Digital Commons Network^™

A Neutrosophic Decision-Making Model For Determining Young People’S Active Engagement, Mihaela Colhon, Monica Tilea, Ana Gonzalez-Marcos, Alina Resceanu, Florentin Smarandache, Fermin Navaridas-Naida

Branch Mathematics and Statistics Faculty and Staff Publications

Young people’s skills and attitudes must be observed, studied and evaluated in order to create appropriate models that would serve an educational purpose. In this paper, we propose a decision-making model with the aim to detect certain attitudinal and behavioural patterns of actively engaged young people. The data used in this research resulted from a questionnaire drawn up by a group of researchers from 6 European countries with the aim to investigate the youth’s awareness about the Sustainable Development Goals and their engagement as active agents of development and change at regional level. For the purpose of this study, we …

Solar Power Plant Location Selection Problem By Using Electre-Iii Method In Pythagorean Neutrosophic Programming Approach (A Case Study On Green Energy In India), Florentin Smarandache, Rajesh Kumar Saini, Ashik Ahirwar Ahirwa

Branch Mathematics and Statistics Faculty and Staff Publications

India dropped its target of generating 500 GW of renewable energy capacity from non responsibilities to ations Framework Convention on reducing carbon Abstract: India dropped its target of 500 GW of renewable energy capacity fossil fuel sources by 2030. Its responsibilities the United Nations Framework Conven Climate Change [UNFCCC],and reducing radiations by one billion tonnes by the end of the decade at the COP26 conference, held in Glasgow in November 2022.Researchers are continually searching for inexhaustible and reasonable energy sources. Solar energy is one of the greenest sources of energy and is also one of the cleanest. The most important …

Linguistic Multidimensional Spaces, W. B. Vasantha Kandasamy, Ilanthenral K., Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

This book extends the concept of linguistic coordinate geometry using linguistic planes or semi-linguistic planes. In the case of coordinate planes, we are always guaranteed of the distance between any two points in that plane. However, in the case of linguistic and semi-linguistic planes, we can not always determine the linguistic distance between any two points. This is the first limitation of linguistic planes and semi-linguistic planes.

Special Subset Linguistic Topological Spaces, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this book, authors, for the first time, introduce the new notion of special subset linguistic topological spaces using linguistic square matrices. This book is organized into three chapters. Chapter One supplies the reader with the concept of ling set, ling variable, ling continuum, etc. Specific basic linguistic algebraic structures, like linguistic semigroup linguistic monoid, are introduced. Also, algebraic structures to linguistic square matrices are defined and described with examples. For the first time, non-commutative linguistic topological spaces are introduced. The notion of a linguistic special subset of doubly non-commutative topological spaces of linguistic topological spaces is introduced in this …

Approach To The Personnel Selection Process In A Group Decision-Making Environment Based On The Psi Method, Maja Stanujkic, Gabrijela Popovic, Dragisa Stanujkic, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

The selection of adequate personnel is significant for the successful operation of companies. Therefore, this article observed the application of the Preference Selection Index (PSI) method for evaluating personnel in the conditions of group decision-making. The PSI method was chosen because it does not require determining the weights of the criteria, which is why it can be suitable for application by HR managers unfamiliar with the application of multiple criteria decision-making methods.

Beyond Negation And Excluded Middle: An Exploration To Embrace The Otherness Beyond Classical Logic System And Into Neutrosophic Logic, Florentin Smarandache, Victor Christianto

Branch Mathematics and Statistics Faculty and Staff Publications

As part of our small contribution in dialogue toward better peace development and reconciliation studies, and following Toffler & Toffler’s War and Antiwar (1993), the present article delves into a realm of logic beyond the traditional confines of negation and the excluded middle principle, exploring the nuances of "Otherness" that transcend classical and Nagatomo logics. Departing from the foundational premises of classical Aristotelian logic systems, this exploration ventures into alternative realms of reasoning, specifically examining Neutrosophic Logic and Klein bottle logic (cf. Smarandache, 2005). The study challenges conventional boundaries and explores the implications of embracing paradoxes and self-reference in logic …

