Textual code information). APIRec-CST is a deep learning model that combines the API usage with the …

Variational Graph Author Topic Modeling, Ce Zhang, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

While Variational Graph Auto-Encoder (VGAE) has presented promising ability to learn representations for documents, most existing VGAE methods do not model a latent topic structure and therefore lack semantic interpretability. Exploring hidden topics within documents and discovering key words associated with each topic allow us to develop a semantic interpretation of the corpus. Moreover, documents are usually associated with authors. For example, news reports have journalists specializing in writing certain type of events, academic papers have authors with expertise in certain research topics, etc. Modeling authorship information could benefit topic modeling, since documents by the same authors tend to reveal …

Regression Analysis Of Resilience And Covid-19 In Idaho Counties, Ishrat Zaman

Boise State University Theses and Dissertations

Global pandemic Coronavirus Disease 2019 (COVID-19) has serious harmful effects on our day-to-day lives. To overcome challenges such as this, critical preparedness, readiness, and response actions are required. This thesis uses estimates of community resilience available through the CRE Tool, published by the US Census Bureau, and COVID19 cases published by John Hopkins Coronavirus Research Center for Idaho counties. Simple linear regression analysis was performed to identify a correlation between COVID-19 cases and deaths in Idaho counties and measures of their resilience. Understanding this correlation could lead to better estimation and prediction of the effect of disasters in Idaho’s counties. …

Towards Making Transformer-Based Language Models Learn How Children Learn, Yousra Mahdy

Boise State University Theses and Dissertations

Transformer-based Language Models (LMs), learn contextual meanings for words using a huge amount of unlabeled text data. These models show outstanding performance on various Natural Language Processing (NLP) tasks. However, what the LMs learn is far from what the meaning is for humans, partly due to the fact that humans can differentiate between concrete and abstract words, but language models make no distinction. Concrete words are words that have a physical representation in the world such as “chair”, while abstract words are ideas such as “democracy”. The process of learning word meanings starts from early childhood when children acquire their …

Automated Detection Of Sockpuppet Accounts In Wikipedia, Mostofa Najmus Sakib

Boise State University Theses and Dissertations

Wikipedia is a free Internet-based encyclopedia that is built and maintained via the open-source collaboration of a community of volunteers. Wikipedia’s purpose is to benefit readers by acting as a widely accessible and free encyclopedia, a comprehensive written synopsis that contains information on all discovered branches of knowledge. The website has millions of pages that are maintained by thousands of volunteer editors. Unfortunately, given its open-editing format, Wikipedia is highly vulnerable to malicious activity, including vandalism, spam, undisclosed paid editing, etc.

Malicious users often use sockpuppet accounts to circumvent a block or a ban imposed by Wikipedia administrators on the …

A Climate Of Confessionalization: Famine And Difference In The Late Ottoman Empire, Matthew Ghazarian

Environmental Science and Policy: Faculty Publications

After the 1877–78 Russo-Ottoman War, the Ottoman Empire saw the rise of ethnic and sectarian clashes in Anatolia, the Balkans, and elsewhere, and the task of explaining that rise remains unfinished. Many have examined the intellectual formations of ethnic and sectarian solidarities after 1878, but the availability of new ideas cannot alone account for their widespread uptake. Why after 1878 did ordinary people respond more to calls upon ethnic and sectarian solidarity? Drawing on sources surrounding the 1879 famine in the Ottoman East, this article steps away from imperial metropoles to examine overlapping environmental, financial, and technological disjunctures. Adopting the …

Using Student Teaching Evaluations To Assess The Efficacy Of A Robert Noyce Teaching Scholarship In Preparing Candidates For Culturally-Responsive Practices, Teresa J. Bixby, Lauren Landando

Chemistry Department Faculty Articles

The goal of the NSF Robert Noyce Teaching Scholarship is to attract and support highly qualified STEM majors to become secondary-education teachers that are prepared to teach in high-needs schools. At Lewis University program activities targeted 1) teaching diverse learners, 2) inquiry and active-learning pedagogies, and 3) strengthening content knowledge. When the climate in education changed nationally mid-grant, activities to explicitly address culturally-relevant practices were developed. Since this emphasis was not directly addressed in the grant evaluation, here the use of student teaching evaluations as a tool to assess preparation in culturally-relevant practices is explored. Although some distinctions between Noyce …

