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Articles 451 - 480 of 6717
Full-Text Articles in Physical Sciences and Mathematics
Generalization Bounds For Inductive Matrix Completion In Low-Noise Settings, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Yann Guermeur, Marius Kloft
Generalization Bounds For Inductive Matrix Completion In Low-Noise Settings, Antoine Ledent, Rodrigo Alves, Yunwen Lei, Yann Guermeur, Marius Kloft
Research Collection School Of Computing and Information Systems
We study inductive matrix completion (matrix completion with side information) under an i.i.d. subgaussian noise assumption at a low noise regime, with uniform sampling of the entries. We obtain for the first time generalization bounds with the following three properties: (1) they scale like the standard deviation of the noise and in particular approach zero in the exact recovery case; (2) even in the presence of noise, they converge to zero when the sample size approaches infinity; and (3) for a fixed dimension of the side information, they only have a logarithmic dependence on the size of the matrix. Differently …
Mirror: Mining Implicit Relationships Via Structure-Enhanced Graph Convolutional Networks, Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, Lianhua Chi
Mirror: Mining Implicit Relationships Via Structure-Enhanced Graph Convolutional Networks, Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, Lianhua Chi
Research Collection School Of Computing and Information Systems
Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a …
Mitigating Popularity Bias For Users And Items With Fairness-Centric Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu
Mitigating Popularity Bias For Users And Items With Fairness-Centric Adaptive Recommendation, Zhongzhou Liu, Yuan Fang, Min Wu
Research Collection School Of Computing and Information Systems
Recommendation systems are popular in many domains. Researchers usually focus on the effectiveness of recommendation (e.g., precision) but neglect the popularity bias that may affect the fairness of the recommendation, which is also an important consideration that could influence the benefits of users and item providers. A few studies have been proposed to deal with the popularity bias, but they often face two limitations. Firstly, most studies only consider fairness for one side - either users or items, without achieving fairness jointly for both. Secondly, existing methods are not sufficiently tailored to each individual user or item to cope with …
Flexible Job-Shop Scheduling Via Graph Neural Network And Deep Reinforcement Learning, Wen Song, Xinyang Chen, Qiqiang Li, Zhiguang Cao
Flexible Job-Shop Scheduling Via Graph Neural Network And Deep Reinforcement Learning, Wen Song, Xinyang Chen, Qiqiang Li, Zhiguang Cao
Research Collection School Of Computing and Information Systems
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching rules (PDRs) for solving complex scheduling problems. However, the existing works face challenges in dealing with flexibility, which allows an operation to be scheduled on one out of multiple machines and is often required in practice. Such one-to-many relationship brings additional complexity in both decision making and state representation. This article considers the well-known flexible job-shop scheduling problem and addresses these issues by proposing a novel DRL method to learn high-quality PDRs end to end. The operation selection and the machine assignment are combined as a composite decision. …
Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua
Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …
Contrastive Learning Approach To Word-In-Context Task For Low-Resource Languages, Pei-Chi Lo, Yang-Yin Lee, Hsien-Hao Chen, Agus Trisnajaya Kwee, Ee-Peng Lim
Contrastive Learning Approach To Word-In-Context Task For Low-Resource Languages, Pei-Chi Lo, Yang-Yin Lee, Hsien-Hao Chen, Agus Trisnajaya Kwee, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Word in context (WiC) task aims to determine whether a target word’s occurrences in two sentences share the same sense. In this paper, we propose a Contrastive Learning WiC (CLWiC) framework to improve the learning of sentence/word representations and classification of target word senses in the sentence pair when performing WiC on lowresource languages. In representation learning, CLWiC trains a pre-trained language model’s ability to cope with lowresource languages using both unsupervised and supervised contrastive learning. The WiC classifier learning further finetunes the language model with WiC classification loss under two classifier architecture options, SGBERT and WiSBERT, which use single-encoder …
Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar
Scalable And Globally Optimal Generalized L1 K-Center Clustering Via Constraint Generation In Mixed Integer Linear Programming, Aravinth Chembu, Scott Sanner, Hassan Khurram, Akshat Kumar
Research Collection School Of Computing and Information Systems
