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Full-Text Articles in Databases and Information Systems

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth May 2024

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth

Electronic Theses, Projects, and Dissertations

The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …

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Elevating Academic Administration: A Comprehensive Faculty Dashboard For Tracking Student Evaluations And Research, Musa M. Azeem Apr 2024

Elevating Academic Administration: A Comprehensive Faculty Dashboard For Tracking Student Evaluations And Research, Musa M. Azeem

Senior Theses

The USC Faculty Dashboard is a web application designed to revolutionize how department heads, professors, and instructors monitor progress and make decisions, providing a centralized hub for efficient data storage and analysis. Currently, there’s a gap in tools tailored for department heads to concisely manage the performance of their department, which our platform aims to fill. The USC Faculty Dashboard offers easy access to upload and view student evaluation and research information, empowering department heads to evaluate the performance of faculty members and seamlessly track their research grants, publications, and expenditures. Furthermore, professors and instructors gain personalized performance analysis tools, …

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Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim Mar 2024

Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and …

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T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen Mar 2024

T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with …

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Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan Mar 2024

Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan

Research Collection School Of Computing and Information Systems

Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number …

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Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft Feb 2024

Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft

Research Collection School Of Computing and Information Systems

Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …

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Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong Feb 2024

Improving Conversational Recommender System Via Contextual And Time-Aware Modeling With Less Domain-Specific Knowledge, Lingzhi Wang, Shafiq Joty, Wei Gao, Xingshan Zeng, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of generation and recommendation modules. Prior work on CRS tends to incorporate more external and domain-specific knowledge like item reviews to enhance performance. Despite the fact that the collection and annotation of the external domain-specific information needs much human effort and degenerates the generalizability, too much extra knowledge introduces more difficulty to balance among them. Therefore, we propose to fully discover and extract the internal knowledge from the context. We capture both entity-level and contextual-level representations to jointly model user …

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Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman Jan 2024

Railroad Condition Monitoring Using Distributed Acoustic Sensing And Deep Learning Techniques, Md Arifur Rahman

Electronic Theses and Dissertations

Proper condition monitoring has been a major issue among railroad administrations since it might cause catastrophic dilemmas that lead to fatalities or damage to the infrastructure. Although various aspects of train safety have been conducted by scholars, in-motion monitoring detection of defect occurrence, cause, and severity is still a big concern. Hence extensive studies are still required to enhance the accuracy of inspection methods for railroad condition monitoring (CM). Distributed acoustic sensing (DAS) has been recognized as a promising method because of its sensing capabilities over long distances and for massive structures. As DAS produces large datasets, algorithms for precise …

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Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek Dec 2023

Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different …

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A Big Data Approach To Augmenting The Huff Model With Road Network And Mobility Data For Store Footfall Prediction, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau Dec 2023

A Big Data Approach To Augmenting The Huff Model With Road Network And Mobility Data For Store Footfall Prediction, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Conventional methodologies for new retail store catchment area and footfall estimation rely on ground surveys which are costly and time-consuming. This study augments existing research in footfall estimation through the innovative integration of mobility data and road network to create population-weighted centroids and delineate residential neighbourhoods via a community detection algorithm. Our findings are then used to enhance Huff Model which is commonly used in site selection and footfall estimation. Our approach demonstrated the vast potential residing within big data where we harness the power of mobility data and road network information, offering a cost-effective and scalable alternative. It obviates …

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Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon Dec 2023

Damage Detection With An Integrated Smart Composite Using A Magnetostriction-Based Nondestructive Evaluation Method: Integrating Machine Learning For Prediction, Christopher Nelon

All Dissertations

The development of composite materials for structural components necessitates methods for evaluating and characterizing their damage states after encountering loading conditions. Laminates fabricated from carbon fiber reinforced polymers (CFRPs) are lightweight alternatives to metallic plates; thus, their usage has increased in performance industries such as aerospace and automotive. Additive manufacturing (AM) has experienced a similar growth as composite material inclusion because of its advantages over traditional manufacturing methods. Fabrication with composite laminates and additive manufacturing, specifically fused filament fabrication (fused deposition modeling), requires material to be placed layer-by-layer. If adjacent plies/layers lose adhesion during fabrication or operational usage, the strength …

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Heterogeneous Graph Neural Network With Multi-View Representation Learning, Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu Nov 2023

Heterogeneous Graph Neural Network With Multi-View Representation Learning, Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu

Research Collection School Of Computing and Information Systems

In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain …

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Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu Nov 2023

Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu

Research Collection School Of Computing and Information Systems

With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …

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Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun Nov 2023

Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun

Research Collection School Of Computing and Information Systems

Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …

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Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen Nov 2023

Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen

Research Collection School Of Computing and Information Systems

Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performances on complex questions are still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances in KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and …

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Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian Oct 2023

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

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Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim Oct 2023