Fake News Detection On Social Media: A Word Embedding-Based Approach, Muammer Eren Sahin, Chunyang Tang, Mohammad A. Al-Ramahi

Computer Information Systems Faculty Publications (Archived)

The rapid development of social media, together with the large number of user-generated content on them, has not only connected an unprecedented number of people together to do good stuff, but also has provided convenient platforms to spread misleading pieces of information such as fake news. Existing research has attempted to leverage machine learning to automatically classify fake news. In this paper, we extend such literature by proposing an approach that utilize word embedding and Long Short-Term Memory (LSTM) neural network algorithm. Unlike existing studies, we used two publicly available datasets of news articles to evaluate the proposed model. The …

Feature Analysis Of Indus Valley And Dravidian Language Scripts With Similarity Matrices, Sarat Sasank Barla, Sai Surya Sanjay Alamuru, Peter Revesz

School of Computing: Conference and Workshop Papers

This paper investigates the similarity between the Indus Valley script and the Kannada, Malayalam, Tamil, and Telugu scripts that are used to write Dravidian languages. The closeness of these scripts is determined by applying a feature analysis of each sign of these scripts and creating similarity matrices that describe the similarity of any pair of signs from two different scripts. The feature list that we use for the analysis of these Dravidian language-related scripts includes six new features beyond the thirteen features that were used for the study of Minoan Linear A and related scripts by Revesz. These new features …

Dimension And Ramsey Results In Partially Ordered Sets., Sida Wan

Electronic Theses and Dissertations

In this dissertation, there are two major parts. One is the dimension results on different classes of partially ordered sets. We developed new tools and theorems to solve the bounds on interval orders using different number of lengths. We also discussed the dimension of interval orders that have a representation with interval lengths in a certain range. We further discussed the interval dimension and semi dimension for posets. In the second part, we discussed several related results on the Ramsey theory of grids, the results involve the application of Product Ramsey Theorem and Partition Ramsey Theorem

Secrep : A Framework For Automating The Extraction And Prioritization Of Security Requirements Using Machine Learning And Nlp Techniques, Shada Khanneh

Theses, Dissertations and Culminating Projects

Gathering and extracting security requirements adequately requires extensive effort, experience, and time, as large amounts of data need to be analyzed. While many manual and academic approaches have been developed to tackle the discipline of Security Requirements Engineering (SRE), a need still exists for automating the SRE process. This need stems mainly from the difficult, error-prone, and time-consuming nature of traditional and manual frameworks. Machine learning techniques have been widely used to facilitate and automate the extraction of useful information from software requirements documents and artifacts. Such approaches can be utilized to yield beneficial results in automating the process of …

Effective Knowledge Graph Aggregation For Malware-Related Cybersecurity Text, Phillip Ryan Boudreau

Graduate Theses and Dissertations

With the rate at which malware spreads in the modern age, it is extremely important that cyber security analysts are able to extract relevant information pertaining to new and active threats in a timely and effective manner. Having to manually read through articles and blog posts on the internet is time consuming and usually involves sifting through much repeated information. Knowledge graphs, a structured representation of relationship information, are an effective way to visually condense information presented in large amounts of unstructured text for human readers. Thusly, they are useful for sifting through the abundance of cyber security information that …

Crosscbr: Cross‑View Contrastive Learning For Bundle Recommendation, Yunshan Ma, Yingzhi He, An Zhang, Xiang Wang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Bundle recommendation aims to recommend a bundle of related items to users, which can satisfy the users' various needs with one-stop convenience. Recent methods usually take advantage of both user-bundle and user-item interactions information to obtain informative representations for users and bundles, corresponding to bundle view and item view, respectively. However, they either use a unified view without differentiation or loosely combine the predictions of two separate views, while the crucial cooperative association between the two views' representations is overlooked.In this work, we propose to model the cooperative association between the two different views through cross-view contrastive learning. By encouraging …