The k-center clustering algorithm, introduced over 35 years ago, is known to be robust to class imbalance prevalent in many clustering problems and has various applications such as data summarization, document clustering, and facility location determination. Unfortunately, existing k-center algorithms provide highly suboptimal solutions that can limit their practical application, reproducibility, and clustering quality. In this paper, we provide a novel scalable and globally optimal solution to a popular variant of the k-center problem known as generalized L1 k-center clustering that uses L1 distance and allows the selection of arbitrary vectors as cluster centers. We show that this clustering objective …
Future Aware Pricing And Matching For Sustainable On-Demand Ride Pooling, Xianjie Zhang, Pradeep Varakantham, Hao Jiang
Future Aware Pricing And Matching For Sustainable On-Demand Ride Pooling, Xianjie Zhang, Pradeep Varakantham, Hao Jiang
Research Collection School Of Computing and Information Systems
The popularity of on-demand ride pooling is owing to the benefits offered to customers (lower prices), taxi drivers (higher revenue), environment (lower carbon footprint due to fewer vehicles) and aggregation companies like Uber (higher revenue). To achieve these benefits, two key interlinked challenges have to be solved effectively: (a) pricing – setting prices to customer requests for taxis; and (b) matching – assignment of customers (that accepted the prices) to taxis/cars. Traditionally, both these challenges have been studied individually and using myopic approaches (considering only current requests), without considering the impact of current matching on addressing future requests. In this …
Cross-Domain Graph Anomaly Detection Via Anomaly-Aware Contrastive Alignment, Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie
Cross-Domain Graph Anomaly Detection Via Anomaly-Aware Contrastive Alignment, Qizhou Wang, Guansong Pang, Mahsa Salehi, Wray Buntine, Christopher Leckie
Research Collection School Of Computing and Information Systems
Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, …
On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang
On Generalized Degree Fairness In Graph Neural Networks, Zemin Liu, Trung Kien Nguyen, Yuan Fang
Research Collection School Of Computing and Information Systems
Conventional graph neural networks (GNNs) are often confronted with fairness issues that may stem from their input, including node attributes and neighbors surrounding a node. While several recent approaches have been proposed to eliminate the bias rooted in sensitive attributes, they ignore the other key input of GNNs, namely the neighbors of a node, which can introduce bias since GNNs hinge on neighborhood structures to generate node representations. In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes. In this paper, we first define …
Alignment-Enriched Tuning For Patch-Level Pre-Trained Document Image Models, Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu
Alignment-Enriched Tuning For Patch-Level Pre-Trained Document Image Models, Lei Wang, Jiabang He, Xing Xu, Ning Liu, Hui Liu
Research Collection School Of Computing and Information Systems
Alignment between image and text has shown promising im provements on patch-level pre-trained document image mod els. However, investigating more effective or finer-grained alignment techniques during pre-training requires a large amount of computation cost and time. Thus, a question natu rally arises: Could we fine-tune the pre-trained models adap tive to downstream tasks with alignment objectives and achieve comparable or better performance? In this paper, we pro pose a new model architecture with alignment-enriched tuning (dubbed AETNet) upon pre-trained document image models, to adapt downstream tasks with the joint task-specific super vised and alignment-aware contrastive objective. Specifically, weintroduce an extra …
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden
Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden
National Training Aircraft Symposium (NTAS)
An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …
Data Poisoning: A New Threat To Artificial Intelligence, Nary Simms
Data Poisoning: A New Threat To Artificial Intelligence, Nary Simms
Mathematics and Computer Science Capstones
Artificial Intelligence (AI) adoption is rapidly being deployed in a number of fields, from banking and finance to healthcare, robotics, transportation, military, e-commerce and social networks. Grand View Research estimates that the global AI market was worth 93.5 billion in 2021 and that it will increase at a compound annual growth rate (CAGR) of 38.1% from 2022 to 2030. According to a 2020 MIT Sloan Management survey, 87% of multinational corporations believe that AI technology will provide a competitive edge. Artificial Intelligence relies heavily on datasets to train its models. The more data, the better it learns and predicts. However, …