Robots And Reference Services, Abdullahi Olayinka Isiaka, Biliamin Abiola Aremu, Abdulfatai Soliu, Fahisat Romoke Isiaq

Library Philosophy and Practice (e-journal)

Abstract

This paper explores the integration of robots into reference services in various library and information settings. The use of robots in these contexts has gained momentum in recent years, offering innovative solutions to enhance user experiences, improve access to information, and expand the capabilities of reference librarians. This paper reviews the current state of robots in reference services, discusses their potential benefits and challenges, and examines case studies to illustrate their practical applications. Furthermore, it offers insights into the future prospects and ethical considerations associated with the integration of robots in this domain. This paper delves into the fascinating …

Seven Pillars For The Future Of Artificial Intelligence, Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho

Research Collection School Of Computing and Information Systems

In recent years, AI research has showcased tremendous potential to impact positively humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision-making, sense disambiguation, sarcasm detection, and narrative understanding, as these require advanced kinds of reasoning, e.g., commonsense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely: Multidisciplinarity, Task Decomposition, Parallel Analogy, Symbol Grounding, Similarity Measure, …

Champions For Social Good: How Can We Discover Social Sentiment And Attitude-Driven Patterns In Prosocial Communication?, Raghava Rao Mukkamala, Robert J. Kauffman, Helle Zinner Henriksen

Research Collection School Of Computing and Information Systems

The UN High Commissioner on Refugees (UNHCR) is pursuing a social media strategy to inform people about displaced populations and refugee emergencies. It is actively engaging public figures to increase awareness through its prosocial communications and improve social informedness and support for policy changes in its services. We studied the Twitter communications of UNHCR social media champions and investigated their role as high-profile influencers. In this study, we offer a design science research and data analytics framework and propositions based on the social informedness theory we propose in this paper to assess communication about UNHCR’s mission. Two variables—refugee-emergency and champion …

Seqadver: Automatic Payload Construction And Injection In Sequence-Based Android Adversarial Attack, Fei Zhang, Ruitao Feng, Xiaofei Xie, Xiaohong Li, Lianshuan Shi

Research Collection School Of Computing and Information Systems

Machine learning has achieved a great success in the field of Android malware detection. In order to avoid being caught by these ML-based Android malware detection, malware authors are inclined to initiate adversarial sample attacks by tampering with mobile applications. Although machine learning has high capability, it lacks robustness against adversarial attacks. Currently, many of the adversarial attacking tools not only inject dead code into target applications, which can never be executed, but also require the injection of many benign features into a malicious APK. This can be easily noticeable by program analysis techniques. In this paper, we propose SeqAdver, …

Estimating Potential Exposure From Abandoned Uranium Mine Sites Through Machine Learning Classification Of Animal Behavior: A Case Study In The Navajo Nation, Christopher Girlamo

Geography ETDs

Waste from Abandoned Uranium Mines (AUMs) has negatively impacted Indigenous communities for generations. To answer these concerns, this research collaborated with Navajo Nation community members in Cove, AZ and Red Valley, AZ in order to better predict the potential AUM waste exposure their flocks face. A GIS-Multi-Criteria Decision Analysis (GIS-MCDA) model quantified potential exposure to AUM waste. Animal behavior was classified based on GPS and accelerometer data utilizing a Hidden Markov Model (HMM). The results of both the GIS-MCDA and animal behavior classification modeling was combined into a cumulative exposure potential value for each animal. This study found that livestock …

Digital Solutions Observed In Clinical Trials: A Formative Feasibility Scoping Review, Taylor M Harrison, Sungrim Moon, Liwei Wang, Sunyang Fu, Hongfang Liu

