Digital Commons Network^™

An Aspect Performance-Aware Hypergraph Neural Network For Review-Based Recommendation, Junrui Liu, Tong Li, Di Wu, Zifang Tang, Yuan Fang, Zhen Yang

Research Collection School Of Computing and Information Systems

Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise recommendations, which has not been taken into account by existing approaches, due to lack of data. In this paper, we propose an aspect performance-aware hypergraph neural network (APH) for the review-based recommendation, which learns the performance of items from the conflicting sentiment polarity of user reviews. Specifically, APH comprehensively models the relationships among users, …

Quantifying The Role Of Active Listening And Reassurance In Virtual Health Coach Interactions, Ghulam Hussain, Brian Keegan, Robert Ross

Conference papers

Conversational Agents have the potential to support healthcare through coaching exercise routines, but are still lacking in demonstrating authentic social behaviours to support engagement. To this end, we present a series of experiments that we conducted in order to investigate how automated health care coaches can be more effective when their interaction style is tailored to demonstrate qualities associated with a good bedside manner, namely active listening and reassurance. To test this, we first developed a dataset of 135 dialogue excerpts from three distinct sources, i.e., original, handcrafted and LLMs, the latter two of which were tuned to demonstrate specific …

Destroy, Rebuild, Repeat: How To Break The Climate Disaster Cycle, Mark Nevitt

Vanderbilt Law Review

Climate change is fundamentally reshaping how we live, where we live, and whether we invest in or retreat from climate-exposed communities—but climate and disaster law is not changing with the climate. This legal latency is driven by antiquated statutes, doctrines, and policies that have not kept pace with the climate moment. Ex ante adaptation decisions governing where to live are life and death choices that shape ex post disaster response. Laws and policies should facilitate sound climate decisionmaking, but too often they frustrate individual and governmental decisions on whether to stay or retreat. In this Article, I argue that laws …

Bridging Cognitive Psychology And Natural Language Processing: A Bias-Detection Framework For Large Language Models, Stephen Surles

Masters Theses & Doctoral Dissertations

Bias in large language models (LLMs) poses substantial risks to fair and accurate information processing, particularly in high-impact contexts such as news dissemination and content moderation. These models often learn and unintentionally amplify systematic biases, ranging from confirmation and negativity to anchoring and partisanship, present in the data on which they are trained, thereby distorting public discourse and potentially fueling misinformation. Building upon theories in cognitive psychology and AI-decision-making, the Cognitive Bias in Artificial Intelligence Theory (CoBAIT) becomes the interdisciplinary theoretical foundation of this work.

Drawing on both cognitive bias theory and a design science approach, this dissertation develops a …

Cal Poly Construction Spanish Class, Josue E. Basilio

Construction Management

This project develops a Construction Spanish & Communication course for Cal Poly’s Construction Management program to address the growing need for bilingual communication on job sites. The course equips students with essential Spanish language skills for safety meetings, work instructions, and team coordination, improving job site efficiency and inclusivity. Deliverables include a detailed course outline in preparation for the semester conversion, curriculum, course catalog entry, and a first-day lecture presentation. The course blends lecture-based instruction with hands-on activities like role-playing, translation exercises, and real-world simulations. Assessments include scenario-based quizzes, a reflection essay, participation grading, and a final exam to ensure …

The Cure-Comm Framework: A Guide For Infusing Cures With Science Communication, Rebekka Darner, Ebenezer Korkor

Faculty Publications – Biological Sciences

Researchers have employed diverse instructional strategies to impart science communication (SciComm) skills, a cherished 21st-century skill into undergraduate students. We reviewed the pedagogical strategies used in developing SciComm skills in undergraduates. This review revealed that analyzing data and scientific literature, increasing exposure to multiple writing contexts, providing multiple opportunities to present findings orally, and providing feedback are used to teach SciComm skills. Many instructors who implement course-based undergraduate research experiences (CURE) report improved SciComm skills as one of the benefits of CUREs. To decipher points of intersection among CUREs and SciComm pedagogical strategies, we also reviewed the literature on pedagogical …

Effectiveness Of Social Skills Training Interventions For Children With Autism Spectrum Disorder: A Systematic Review And Meta-Analysis, Fatimah S. Alahmari, Abdulhadi A. Alhabbad, Hussain A. Alshamrani, Mohammed A. Almuqbil