Your Cursor Reveals: On Analyzing Workers’ Browsing Behavior And Annotation Quality In Crowdsourcing Tasks, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In this work, we investigate the connection between browsing behavior and task quality of crowdsourcing workers performing annotation tasks that require information judgements. Such information judgements are often required to derive ground truth answers to information retrieval queries. We explore the use of workers’ browsing behavior to directly determine their annotation result quality. We hypothesize user attention to be the main factor contributing to a worker’s annotation quality. To predict annotation quality at the task level, we model two aspects of task-specific user attention, also known as general and semantic user attentions . Both aspects of user attention can be …

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Robust Bidirectional Poly-Matching, Ween Jiann Lee, Maksim Tkachenko, Hady Wirawan Lauw Oct 2023

Robust Bidirectional Poly-Matching, Ween Jiann Lee, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A fundamental problem in many scenarios is to match entities across two data sources. It is frequently presumed in prior work that entities to be matched are of comparable granularity. In this work, we address one-to-many or poly-matching in the scenario where entities have varying granularity. A distinctive feature of our problem is its bidirectional nature, where the 'one' or the 'many' could come from either source arbitrarily. Moreover, to deal with diverse entity representations that give rise to noisy similarity values, we incorporate novel notions of receptivity and reclusivity into a robust matching objective. As the optimal solution to …

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Hrgcn: Heterogeneous Graph-Level Anomaly Detection With Hierarchical Relation-Augmented Graph Neural Networks, Jiaxi Li, Guansong Pang, Ling Chen, Mohammad-Reza Namazi-Rad Oct 2023

Hrgcn: Heterogeneous Graph-Level Anomaly Detection With Hierarchical Relation-Augmented Graph Neural Networks, Jiaxi Li, Guansong Pang, Ling Chen, Mohammad-Reza Namazi-Rad

Research Collection School Of Computing and Information Systems

This work considers the problem of heterogeneous graph-level anomaly detection. Heterogeneous graphs are commonly used to represent behaviours between different types of entities in complex industrial systems for capturing as much information about the system operations as possible. Detecting anomalous heterogeneous graphs from a large set of system behaviour graphs is crucial for many real-world applications like online web/mobile service and cloud access control. To address the problem, we propose HRGCN, an unsupervised deep heterogeneous graph neural network, to model complex heterogeneous relations between different entities in the system for effectively identifying these anomalous behaviour graphs. HRGCN trains a hierarchical …

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Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw Sep 2023

Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users …

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Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu Aug 2023

Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

With the boom of cryptocurrency and its concomitant financial risk concerns, detecting fraudulent behaviors and associated malicious addresses has been drawing significant research effort. Most existing studies, however, rely on the full history features or full-fledged address transaction networks, both of which are unavailable in the problem of early malicious address detection and therefore failing them for the task. To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose …

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Interposition Based Container Optimization For Data Intensive Applications, Rohan Tikmany Jul 2023

Interposition Based Container Optimization For Data Intensive Applications, Rohan Tikmany

College of Computing and Digital Media Dissertations

Reproducibility of applications is paramount in several scenarios such as collaborative work and software testing. Containers provide an easy way of addressing reproducibility by packaging the application's software and data dependencies into one executable unit, which can be executed multiple times in different environments. With the increased use of containers in industry as well as academia, current research has examined the provisioning and storage cost of containers and has shown that container deployments often include unnecessary software packages. Current methods to optimize the container size prune unnecessary data at the granularity of files and thus make binary decisions. We show …

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A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng Jul 2023

A Data-Driven Approach For Scheduling Bus Services Subject To Demand Constraints, Brahmanage Janaka Chathuranga Thilakarathna, Thivya Kandappu, Baihua Zheng

Research Collection School Of Computing and Information Systems

Passenger satisfaction is extremely important for the success of a public transportation system. Many studies have shown that passenger satisfaction strongly depends on the time they have to wait at the bus stop (waiting time) to get on a bus. To be specific, user satisfaction drops faster as the waiting time increases. Therefore, service providers want to provide a bus to the waiting passengers within a threshold to keep them satisfied. It is a two-pronged problem: (a) to satisfy more passengers the transport planner may increase the frequency of the buses, and (b) in turn, the increased frequency may impact …

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Multi-View Hypergraph Contrastive Policy Learning For Conversational Recommendation, Sen Zhao, Wei Wei, Xian-Ling Mao, Shuai: Yang Zhu, Zujie Wen, Dangyang Chen, Feida Zhu, Feida Zhu Jul 2023

Multi-View Hypergraph Contrastive Policy Learning For Conversational Recommendation, Sen Zhao, Wei Wei, Xian-Ling Mao, Shuai: Yang Zhu, Zujie Wen, Dangyang Chen, Feida Zhu, Feida Zhu

