Open Access. Powered by Scholars. Published by Universities.®
Databases and Information Systems Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Social and Behavioral Sciences (1152)
- Communication (869)
- OS and Networks (780)
- Numerical Analysis and Scientific Computing (683)
- Life Sciences (680)
-
- Communication Technology and New Media (674)
- Science and Technology Studies (647)
- Bioinformatics (644)
- Graphics and Human Computer Interfaces (292)
- Business (280)
- Engineering (267)
- Software Engineering (263)
- Artificial Intelligence and Robotics (252)
- Theory and Algorithms (248)
- Computer Engineering (212)
- Social Media (182)
- Information Security (177)
- Medicine and Health Sciences (111)
- Other Computer Sciences (110)
- Programming Languages and Compilers (109)
- Management Information Systems (93)
- Public Affairs, Public Policy and Public Administration (90)
- Library and Information Science (88)
- Education (87)
- Data Storage Systems (81)
- Systems Architecture (71)
- Geography (64)
- Institution
-
- Singapore Management University (2763)
- Wright State University (632)
- University of Nebraska at Omaha (106)
- University of Dayton (69)
- Old Dominion University (64)
-
- University of Nebraska - Lincoln (50)
- Technological University Dublin (49)
- City University of New York (CUNY) (45)
- Claremont Colleges (38)
- Western Kentucky University (36)
- Ateneo de Manila University (34)
- Cleveland State University (29)
- Florida International University (23)
- Portland State University (22)
- Kennesaw State University (21)
- University of Pittsburgh School of Law (19)
- Chapman University (16)
- Edith Cowan University (15)
- Western University (13)
- The University of San Francisco (12)
- University of Nevada, Las Vegas (11)
- The University of Maine (10)
- Department of Primary Industries and Regional Development, Western Australia (8)
- Maurer School of Law: Indiana University (7)
- Bryant University (6)
- Dakota State University (6)
- Montclair State University (6)
- San Jose State University (6)
- University of Massachusetts Amherst (6)
- Wayne State University (6)
- Keyword
-
- Data mining (60)
- Machine learning (59)
- Twitter (50)
- Semantic Web (49)
- Social media (49)
-
- Online learning (45)
- Artificial intelligence (39)
- Deep learning (36)
- Classification (34)
- Ontology (30)
- Algorithms (29)
- Information technology (29)
- Privacy (28)
- Query processing (28)
- Northern Ohio Data and Information Service (NODIS) (27)
- Semantics (27)
- Big data (26)
- Clustering (25)
- Algorithm (23)
- Cloud computing (23)
- Deep Learning (23)
- Neural networks (23)
- Information retrieval (22)
- Natural language processing (22)
- Reinforcement learning (22)
- Semantic Sensor Web (22)
- Social Media (22)
- Machine Learning (21)
- Visualization (21)
- Data Mining (20)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (2727)
- Kno.e.sis Publications (541)
- Computer Science and Engineering Faculty Publications (91)
- Computer Science Faculty Publications (90)
- Information Systems and Quantitative Analysis Faculty Proceedings & Presentations (64)
-
- Information Systems and Quantitative Analysis Faculty Publications (41)
- CGU Faculty Publications and Research (37)
- Department of Information Systems & Computer Science Faculty Publications (33)
- Open Educational Resources (33)
- Articles (28)
- Conference papers (28)
- All Maxine Goodman Levin School of Urban Affairs Publications (27)
- Faculty and Research Publications (21)
- MIS/OM/DS Faculty Publications (18)
- Masters Theses & Specialist Projects (17)
- FIU Electronic Theses and Dissertations (16)
- Computer Science Faculty Publications and Presentations (13)
- Electrical and Computer Engineering Publications (13)
- Publications and Research (12)
- Faculty Publications (9)
- Research Collection Lee Kong Chian School Of Business (9)
- WKU Archives Records (9)
- Asian Management Insights (8)
- Business Analytics and Information Systems (8)
- Engineering Management & Systems Engineering Faculty Publications (7)
- Information Technology & Decision Sciences Faculty Publications (7)
- All Faculty Scholarship (6)
- Book Chapters (6)
- Copyright, Fair Use, Scholarly Communication, etc. (6)
- Department of Computer Science and Engineering: Dissertations, Theses, and Student Research (6)
- File Type
Articles 1 - 30 of 4289
Full-Text Articles in Databases and Information Systems