Saudi Medical Journal

Objectives: To evaluatethe effectiveness of social skills training (SST) interventions for children with autism spectrum disorder (ASD). Methods: A systematic review and meta-analysis were carried out to identify relevant studies published between 2000-2023. The study was conductedin accordance with the PRISMA guidelines. Studies were included if they met the following criteria: randomized controlled trials (RCTs) or quasi-RCTs, participants were children with ASD aged 3-18 years, SST interventions were delivered individually or in groups, and social skills were assessed using standardized measures. Effect sizes were calculated using Cohen’s d, and meta-analyses were carried out using a random-effects model. Results: A total …

Law, Politics, And The Academy In Asia: Navigating Constraints As Public Law Scholars, De Maartje Visser, Qian Liu, Victor V. Ramraj

Research Collection Yong Pung How School Of Law

How do academics interested in the study of legal topics that implicate the state relate to and deal with pressures that shape the space available to conduct research? This article examines the nature and impact of such pressures on Asia-focused public law scholars who must contend with a more diverse socio-political environment than the liberal democratic setting in which questions of academic freedom are typically explored. We find that the Asia-centric academy is affected by a wide range of constraints that notably extends beyond intra-institutional demands to those put in place by the state. This article also highlights how the …

Overcoming The Dilemma Of Subjectivism Vs. Objectivism In The Reformed Theology And Hermeneutics Of G. C. Berkouwer, Eduardo Echeverria

Pro Rege

No abstract provided.

The Learning Crisis In The United States Three Years After Covid-19, Harry Anthony Patrinos, Maciej Jakubowski, Tomasz Gajderowicz

Education Reform Faculty and Graduate Students Publications

The COVID-19 pandemic caused widespread disruptions to education, with school closures affecting over one billion children. These closures, aimed at reducing virus transmission, resulted in significant learning losses, particularly in mathematics and science. Using United States data from TIMSS, this study analyzes the impact of school closure on learning outcomes. The losses amount to 0.36 SD for mathematics and 0.16 SD for science. The declines are similar across grades. The average decline in mathematics performance among U.S. students is substantially greater than the global average. n science, the decline observed among U.S. students does not significantly differ from the global …

"Distinct And Separate Issues": Examining Us Adults' Attitudes Toward Abortion During Covid-19, Maria Montenegro, Danny Valdez, Frederica Jackson, Wen-Juo Lo, Ronna C. Turner, Brandon L. Crawford, Kristen N. Jozkowski

Counseling, Leadership, and Research Methods Faculty Publications and Presentations

BackgroundThe COVID-19 pandemic reshaped people's healthcare experiences and access to healthcare, including abortion. In response to the COVID-19 outbreak, some policymakers claimed that abortion is a nonessential service and should be restricted. In contrast, other policymakers contended that abortion is time-sensitive essential healthcare, and access to it should be protected. These efforts put access to abortion into the public arena during the onset of the pandemic. We examined whether people perceived the pandemic changed their attitudes toward abortion and their rationale for whether their support for abortion increased, decreased, or remained the same.MethodWe administered a web-based survey to US-based English …

Explainable Neural Networks With Guarantees: A Sparse Estimation Approach, Antoine Ledent, Peng Liu

Research Collection School Of Computing and Information Systems

Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictiveness and explainability. Our model is designed as a linear combination of a sparse set of jointly learned features, each derived from a different trainable function applied to a single 1-dimensional input feature. Leveraging the ability to learn arbitrarily complex relationships, our neural network architecture enables automatic selection of a sparse set of important features, with the final prediction being …

Varium: Variational Autoencoder For Multi-Interest Representation With Inter-User Memory, Nhu Thuat Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Frameworks for discovering multiple user interest factors based on Variational AutoEncoder (VAE) has demonstrated competitive recommendation performance. However, as VAE only considers one user as input at a time, sharing across like-minded users may not be adequately facilitated. Moreover, interest sharing between users is not always available and thus, poses a challenge for VAE to explicitly model this information. To resolve this, we introduce an inter-user memory-based mechanism to unsupervisedly discover latent interest sharing between users under VAE framework. Concretely, we design a memory including an array of prototypes, each hypothetically representing a group of users sharing a particular interest. …

Multisfl: Towards Accurate Split Federated Learning Via Multi-Model Aggregation And Knowledge Replay, Zeke Xia, Ming Hu, Dengke Yan, Ruixuan Liu, Anran Li, Xiaofei Xie, Mingsong Chen