Diversification Strategies Business Managers Use To Improve Profitability, Kayode Itiola
Diversification Strategies Business Managers Use To Improve Profitability, Kayode Itiola
Walden Dissertations and Doctoral Studies
A lack of diversification strategies can negatively impact business profitability. Business managers of small and medium-sized enterprises who implement appropriate diversification strategies can improve business profitability, ensuring the firm’s sustainability. Grounded in the modern portfolio theory, the purpose of this qualitative single case study was to explore diversification strategies business managers use to improve profitability. The participants were five business managers who successfully used diversification strategies to enhance business profitability. Sources for data collection were semi-structured interviews, company archival documents, and field notes. Data were analyzed using thematic analysis. Four themes emerged: concentric diversification strategy, horizontal diversification strategy, customer needs …
Go Green With Ecosia: The Search Engine With A Sustainable Business Model, James Thibeault
Go Green With Ecosia: The Search Engine With A Sustainable Business Model, James Thibeault
Library Publications
Ecosia, a non-profit search engine, is not only a thriving business but is also directly responsible for planting over 185 million trees. Focusing on sustainability and social responsibility, Ecosia demonstrates how business models can positively impact the environment.
Leadership Strategies Supply Chain Managers Use In Adopting Innovative Technology, Bukola Loveth Olowo
Leadership Strategies Supply Chain Managers Use In Adopting Innovative Technology, Bukola Loveth Olowo
Walden Dissertations and Doctoral Studies
Supply chain managers face challenges when adopting new technologies to remain competitive and satisfy consumer demands involving expedited delivery of food and services. Supply chain managers who fail to adopt new technology have a decreased propensity to stay competitive. Grounded in the transformation leadership theory, the purpose of this qualitative multiple-case study was to explore leadership strategies supply chain managers use in adopting innovative technology. Participants were six supply chain managers who successfully used leadership strategies to adopt new innovative technology. Sources for data collection were semistructured interviews, company archival documents, and field notes. Research data were analyzed via thematic …
Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei
Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei
Walden Dissertations and Doctoral Studies
Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …
Understanding U.S. Customers' Intention To Adopt Robo-Advisor Technology, Deborah Wall
Understanding U.S. Customers' Intention To Adopt Robo-Advisor Technology, Deborah Wall
Walden Dissertations and Doctoral Studies
Finance and information technology scholars wrote that there is a literature gap on what factors drive investors in Western financial markets to use a Robo-advisor to manage their investments. The purpose of this qualitative, single case study with embedded units is to understand the adoption intentions of retail investors in U.S. markets to use a Robo-advisor instead of a human advisor. A single case study design addressed the literature gap, and qualitative data from seven semi=structured interviews, reflective field notes, and archival data were triangulated to answer the research question. This study was grounded in a theoretical framework that includes …
How It Professionals Acquire Soft Skills, Paul Majett
How It Professionals Acquire Soft Skills, Paul Majett
Walden Dissertations and Doctoral Studies
AbstractIn this study, I investigated how information technology (IT) professionals learn/acquire soft skills. Little is understood about how IT professionals describe and apply the soft skills that they consider necessary for their own employability. Exploring how IT professionals describe and apply soft skills is important to their future work and career advancement. The purpose of this basic qualitative study was to better understand how IT professionals learn/acquire soft skills. Bandura’s 1986, social learning theory served as the organizational conceptual framework of this study and guided the research question, which asked how IT professionals acquire learn/acquire soft skills. This research question …
Generational Information Security Awareness And The Role Of Big Five Personality Traits, Gloria Mccue
Generational Information Security Awareness And The Role Of Big Five Personality Traits, Gloria Mccue
Walden Dissertations and Doctoral Studies