Faculty, Staff and Student Publications

Growing digital access accelerates digital transformation of clinical trials where digital solutions (DSs) are increasingly and widely leveraged for improving trial efficiency, effectiveness, and accessibility. Many factors impact DS success including technology barriers, privacy concerns, or user engagement activities. It is unclear how those factors are considered or reported in the literature. Here, we perform a formative feasibility scoping review to identify gaps impacting DS quality and reproducibility in trials. Articles containing digital terms published in English from 2009 to 2022 were collected (n=4,167). 130 articles published between 2016 and 2022 were randomly selected for full-text review. Eligible articles (n=100) …

Dental Claires: Contrastive Language Image Retrieval Search For Dental Research, Tanjida Kabir, Luyao Chen, Muhammad F Walji, Luca Giancardo, Xiaoqian Jiang, Shayan Shams

Faculty, Staff and Student Publications

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design a search tool that uses a user's query for oral-related research. The proposed framework,

Use Gpt-J Prompt Generation With Roberta For Ner Models On Diagnosis Extraction Of Periodontal Diagnosis From Electronic Dental Records, Yao-Shun Chuang, Xiaoqian Jiang, Chun-Teh Lee, Ryan Brandon, Duong Tran, Oluwabunmi Tokede, Muhammad F Walji

Faculty, Staff and Student Publications

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. …

A Novel Nih Research Grant Recommender Using Bert, Jie Zhu, Braja Gopal Patra, Hulin Wu, Ashraf Yaseen

Faculty, Staff and Student Publications

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the …

Sensitive Data Detection With High-Throughput Machine Learning Models In Electrical Health Records, Kai Zhang, Xiaoqian Jiang

Faculty, Staff and Student Publications

In the era of big data, there is an increasing need for healthcare providers, communities, and researchers to share data and collaborate to improve health outcomes, generate valuable insights, and advance research. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) is a federal law designed to protect sensitive health information by defining regulations for protected health information (PHI). However, it does not provide efficient tools for detecting or removing PHI before data sharing. One of the challenges in this area of research is the heterogeneous nature of PHI fields in data across different parties. This variability makes rule-based …

Large Language Models For Healthcare Data Augmentation: An Example On Patient-Trial Matching, Jiayi Yuan, Ruixiang Tang, Xiaoqian Jiang, Xia Hu

Faculty, Staff and Student Publications

The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security …

Local Contrastive Learning For Medical Image Recognition, Syed A Rizvi, Ruixiang Tang, Xiaoqian Jiang, Xiaotian Ma, Xia Hu

Faculty, Staff and Student Publications

The proliferation of Deep Learning (DL)-based methods for radiographic image analysis has created a great demand for expert-labeled radiology data. Recent self-supervised frameworks have alleviated the need for expert labeling by obtaining supervision from associated radiology reports. These frameworks, however, struggle to distinguish the subtle differences between different pathologies in medical images. Additionally, many of them do not provide interpretation between image regions and text, making it difficult for radiologists to assess model predictions. In this work, we propose Local Region Contrastive Learning (LRCLR), a flexible fine-tuning framework that adds layers for significant image region selection as well as cross-modality …

Understanding Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel

Research Collection Lee Kong Chian School Of Business

The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions. Consumers increasingly want hyperpersonalised interactions that are more frequent and proactive, while financial institutions have a growing need to cater to consumers’ new demands. Financial institutions, such as banks, continuously adapt to the latest technologies to keep pace with evolving customer behaviours, needs, and experiences. One such emerging technology is artificial intelligence (AI). Many organisations realise the potential of AI; however, a human-centred AI system must be capable of understanding human characteristics and making decisions like humans. This paper presents …

Perbandingan Metode Fuzzy Times Series Cheng Dan Fuzzy Times Series Markov Chain Dalam Memprediksi Tingkat Pengangguran Terbuka Di Bali, Kadek Tresna Dwipayana, I Gusti Nyoman Yudi Hartawan, I Made Candiasa