Research Collection School Of Computing and Information Systems

Although Split Federated Learning (SFL) effectively enables knowledge sharing among resource-constrained clients, it suffers from low training performance due to the neglect of data heterogeneity and catastrophic forgetting problems. To address these issues, we propose a novel SFL approach named MultiSFL, which adopts i) an effective multimodel aggregation mechanism to alleviate gradient divergence caused by heterogeneous data and ii) a novel knowledge replay strategy to deal with the catastrophic forgetting problem. MultiSFL adopts two servers (i.e., the fed server and main server) to maintain multiple branch models for local training and an aggregated master model for knowledge sharing among branch …

Lightprof: A Lightweight Reasoning Framework For Large Language Model On Knowledge Graph, Tu Ao, Yanhua Yu, Yuling Wang, Yang Deng, Zirui Guo, Liang Pang, Pinghui Wang, Tat-Seng Chua, Xiao Zhang, Zhen Cai

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly or produce harmful results. Knowledge Graphs (KGs) provide rich and reliable contextual information for the reasoning process of LLMs by structurally organizing and connecting a wide range of entities and relations. Existing KG-based LLM reasoning methods only inject KGs’ knowledge into prompts in a textual form, ignoring its structural information. Moreover, they mostly rely on close-source models or open-source models with large parameters, which poses challenges to high resource consumption. To address this, we propose a …

Simulation-Free Hierarchical Latent Policy Planning For Proactive Dialogues, Tao He, Lizi Liao, Yixin Cao, Yuanxing Liu, Yiheng Sun, Zerui Chen, Ming Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Recent advancements in proactive dialogues have garnered significant attention, particularly for more complex objectives (e.g. emotion support and persuasion). Unlike traditional task-oriented dialogues, proactive dialogues demand advanced policy planning and adaptability, requiring rich scenarios and comprehensive policy repositories to develop such systems. However, existing approaches tend to rely on Large Language Models (LLMs) for user simulation and online learning, leading to biases that diverge from realistic scenarios and result in suboptimal efficiency. Moreover, these methods depend on manually defined, context-independent, coarse-grained policies, which not only incur high expert costs but also raise concerns regarding their completeness. In our work, we …

Evaluating Software Development Agents: Patch Patterns, Code Quality, And Issue Complexity In Real-World Github Scenarios, Zhi Chen, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

In recent years, AI-based software engineering has progressed from pre-trained models to advanced agentic workflows, with Software Development Agents representing the next major leap. These agents, capable of reasoning, planning, and interacting with external environments, offer promising solutions to complex software engineering tasks. However, while much research has evaluated code generated by large language models (LLMs), comprehensive studies on agent-generated patches, particularly in real-world settings, are lacking. This study addresses that gap by evaluating 4,892 patches from 10 top-ranked agents on 500 real-world GitHub issues from SWE-Bench Verified, focusing on their impact on code quality. Our analysis shows no single …

Learning To Identify Seen, Unseen And Unknown In The Open World: A Practical Setting For Zero-Shot Learning, Sethupathy Parameswaran, Yuan Fang, Chandan Gautam, Savitha Ramasamy, Xiaoli Li

Research Collection School Of Computing and Information Systems

As vision-language models advance, addressing the Zero-Shot Learning (ZSL) problem in the open world becomes increasingly crucial. Specifically, a robust model must handle three types of samples during inference: seen classes with visual and semantic information provided in training, unseen classes with only the semantic information in training, and unknown samples with no prior information from training. Existing methods either handle seen and unseen classes together (ZSL) or seen and unknown classes (known as Open-Set Recognition, OSR). However, none addresses the simultaneous handling of all three, which we term Open-Set Zero-Shot Learning (OZSL). To address this problem, we propose a …

Token Turing Machines Are Efficient Vision Models, Purvish Jajal, Nick Eliopoulos, Benjamin Shiue-Hal Chou, George K. Thiruvathukal, James C. Davis, Yung-Hsiang Lu

Computer Science: Faculty Publications and Other Works

We propose Vision Token Turing Machines (ViTTM), an efficient, low-latency, memory-augmented Vision Transformer (ViT). Our approach builds on Neural Turing Machines (NTM) and Token Turing Machines (TTM), which were applied to NLP and sequential visual understanding tasks. ViTTMs are designed for non-sequential computer vision tasks such as image classification and segmentation. Our model creates two sets of tokens: process tokens and memory tokens; process tokens pass through encoder blocks and read-write from memory tokens at each encoder block in the network, allowing them to store and retrieve information from memory. By ensuring that there are fewer process tokens, we are …