AbstractTechnological change drives organizations to safeguard information systems. However, such safeguards are dependent upon people to follow security rules. This study examined generational cohorts and personality traits and their impact on information security awareness. Participants in this study were 137 volunteers who completed an anonymous survey online. Two tools were utilized to collect data from the participants: the Human Aspects of Information Security Questionnaire and the Big Five Inventory, which captured behaviors and personality traits, respectively. The three main generational cohorts represented in the study, Baby Boomers, Generation X, and Generation Y, were in today’s workforce. The results of the …
Strategies To Increase Competitive Advantage In The Automotive Manufacturing Supply Chain, Amber Willis
Strategies To Increase Competitive Advantage In The Automotive Manufacturing Supply Chain, Amber Willis
Walden Dissertations and Doctoral Studies
Some automotive manufacturing supply chain leaders lack strategies that are needed to implement information technology (IT) systems. Business leaders are concerned with implementing IT systems to achieve and maintain a competitive advantage. Grounded in the resource-based view theory (RBV), the purpose of this qualitative single case study was to explore information system strategies used by leaders in the automotive manufacturing supply chain to achieve competitive advantage. Participants were five leaders of an automotive manufacturing supply chain organization who implemented IT systems. Data were collected through semistructured interviews and a review of organization project documents. Through thematic analysis, five themes were …
The Relationship Between Organizational Knowledge Management Constructs And Organizational Flexibility, Marcus B. Williams
The Relationship Between Organizational Knowledge Management Constructs And Organizational Flexibility, Marcus B. Williams
Walden Dissertations and Doctoral Studies
The role that the information technology (IT) department serves is governed by the corporate culture and how it values the use of knowledge, including IT, to achieve a strategic competitive advantage. The purpose of this quantitative study was to examine the potential relationships between information acquisition, knowledge dissemination, shared interpretation, organizational memory, and organizational flexibility. Two theories served as the theoretical foundation for this study: contingency theory and the resource-based view of the firm. To answer the question of possible correlation between organizational flexibility and components of knowledge management, a randomly selected sample of 193 IT professionals employed at small- …
Ai Usage In Development, Security, And Operations, Maurice Ayidiya
Ai Usage In Development, Security, And Operations, Maurice Ayidiya
Walden Dissertations and Doctoral Studies
Artificial intelligence (AI) has become a growing field in information technology (IT). Cybersecurity managers are concerned that the lack of strategies to incorporate AI technologies in developing secure software for IT operations may inhibit the effectiveness of security risk mitigation. Grounded in the technology acceptance model, the purpose of this qualitative exploratory multiple case study was to explore strategies cybersecurity professionals use to incorporate AI technologies in developing secure software for IT operations. The participants were 10 IT professionals in the United States with at least 5 years of professional experience working in DevSecOps and managing teams of at least …
Knowledge-Sharing Practice As A Tool In Organizational Development In Nigerian Higher Educational Institutions, Emmanuel Ajiri Ojo
Knowledge-Sharing Practice As A Tool In Organizational Development In Nigerian Higher Educational Institutions, Emmanuel Ajiri Ojo
Walden Dissertations and Doctoral Studies
AbstractUnderstanding knowledge management is key to understanding organizational development and innovations. Inadequate knowledge-sharing practices in Nigerian educational institutions has impeded innovation and management development. The purpose of this qualitative modified Delphi study was to seek consensus among administrators from Nigerian educational institutions and scholars from Nigerian universities regarding knowledge-sharing practices that nourish innovation in Nigerian higher educational institutions. The organizational development framework was used to guide the study. Data collection included a nonprobability purposive sampling of 25 participants and three rounds of surveys administered online. A consensus was reached on eight factors after coding and thematic analysis: setting knowledge-sharing expectations, …
Developing Consensus On The Use Of Emotional Intelligence Training In Small Utility Companies, Nathaniel E. Holloway
Developing Consensus On The Use Of Emotional Intelligence Training In Small Utility Companies, Nathaniel E. Holloway
Walden Dissertations and Doctoral Studies