Calculus Iii: Under The Influence Of Peer Instruction, Alan Von Herrmann, L. Jeneva Clark

Journal of Humanistic Mathematics

In peer Instruction, students engage with core course concepts and then explain those concepts to one another in small groups. Unlike in lecture format, peer instruction involves every student in the class. In Spring 2019, the first authot began using a modified version of peer instruction in Calculus III classes. He started each class by discussing important Calculus III concepts from three standpoints (the formula, the geometry behind the formula, and the physics behind the formula). During the last 20 minutes of each 50-minute class session, he polled the students using questions in the “Goldilocks Zone” – not too hard …

Higher Meanings: A Speaker Series Connecting Mathematics And Religion, Lawrence M. Lesser, Patricia S. Barrientos, Ben Zeidman

Journal of Humanistic Mathematics

An innovative grant-funded general adult audiences international speaker series on connections between mathematics and religion yielded six 2021 (now archived) presentations. We share reflections and lessons learned, informed by two sets of surveys.

Beyond Ethics: Considerations For Centering Equity-Minded Data Science, Nathan Alexander, Carrie Diaz Eaton, Anelise H. Shrout, Belin Tsinnajinnie, Krystal Tsosie

Journal of Humanistic Mathematics

In this paper, we utilize duoethnography - a research method in which practitioners discursively interrogate the relationships between culture, context, and the mechanisms which shape individual autobiographical experiences - to explore what may be beyond ethics in the context of data science. Although ethical frameworks have the ability to reflect cultural priorities, a singular view of ethics, as we explore, often fails to speak to the multiple and diverse priorities held both within and across institutional spaces. To that end, this paper explores multiple perspectives, epistemologies, and worldviews that can collectively push researchers towards considerations of a data science education …

Ethics And Mathematics – Some Observations Fifty Years Later, Gregor Nickel

Journal of Humanistic Mathematics

Almost exactly fifty years ago, Friedrich Kambartel, in his classic essay “Ethics and Mathematics,” did pioneering work in an intellectual environment that almost self-evidently assumed a strict separation of the two fields. In our first section we summarize and discuss that classical paper. The following two sections are devoted to complement and contrast Kambartel’s picture. In particular, the second section is devoted to ethical aspects of the indirect and direct mathematization of modern societies. The final section gives a short categorization of various philosophical positions with respect to the rationality of ethics and the mutual relation between ethics and mathematics.

Mathematics Textbook: Motivation, Experiences, And Didactical Aspect From Authors’ Perspectives, Deepak Basyal, Bhesh Raj Mainali

Mathematics and Statistics

Textbooks play a vital role in the Nepali education system since they are one of the main resources for teaching and learning mathematics. Because of poor physical infrastructure and inadequate educational resources, both teachers and students heavily rely on textbooks. In this regard, this study investigated the mathematics textbook authors' experiences and motivation, and what types of didactical knowledge were utilised while writing textbooks. A convenient yet purposeful sampling method was utilised to select four participants. The data analysis unveiled that each textbook author had different types of motivation and experiences, and only one participant was aware of the ideas …

Wikimarks: Harvesting Relevance Benchmarks From Wikipedia, Laura Dietz, Shubham Chatterjee, Connor Lennox, Sumanta Kashyapi, Pooja Oza, Ben Gamari

Computer Science Faculty Research & Creative Works

We provide a resource for automatically harvesting relevance benchmarks from Wikipedia - which we refer to as "Wikimarks"to differentiate them from manually created benchmarks. Unlike simulated benchmarks, they are based on manual annotations of Wikipedia authors. Studies on the TREC Complex Answer Retrieval track demonstrated that leaderboards under Wikimarks and manually annotated benchmarks are very similar. Because of their availability, Wikimarks can fill an important need for Information Retrieval research. We provide a meta-resource to harvest Wikimarks for several information retrieval tasks across different languages: paragraph retrieval, entity ranking, query-specific clustering, outline prediction, and relevant entity linking and many more. …