PYTHAGORAS : Jurnal Matematika dan Pendidikan Matematika

Penelitian ini bertujuan menemukan model untuk tingkat pengangguran terbuka di Bali dengan Fuzzy Times series Cheng (FTS Cheng) dan Fuzzy Times series Markov Chain (FTS Markov Chain) serta membandingkan tingkat akurasi kedua model tersebut. Jenis penelitian ini adalah jenis penelitian kuantitatif. Data tingkat pengangguran terbuka di Bali yang digunakan sebanyak 34 dari periode Februari 2006 sampai dengan Agustus 2021. Dari 34 data kemudian dibagi menjadi dua dengan proporsi 30% data latih dan 70% data uji. Tahapan penelitian yang dilakukan dengan menghitung FTS Cheng dan FTS Markov Chain pada data latih kemudian dilanjutkan pada data uji. Selanjutnya model FTS Cheng dan …

Historical Tsunami Observability For Izu–Bonin–Mariana Sources, Walter Szeliga, Rachelle Reisinger, Breanyn T. Macinnes

Geological Sciences Faculty Scholarship

The Izu–Bonin–Mariana Subduction System (IBM) is one of the longest subduction zones in the world with no instrumental history of shallow focus, great earthquakes (Mw > 8). Over the last 50 years, researchers have speculated on the reason for the absence of large magnitude, shallow seismicity on this plate interface, exploring factors from plate age to convergence rate. We approach the question from a different point of view: what if the IBM has hosted great earthquakes and no documentable evidence was left? To address the question of observability, we model expected tsunami wave heights from nine great earthquake scenarios on the …

Indonesian Language Term Extraction Using Multi-Task Neural Network, Joan Santoso, Esther Irawati Setiawan, Fransiskus Xaverius Ferdinandus, Gunawan Gunawan, Leonel Hernandez Collantes

Knowledge Engineering and Data Science

The rapidly expanding size of data makes it difficult to extricate information and store it as computerized knowledge. Relation extraction and term extraction play a crucial role in resolving this issue. Automatically finding a concealed relationship between terms that appear in the text can help people build computer-based knowledge more quickly. Term extraction is required as one of the components because identifying terms that play a significant role in the text is the essential step before determining their relationship. We propose an end-to-end system capable of extracting terms from text to address this Indonesian language issue. Our method combines two …

Examining Early Elementary Computer Science Identity Repertoires Within A Curriculum: Implications For Epistemologically Pluralistic Identities, Eleanor Richard, Shakhnoza Kayumova

Journal of Computer Science Integration

As computer science (CS) enters an increasing number of elementary classrooms, researchers must investigate the representations of what kinds of people are presented as doing computer science within CS curricula. In this paper, we explore a widely used, freely accessible, web-based, early elementary CS curriculum to examine the kinds of identity repertoires (behaviors, actions, skills, and socioemotional norms) that are promoted as representative of being/becoming a CS person. More specifically, we draw on identity studies and employ critical discourse analysis to examine how the kinds of norms and repertoires of CS practice made available in the curricular materials might construct …

Doctoral Studies As Learning To Rename The World, Hyleen Mariaye

Journal of Multicultural Affairs

The reflective experience documented in this paper engages with doctoral learning from Freire’s (1968/2000) conceptual lens of naming the world. Written from the narrative lens of the supervisor, it considers how doctoral level studies in education can position both the supervisor and the candidates as agents actively reconstructing their understanding of the world and their place in it. The doctoral journey is viewed as praxis compelling researchers to expand their frames for reading the world, accommodating the other, including multiple voices and thus demonstrating commitment to a global and yet constantly contested notion of citizenship.