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Unveiling The Dynamics Of Crisis Events: Sentiment And Emotion Analysis Via Multi-Task Learning With Attention Mechanism And Subject-Based Intent Prediction, Phyo Yi Win Myint, Siaw Ling Lo, Yuhao Zhang
Research Collection School Of Computing and Information Systems
In the age of rapid internet expansion, social media platforms like Twitter have become crucial for sharing information, expressing emotions, and revealing intentions during crisis situations. They offer crisis responders a means to assess public sentiment, attitudes, intentions, and emotional shifts by monitoring crisis-related tweets. To enhance sentiment and emotion classification, we adopt a transformer-based multi-task learning (MTL) approach with attention mechanism, enabling simultaneous handling of both tasks, and capitalizing on task interdependencies. Incorporating attention mechanism allows the model to concentrate on important words that strongly convey sentiment and emotion. We compare three baseline models, and our findings show that …
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Diffusion-Based Negative Sampling On Graphs For Link Prediction, Yuan Fang, Yuan Fang
Research Collection School Of Computing and Information Systems
Link prediction is a fundamental task for graph analysis with important applications on the Web, such as social network analysis and recommendation systems, etc. Modern graph link prediction methods often employ a contrastive approach to learn robust node representations, where negative sampling is pivotal. Typical negative sampling methods aim to retrieve hard examples based on either predefined heuristics or automatic adversarial approaches, which might be inflexible or difficult to control. Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space. …
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao
On The Feasibility Of Simple Transformer For Dynamic Graph Modeling, Yuxia Wu, Yuan Fang, Lizi Liao
Research Collection School Of Computing and Information Systems
Dynamic graph modeling is crucial for understanding complex structures in web graphs, spanning applications in social networks, recommender systems, and more. Most existing methods primarily emphasize structural dependencies and their temporal changes. However, these approaches often overlook detailed temporal aspects or struggle with long-term dependencies. Furthermore, many solutions overly complicate the process by emphasizing intricate module designs to capture dynamic evolutions. In this work, we harness the strength of the Transformer’s self-attention mechanism, known for adeptly handling long-range dependencies in sequence modeling. Our approach offers a simple Transformer model, called SimpleDyG, tailored for dynamic graph modeling without complex modifications. We …
Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan
Multigprompt For Multi-Task Pre-Training And Prompting On Graphs, Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhan
Research Collection School Of Computing and Information Systems
Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availability of task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the …
Immersive Japanese Language Learning Web Application Using Spaced Repetition, Active Recall, And An Artificial Intelligent Conversational Chat Agent Both In Voice And In Text, Marc Butler
MS in Computer Science Project Reports
In the last two decades various human language learning applications, spaced repetition software, online dictionaries, and artificial intelligent chat agents have been developed. However, there is no solution to cohesively combine these technologies into a comprehensive language learning application including skills such as speaking, typing, listening, and reading. Our contribution is to provide an immersive language learning web application to the end user which combines spaced repetition, a study technique used to review information at systematic intervals, and active recall, the process of purposely retrieving information from memory during a review session, with an artificial intelligent conversational chat agent both …
Artificial Intelligence Could Probably Write This Essay Better Than Me, Claire Martino
Artificial Intelligence Could Probably Write This Essay Better Than Me, Claire Martino
Augustana Center for the Study of Ethics Essay Contest
No abstract provided.
Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan
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 …
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
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 …
Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao
Screening Through A Broad Pool: Towards Better Diversity For Lexically Constrained Text Generation, Changsen Yuan, Heyan Huang, Yixin Cao, Qianwen Cao
Research Collection School Of Computing and Information Systems
Lexically constrained text generation (CTG) is to generate text that contains given constrained keywords. However, the text diversity of existing models is still unsatisfactory. In this paper, we propose a lightweight dynamic refinement strategy that aims at increasing the randomness of inference to improve generation richness and diversity while maintaining a high level of fluidity and integrity. Our basic idea is to enlarge the number and length of candidate sentences in each iteration, and choose the best for subsequent refinement. On the one hand, different from previous works, which carefully insert one token between two words per action, we insert …
Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose
Hypergraphs With Attention On Reviews For Explainable Recommendation, Theis E. Jendal, Trung Hoang Le, Hady Wirawan Lauw, Matteo Lissandrini, Peter Dolog, Katja Hose
Research Collection School Of Computing and Information Systems
Given a recommender system based on reviews, the challenges are how to effectively represent the review data and how to explain the produced recommendations. We propose a novel review-specific Hypergraph (HG) model, and further introduce a model-agnostic explainability module. The HG model captures high-order connections between users, items, aspects, and opinions while maintaining information about the review. The explainability module can use the HG model to explain a prediction generated by any model. We propose a path-restricted review-selection method biased by the user preference for item reviews and propose a novel explanation method based on a review graph. Experiments on …
Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw
Community Similarity Based On User Profile Joins, Konstantinos Theocharidis, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Similarity joins on multidimensional data are crucial operators for recommendation purposes. The classic ��-join problem finds all pairs of points within �� distance to each other among two ��-dimensional datasets. In this paper, we consider a novel and alternative version of ��-join named community similarity based on user profile joins (CSJ). The aim of CSJ problem is, given two communities having a set of ��-dimensional users, to find how similar are the communities by matching every single pair of users (a user can be matched with at most one other user) having an absolute difference of at most �� per …
On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman
On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman
Research Collection School Of Computing and Information Systems
We report on two studies that examine how social sentiment influences information asymmetry in digital currency markets. We also assess whether cryptocurrency can be an investment vehicle, as opposed to only an instrument for asset speculation. Using a dataset on transactions from an exchange in South Korea and sentiment from Korean social media in 2018, we conducted a study of different trading behavior under two cryptocurrency trading market microstructures: a bid-ask spread dealer's market and a continuous trading buy-sell, immediate trade execution market. Our results highlight the impacts of positive and negative trader social sentiment valences on the effects of …
Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. Yu, Nabila Y. Salsabila, Shih-W Lin, Aldy Gunawan
Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. Yu, Nabila Y. Salsabila, Shih-W Lin, Aldy Gunawan
Research Collection School Of Computing and Information Systems
This research investigates the Set Team Orienteering Problem with Time Windows (STOPTW), a new variant of the well-known Team Orienteering Problem with Time Windows and Set Orienteering Problem. In the STOPTW, customers are grouped into clusters. Each cluster is associated with a profit attainable when a customer in the cluster is visited within the customer's time window. A Mixed Integer Linear Programming model is formulated for STOPTW to maximizing total profit while adhering to time window constraints. Since STOPTW is an NP-hard problem, a Simulated Annealing with Reinforcement Learning (SARL) algorithm is developed. The proposed SARL incorporates the core concepts …
Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Mai Anh Tien, Pradeep Varakantham
Imitate The Good And Avoid The Bad: An Incremental Approach To Safe Reinforcement Learning, Minh Huy Hoang, Mai Anh Tien, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
A popular framework for enforcing safe actions in Reinforcement Learning (RL) is Constrained RL, where trajectory based constraints on expected cost (or other cost measures) are employed to enforce safety and more importantly these constraints are enforced while maximizing expected reward. Most recent approaches for solving Constrained RL convert the trajectory based cost constraint into a surrogate problem that can be solved using minor modifications to RL methods. A key drawback with such approaches is an over or underestimation of the cost constraint at each state. Therefore, we provide an approach that does not modify the trajectory based cost constraint …
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft
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 …
When Evolutionary Computation Meets Privacy, Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Jun Zhang
When Evolutionary Computation Meets Privacy, Bowen Zhao, Wei-Neng Chen, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Jun Zhang