Park and Shaw shared the impact on organizations from unmotivated and unsatisfied employees link to higher turnover ratios. The use of emotional intelligence in manager training lowered employee turnover by 13%. The problem address in this Delphi study was that small utility companies do not have an emotional intelligence plan in place for managerial training. Goleman and Mayer’s framework was used as the theorical lens for examining response of participants to the Delphi study. A panel of experts submitted data in the form of responses to three rounds of questions regarding the use of emotional intelligence training in small businesses. …
Technical Training To Nonprofit Managers Influences Using Big Data Technology In Business Operations, Dr. Arslan Isaac Phd
Technical Training To Nonprofit Managers Influences Using Big Data Technology In Business Operations, Dr. Arslan Isaac Phd
Walden Dissertations and Doctoral Studies
This nonexperimental, survey-based online quantitative study on nonprofit managers’ technical training measures the extent of the influence on big data technology use. The unified theory of acceptance and use of technology is a theoretical framework to determine whether business managers are trained to have know-how in using big data technology. This study followed a quantitative methodology to help narrow the gap in research between what is not known in relation to the nonprofit manager’s technical training on the use of big data technology. Today’s data is the most critical asset, but progress toward big data technology-oriented usage needs to be …
Querying The Past: Automatic Source Attribution With Language Models, Ryan Muther, Mathew Barber, David Smith
Querying The Past: Automatic Source Attribution With Language Models, Ryan Muther, Mathew Barber, David Smith
Faculty & Staff Publications
This paper explores new methods for locating the sources used to write a text by 昀椀ne-tuning a variety of language models to rerank candidate sources. These methods promise to shed new light on traditions with complex citational practices, such as in medieval Arabic where citations are ambiguous and boundaries of quotation are poorly defined. After retrieving candidates sources using a baseline BM25 retrieval model, a variety of reranking methods are tested to see how effective they are at the task of source attribution. We conduct experiments on two datasets—English Wikipedia and medieval Arabic historical writing—and employ a variety of retrieval- …
Ai Usage In Development, Security, And Operations, Maurice Ayidiya
Ai Usage In Development, Security, And Operations, Maurice Ayidiya
Walden Dissertations and Doctoral Studies
Artificial intelligence (AI) has become a growing field in information technology (IT). Cybersecurity managers are concerned that the lack of strategies to incorporate AI technologies in developing secure software for IT operations may inhibit the effectiveness of security risk mitigation. Grounded in the technology acceptance model, the purpose of this qualitative exploratory multiple case study was to explore strategies cybersecurity professionals use to incorporate AI technologies in developing secure software for IT operations. The participants were 10 IT professionals in the United States with at least 5 years of professional experience working in DevSecOps and managing teams of at least …
Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei
Relationship Between Strategic Dexterity, Absorptive Capacity, And Competitive Advantage, Ifechide Monyei
Walden Dissertations and Doctoral Studies
Small- and medium-sized enterprise (SME) manufacturing executives and managers are concerned with the rapid technological changes involving artificial intelligence (AI), machine learning, and big data. To compete in the global landscape, effectively managing digital and artificial intelligence changes among SME manufacturing executives and managers is critical for leaders to compete in 2023 and beyond. Grounded in the dynamic capabilities view theory, the purpose of this quantitative correlation study was to examine the relationship between strategic dexterity, absorptive capacity, and competitive advantage. The participants were 66 executives and managers of SME manufacturing organizations who use big data and analytics daily and …
Strategies Information Technology Managers Use To Retain Qualified Information Technology Employees, Wayne Arnold Reu
Strategies Information Technology Managers Use To Retain Qualified Information Technology Employees, Wayne Arnold Reu
Walden Dissertations and Doctoral Studies
Retaining qualified information technology (IT) personnel can take time and effort, given the high demand for skilled positions. Business leaders are concerned with the high turnover of IT employees because of the cost of recruiting and training personnel and the disruption to organizational processes and performance. Grounded in job characteristics theory, the purpose of this qualitative pragmatic inquiry was to explore IT managers' strategies to retain qualified IT employees in organizations across the southwestern United States. Eight IT leaders participated because of their years of experience implementing strategies to retain qualified IT professionals. Data were collected using semistructured interviews and …