Poetry And Praxis: Lessons From An Activist Educator, Dr. Emmanuel Tabi

Journal of Multicultural Affairs

Drawing on data from a narrative multi-case study based in Toronto, Canada, this article discusses the lived experiences of one Black activist. Utilizing critical race theory, new literacy studies and the rhetoric of cultural production as theoretical frameworks, the article foregrounds the work of Ebele, a Toronto activist whose work supported the educational trajectories and emotional well-being of Black students, many of whom reported being marginalized in school. Through his creative labor, Ebele directly addressed the sociology of anti-Black racism that deeply influences the lives of Afrodiasporic people in Canada. This article continues the conversation about what it means to …

Creating Data From Unstructured Text With Context Rule Assisted Machine Learning (Craml), Stephen Meisenbacher, Peter Norlander

School of Business: Faculty Publications and Other Works

Popular approaches to building data from unstructured text come with limitations, such as scalability, interpretability, replicability, and real-world applicability. These can be overcome with Context Rule Assisted Machine Learning (CRAML), a method and no-code suite of software tools that builds structured, labeled datasets which are accurate and reproducible. CRAML enables domain experts to access uncommon constructs within a document corpus in a low-resource, transparent, and flexible manner. CRAML produces document-level datasets for quantitative research and makes qualitative classification schemes scalable over large volumes of text. We demonstrate that the method is useful for bibliographic analysis, transparent analysis of proprietary data, …

Two Studies On Binary Program Stylometry: A White-Box Analysis And A Deep Learning Analysis On Some Crypto Ransomware, Alexander Mitchell

Masters Theses, 2020-current

Computer programmers often leave their individual programming styles in source code. Recent studies show that contrary to a popular belief, many of such programming styles can survive, in controlled environments, code compilation into binary. From the binary programming styles can be effectively retrieved for enhanced binary authorship attribution; such binary authorship attribution is often called binary program stylometry. In this thesis, we first perform a white-box impact analysis of various factors in code compilation on programming styles. For the MS Windows platform, we study the impact of multiple compilers, including gcc, Clang, and MSVC, their optimization levels, symbol stripping, and …

Evaluating Equity In Transportation Greenhouse Gas Emission Reduction Strategies In Local Climate Action Planning In Oakland, California, Eliza J. Kane

Master's Projects and Capstones

Emissions from California’s transportation sector contribute to global climate change and impact local air quality and public health. Forty-one percent of California’s greenhouse gas emissions in 2019 were from transportation. Transportation emissions are also a source of many health-harming air pollutants such as carbon monoxide, volatile organic compounds, and particulate matter. Disadvantaged communities in California experience disproportionate impacts from transportation emissions and suffer worse health effects, including higher incidences of asthma, cardiovascular disease, and premature death. Cities have led the way in policymaking to mitigate and adapt to climate change, including creating Climate Action Plans (CAPs) to propose strategies to …

Low-Resource Machine Learning Techniques For The Analysis Of Online Social Media Textual Data, Toktam Amanzadeh Oghaz

Electronic Theses and Dissertations, 2020-2023

Low-resource and label-efficient machine learning methods can be described as the family of statistical and machine learning techniques that can achieve high performance without needing a substantial amount of labeled data. These methods include both unsupervised learning techniques, such as LDA, and supervised methods, such as active learning, each providing different benefits. Thus, this dissertation is devoted to the design and analysis of unsupervised and supervised techniques to provide solutions for the following problems: Unsupervised narrative summary extraction for social media content, Social media text classification with Active Learning (AL), Investigating restrictions and benefits of using Curriculum Learning (CL) for …

Full-Text Search Using Elasticsearch, Akash Shrestha

Masters Projects

Search engines have changed the way we use the internet. They can search or filter out relevant and valuable content of interest to the users. But many of the applications we use today lack search or are just poor. So how can we leverage the same power of search engines in our applications? This project aims to look at “Full-Text Search” which allows us to do a text-based search in text-intensive data. The search will be performed by matching any, or all words of the query exactly or with some relevancy against the indexes created by the searching tool. The …