Study Of Knowledge, Attitude, And Practice Of Self-Medication In Riobamba, Ecuador Based On An Indeterminate Likert Scale, Lizbeth Geovanna Silva-Guayasamín, Zully Mayra Romero-Orellana, Lisbeth Josefina Reales-Chacón, Isaac Germán Pérez-Vargas, Sulema De La Caridad Hernández-Batista, Carlos Antonio Escobar-Suárez, María López-Pino, Josselyn Gabriela Bonilla-Ayala

Neutrosophic Sets and Systems

Self-medication (SM) is a significant public health problem in Riobamba, Ecuador, characterized by the use of drugs without a prescription. This practice carries risks such as inappropriate drug use and antimicrobial resistance. This study assesses the prevalence, associated factors, and attitudes towards SM in Riobamba. To do so, data were collected from 383 city residents, who also responded to a survey. To ensure the accuracy of the results, we asked respondents to answer some questions based on an Indeterminate Likert Scale. In this way, people can evaluate each of the different opinions, thus obtaining a more reliable result of the …

Hypersoft Semi-Connected Spaces, Florentin Smarandache, V. Inthumathi, M. Amsaveni

Neutrosophic Sets and Systems

Neutrosophic sets are an important field of topology that deals with inconsistent, indeterminate, and uncertain circumstances in real life. Hypersoft sets have grown in popularity due to their ability to represent data as a collection of trait-valued disjoint sets that combine various attributes. Motivated by this concept, we introduce a new hyper-approach known as hypersoft semi-separated, which employs hypersoft semi-open sets [HSO]. This article presents several local properties of HSO sets, such as hypersoft semi-neighborhoods of hypersoft points and hypersoft semi-first countable spaces. Additionally, definitions of hypersoft semi-connected [HSCO] spaces, hypersoft locally semi-connected [HLSCO] spaces, and associated properties are provided. …

Predicting Customer Satisfaction Based On Neutrosophic Sets: Applied On Mobile Food Ordering Application, Walaa Elsanabary, Nouran M. Radwan

Neutrosophic Sets and Systems

Customers’ satisfaction prediction is a vital process for all business organizations to draw new customers and maintain existing customers. One of the most efficient methods to predict customers' satisfaction is classifying customers' feedback. In the real world, customer’s feedback is ambiguous, confusing and inconsistent. This option can be stated in neutrosophic logic as indeterminacy membership, associated with truth and falsity membership. In this study, a classification model based on neutrosophic sets to handle the inconsistency of customer responses is presented. Also, significant factors that impact customers' satisfaction are defined. In order to show the procedures and the application of the …

A Multi-Criteria Decision-Making Approach With Hyper Soft Set To Teaching Quality Assessment In English Language And Literature, Yuhua He

Neutrosophic Sets and Systems

The training ground for professional English speakers is the English departments of colleges and universities. Economic development and international interactions are impacted by the quality of their instruction. This study created an evaluation method with for evaluating the quality of English language and literature using questionnaire surveys, interviews, and a review of the literature. The multi-criteria decision making (MCDM) method is used in this study to evaluate the English langue and literature. Neutrosophic set is integrated with the MCDM method to deal with uncertainty and value information. This study uses the CRITIC method to compute the criteria weights. The CRITIC …

Neutrosophic Non-Newtonian And Geometric Measures: A Consistent Analytical Framework, Amer Darweesh, Kamel Al-Khaled, Mohammed Shakhatreh, Mahmood Shareef Ajeel, Nermin Abo Hasan

Neutrosophic Sets and Systems

The neutrosophic measure is a generalization of the classical measure in situations when the space contains some indeterminacy. In this paper, we introduce the concept of the Neutrosophic Geometric Measure, we also provide some results, and examples related to the Neutrosophic Geometric Measure. A classical measure of the objects determinate component, a classical measure of its indeterminate part, and a further classical measure of the objects opposite determinate part are the three classical measures that make up the neutrosophic measure. To de ne the Neutrosophic Geometric Measure, we introduce a new measure on R+ and call it the geometric Lebesgue …

University Mathematics Classroom Teaching Quality Assessment Based On Core Competencies Using The Dematel Approach And Single-Valued Neutrosophic Hypersoft Sets, Jingjing Qiao

Neutrosophic Sets and Systems

This study proposes a multi-criteria decision-making (MCDM) approach to evaluate the quality of University Mathematics Classroom Teaching based on core competencies. The MCDM approach is applied within the framework of the neutrosophic set to address vague and uncertain data. Unlike the hypersoft set, which handles multiple disjoint attribute-valued sets corresponding to various characteristics, the soft set operates with a single set of attributes. This study introduces the concept of Single-Valued Neutrosophic Hypersoft Expert Sets (SVNS), which integrate single-valued neutrosophic sets and hypersoft expert sets. Eight criteria were employed to construct the pairwise comparison matrix. The Decision-Making Trial and Evaluation Laboratory …