Bert-Er: Query-Specific Bert Entity Representations For Entity Ranking, Shubham Chatterjee, Laura Dietz

Computer Science Faculty Research & Creative Works

Entity-oriented search systems often learn vector representations of entities via the introductory paragraph from the Wikipedia page of the entity. As such representations are the same for every query, our hypothesis is that the representations are not ideal for IR tasks. In this work, we present BERT Entity Representations (BERT-ER) which are query-specific vector representations of entities obtained from text that describes how an entity is relevant for a query. Using BERT-ER in a downstream entity ranking system, we achieve a performance improvement of 13-42% (Mean Average Precision) over a system that uses the BERT embedding of the introductory paragraph …

Textual Emotion Detection Approaches: A Survey, Mahinda Mahmoud Samy Zidan, Ibrahim Elhenawy, Ahmed R. Abas, Mahmoud Othman

Future Computing and Informatics Journal

Over the past decades, social media attracted individuals to express their feelings on any topic or item, resulting in an incremental growth in the size of created data. These feelings and unstructured data paved the path for business organizations to gather information and build statistical analysis. Various machine learning and natural language processing-based approaches are used for sentiment and emotion analysis. Moreover, deep learning-based approaches recently gained popularity due to their remarkable performance in text analysis. This paper provides a comprehensive overview of the prominent machine learning models applied in emotion analysis. It explores various emotion analysis taxonomies, in addition …

Querai – A Smart Quiz Generator, Elton Da Silva, Fernando Aires Da Silva, Kim Jang Womg, Tai Teei Ho

ICT

QUERAI is a website powered by an Artificial Intelligence Question & Answer quiz generator model aiming to enhance students' learning experience and improve teachers' qualitative work by giving them more time to deal with other activities such as assignment correction, general grading, and class preparation.

A Novel Hybrid Algorithm For Morphological Analysis: Artificial Neural-Net-Xmor, Ayla Kayabaş, Ahmet Ercan Topcu, Özkan Kiliç

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, we present a novel algorithm that combines a rule-based approach and an artificial neural network-based approach in morphological analysis. The usage of hybrid models including both techniques is evaluated for performance improvements. The proposed hybrid algorithm is based on the idea of the dynamic generation of an artificial neural network according to two-level phonological rules. In this study, the combination of linguistic parsing, a neural network-based error correction model, and statistical filtering is utilized to increase the coverage of pure morphological analysis. We experimented hybrid algorithm applying rule-based and long short-term memory-based (LSTM-based) techniques, and the results …

Automatic Keyword Assignment System For Medical Research Articles Using Nearest-Neighbor Searches, Fati̇h Di̇lmaç, Adi̇l Alpkoçak

Turkish Journal of Electrical Engineering and Computer Sciences

Assigning accurate keywords to research articles is increasingly important concern. Keywords should be selected meticulously to describe the article well since keywords play an important role in matching readers with research articles in order to reach a bigger audience. So, improper selection of keywords may result in less attraction to readers which results in degradation in its audience. Hence, we designed and developed an automatic keyword assignment system (AKAS) for research articles based on k-nearest neighbor (k-NN) and threshold-nearest neighbor (t-NN) accompanied with information retrieval systems (IRS), which is a corpus-based method by utilizing IRS using the Medline dataset in …

Learning Term Weights By Overfitting Pairwise Ranking Loss, Ömer Şahi̇n, İlyas Çi̇çekli̇, Gönenç Ercan

Turkish Journal of Electrical Engineering and Computer Sciences

A search engine strikes a balance between effectiveness and efficiency to retrieve the best documents in a scalable way. Recent deep learning-based ranker methods are proving to be effective and improving the state-of-the-art in relevancy metrics. However, as opposed to index-based retrieval methods, neural rankers like bidirectional encoder representations from transformers (BERT) do not scale to large datasets. In this article, we propose a query term weighting method that can be used with a standard inverted index without modifying it. Query term weights are learned using relevant and irrelevant document pairs for each query, using a pairwise ranking loss. The …