Research Collection School Of Computing and Information Systems

Conversational recommendation systems (CRS) aim to interactively acquire user preferences and accordingly recommend items to users. Accurately learning the dynamic user preferences is of crucial importance for CRS. Previous works learn the user preferences with pairwise relations from the interactive conversation and item knowledge, while largely ignoring the fact that factors for a relationship in CRS are multiplex. Specifically, the user likes/dislikes the items that satisfy some attributes (Like/Dislike view). Moreover social influence is another important factor that affects user preference towards the item (Social view), while is largely ignored by previous works in CRS. The user preferences from these …

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Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu Jul 2023

Do-Good: Towards Distribution Shift Evaluation For Pre-Trained Visual Document Understanding Models, Jiabang He, Yi Hu, Lei Wang, Xing Xu, Ning Liu, Hui Liu

Research Collection School Of Computing and Information Systems

Numerous pre-training techniques for visual document understanding (VDU) have recently shown substantial improvements in performance across a wide range of document tasks. However, these pre-trained VDU models cannot guarantee continued success when the distribution of test data differs from the distribution of training data. In this paper, to investigate how robust existing pre-trained VDU models are to various distribution shifts, we first develop an out-of-distribution (OOD) benchmark termed Do-GOOD for the fine-Grained analysis on Document image-related tasks specifically. The Do-GOOD benchmark defines the underlying mechanisms that result in different distribution shifts and contains 9 OOD datasets covering 3 VDU related …

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Beyond Anthropomorphism: Unraveling The True Priorities Of Chatbot Usage In Smes, Tamas Makany, Sungjong Roh, Kotaro Hara, Jie Min Hua, Felicia Si Ying Goh, Wilson Yang Jie Teh Jul 2023

Beyond Anthropomorphism: Unraveling The True Priorities Of Chatbot Usage In Smes, Tamas Makany, Sungjong Roh, Kotaro Hara, Jie Min Hua, Felicia Si Ying Goh, Wilson Yang Jie Teh

Research Collection Lee Kong Chian School Of Business

This study examined business communication practices with chatbots among various Small and Medium Enterprise (SME) stakeholders in Singapore, including business owners/employees, customers, and developers. Through qualitative interviews and chatbot transcript analysis, we investigated two research questions: (1) How do the expectations of SME stakeholders compare to the conversational design of SME chatbots? and (2) What are the business reasons for SMEs to add human-like features to their chatbots? Our findings revealed that functionality is more crucial than anthropomorphic characteristics, such as personality and name. Stakeholders preferred chatbots that explicitly identified themselves as machines to set appropriate expectations. Customers prioritized efficiency, …

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Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao Jun 2023

Ldptrace: Locally Differentially Private Trajectory Synthesis, Yuntao Du, Yujia Hu, Zhikun Zhang, Ziquan Fang, Lu Chen, Baihua Zheng, Yunjun Gao

Research Collection School Of Computing and Information Systems

Trajectory data has the potential to greatly benefit a wide-range of real-world applications, such as tracking the spread of the disease through people's movement patterns and providing personalized location-based services based on travel preference. However, privacy concerns and data protection regulations have limited the extent to which this data is shared and utilized. To overcome this challenge, local differential privacy provides a solution by allowing people to share a perturbed version of their data, ensuring privacy as only the data owners have access to the original information. Despite its potential, existing point-based perturbation mechanisms are not suitable for real-world scenarios …

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Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim Jun 2023

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

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Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson May 2023

Fair Signposting Profile, Herbert Van De Sompel, Martin Klein, Shawn Jones, Michael L. Nelson, Simeon Warner, Anusuriya Devaraju, Robert Huber, Wilko Steinhoff, Vyacheslav Tykhonov, Luc Boruta, Enno Meijers, Stian Soiland-Reyes, Mark Wilkonson

Computer Science Faculty Publications

[First paragraph] This page details concrete recipes that platforms that host research outputs (e.g. data repositories, institutional repositories, publisher platforms, etc.) can follow to implement Signposting, a lightweight yet powerful approach to increase the FAIRness of scholarly objects.

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Bluetooth Low Energy Indoor Positioning System, Jackson T. Diamond, Jordan Hanson Dr May 2023

Bluetooth Low Energy Indoor Positioning System, Jackson T. Diamond, Jordan Hanson Dr

Whittier Scholars Program

Robust indoor positioning systems based on low energy bluetooth signals will service a wide range of applications. We present an example of a low energy bluetooth positioning system. First, the steps taken to locate the target with the bluetooth data will be reviewed. Next, we describe the algorithms of the set of android apps developed to utilize the bluetooth data for positioning. Similar to GPS, the algorithms use trilateration to approximate the target location by utilizing the corner devices running one of the apps. Due to the fluctuating nature of the bluetooth signal strength indicator (RSSI), we used an averaging …

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