Intuitionistic Quadri-Partitioned Neutrosophic Sets With Extended Operators, Sai Anu P, Arulselvam A, Bobin A

Neutrosophic Sets and Systems

We present the concept of intuitionistic quadri-partitioned neutrosophic sets (IQ-PNSs) and extend it to intuitionistic quadri-partitioned neutrosophic soft sets (IQ-PNSSs). These sets introduce a constraint where the combined membership values for truth and falsity must not exceed one, while the indeterminacy and contradiction membership values are restricted to the interval [01], ensuring the total sum of all membership values remains below three. In addition, we de ne necessity and possibility operators, along with two novel operators, ( and ), on IQ-PNSSs and explore their properties. Furthermore, we introduce and analyze the operators , , and , as well as the …

Neutrosophic Analysis Of The Cultural Impact Of The Pinkullo In Llata: Integrating Chatgpt And Fsqca, Fredy Romulo Marcellini Morales, Betty Leon Trujillo, Esio Ocana Igarza, Roberto Carlos Cardenas Viviano

Neutrosophic Sets and Systems

This study explores the cultural impact of the Pinkullo, a traditional Andean aerophone, on the community of Llata, Peru, through an innovative approach that integrates neutrosophic set theory with computational methods. By employing fuzzy-set Qualitative Comparative Analysis (fsQCA) and a novel application of ChatGPT for neutrosophic sentiment analysis, the research investigates the complex interplay between the Pinkullo and various aspects of community life, including cultural participation, social impact, and cultural preservation. Textual data collected from surveys was analyzed using the "ChatGPT Constructor" add-on in Orange Data Mining to generate neutrosophic scores, representing the truth, indeterminacy, and falsity of expressed sentiments …

Non-Destructive Detection Of Fillet Fish Quality Using Mq135 Gas Sensor And Neutrosophic Logic-Enhanced System, M. Y. Shams, M. R. Darwesh, Roheet Bhatnagar, N. S. A. Al-Sattary, A. A. Salama, M. S. Ghoname

Neutrosophic Sets and Systems

This paper demonstrates the feasibility of using an electronic nose to assess fish quality by analyzing air quality and examining volatile organic compounds (VOCs) alongside physical variables, with pH, protein content, and VOCs serving as chemical reference points. Artificial intelligence algorithms were employed to predict quality and calculate regression coefficients. Using the reference neural network algorithm based on chemical and physical compounds, regression coefficients (R values) achieved were 0.99, 0.98, and 0.97, respectively. Additionally, ANFIS (Adaptive Neuro-Fuzzy Inference System) produced R values of 0.99, 0.85, and 0.98. Both fuzzy logic and ANFIS proved effective for predicting fish quality. Image processing …

Quality Evaluation In Hospital Operations Management Using Multi-Criteria Decision-Making With Plithogenic Approach To Address Uncertainty And Enhance Decision-Making Processes, Meng Zhang, Yafang Li

Neutrosophic Sets and Systems

Coordination of medical facilities' operations depends on operations management. Hospitals must function effectively to meet the medical demands of their patients, who get critical and emergency treatment. The decision support system for Quality Evaluation in Hospital Operations Management is created in this study. Plithogenic sets and multicriteria decision-making (MCDM) form the foundation of this decision assistance system. The study presents a novel approach to evaluating and rating Hospital Operations Management. Alternatives are ranked using the plithogenic reference-based normalization alternative ranking (RBNAR) technique. Thus, the algorithm for plithogenic-RBNAR is constructed and presented. An analysis of Hospital Operations Management is conducted using …

Ecological Efficiency Evaluation In Petrochemical Enterprises Using Plithogenic-Artasi Model, Hailing Chen, Dingqun Zhang

Neutrosophic Sets and Systems

The application of the ecological carrying capacity theory to the assessment of petrochemical companies' eco-efficiency is the main goal of this article. This study deviates from previous research that examines company eco-efficiency from a strategic and behavioral incentive standpoint. This study uses the multiple-criteria decision-making (MCDM) approach, considering both qualitative and quantitative factors. Plithogenic sets are used to compute expert importance levels in a decision support system for choosing Petrochemical Enterprises. The suggested plithogenic-based alternative ranking methodology based on adaptive standardized intervals (ARTASI) method is used to determine the ranking of Petrochemical Enterprises. The plithogenic-ARTASI model is a combination of …