Research Collection School Of Computing and Information Systems
Recently, evolutionary computation (EC) has experienced significant advancements due to the integration of machine learning, distributed computing, and big data technologies. These developments have led to new research avenues in EC, such as distributed EC and surrogate-assisted EC. While these advancements have greatly enhanced the performance and applicability of EC, they have also raised concerns regarding privacy leakages, specifically the disclosure of optimal results and surrogate models. Consequently, the combination of evolutionary computation and privacy protection becomes an increasing necessity. However, a comprehensive exploration of privacy concerns in evolutionary computation is currently lacking, particularly in terms of identifying the object, …
Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Handling Long And Richly Constrained Tasks Through Constrained Hierarchical Reinforcement Learning, Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which …
Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan
Foodmask: Real-Time Food Instance Counting, Segmentation And Recognition, Huu-Thanh Nguyen, Yu Cao, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Food computing has long been studied and deployed to several applications. Understanding a food image at the instance level, including recognition, counting and segmentation, is essential to quantifying nutrition and calorie consumption. Nevertheless, existing techniques are limited to either category-specific instance detection, which does not reflect precisely the instance size at the pixel level, or category-agnostic instance segmentation, which is insufficient for dish recognition. This paper presents a compact and fast multi-task network, namely FoodMask, for clustering-based food instance counting, segmentation and recognition. The network learns a semantic space simultaneously encoding food category distribution and instance height at pixel basis. …
Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam
Public Acceptance Of Using Artificial Intelligence-Assisted Weight Management Apps In High-Income Southeast Asian Adults With Overweight And Obesity: A Cross-Sectional Study, Han Shi Jocelyn Chew, Palakorn Achananuparp, Palakorn Achananuparp, Nicholas W. S. Chew, Yip Han Chin, Yujia Gao, Bok Yan Jimmy So, Asim Shabbir, Ee-Peng Lim, Kee Yuan Ngiam
Research Collection School Of Computing and Information Systems
Introduction: With in increase in interest to incorporate artificial intelligence (AI) into weight management programs, we aimed to examine user perceptions of AI-based mobile apps for weight management in adults with overweight and obesity. Methods: 280 participants were recruited between May and November 2022. Participants completed a questionnaire on sociodemographic profiles, Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), and Self-Regulation of Eating Behavior Questionnaire. Structural equation modeling was performed using R. Model fit was tested using maximum-likelihood generalized unweighted least squares. Associations between influencing factors were analyzed using correlation and linear regression. Results: 271 participant responses were …
Frameworks For Measuring Population Health: A Scoping Review, Sze Ling Chan, Clement Zhong Hao Ho, Nang Ei Ei Khaing, Ezra Ho, Candelyn Pong, Jia Sheng Guan, Calida Chua, Zongbin Li, Trudi Lim Wenqi, Sean Shao Wei Lam, Lian Leng Low, Choon How How
Frameworks For Measuring Population Health: A Scoping Review, Sze Ling Chan, Clement Zhong Hao Ho, Nang Ei Ei Khaing, Ezra Ho, Candelyn Pong, Jia Sheng Guan, Calida Chua, Zongbin Li, Trudi Lim Wenqi, Sean Shao Wei Lam, Lian Leng Low, Choon How How
Research Collection School Of Computing and Information Systems
Introduction Many regions in the world are using the population health approach and require a means to measure the health of their population of interest. Population health frameworks provide a theoretical grounding for conceptualization of population health and therefore a logical basis for selection of indicators. The aim of this scoping review was to provide an overview and summary of the characteristics of existing population health frameworks that have been used to conceptualize the measurement of population health. Methods We used the Population, Concept and Context (PCC) framework to define eligibility criteria of frameworks. We were interested in frameworks applicable …
Reverse Multi-Choice Dialogue Commonsense Inference With Graph-Of-Thought, Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng
Reverse Multi-Choice Dialogue Commonsense Inference With Graph-Of-Thought, Li Zheng, Hao Fei, Fei Li, Bobo Li, Lizi Liao, Donghong Ji, Chong Teng
Research Collection School Of Computing and Information Systems
With the proliferation of dialogic data across the Internet, the Dialogue Commonsense Multi-choice Question Answering (DC-MCQ) task has emerged as a response to the challenge of comprehending user queries and intentions. Although prevailing methodologies exhibit effectiveness in addressing single-choice questions, they encounter difficulties in handling multi-choice queries due to the heightened intricacy and informational density. In this paper, inspired by the human cognitive process of progressively excluding options, we propose a three-step Reverse Exclusion Graph-of-Thought (ReX-GoT) framework, including Option Exclusion, Error Analysis, and Combine Information. Specifically, our ReX-GoT mimics human reasoning by gradually excluding irrelevant options and learning the reasons …
Hgprompt: Bridging Homogeneous And Heterogeneous Graphs For Few-Shot Prompt Learning, Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang
Hgprompt: Bridging Homogeneous And Heterogeneous Graphs For Few-Shot Prompt Learning, Xingtong Yu, Yuan Fang, Zemin Liu, Xinming Zhang
Research Collection School Of Computing and Information Systems
Graph neural networks (GNNs) and heterogeneous graph neural networks (HGNNs) are prominent techniques for homogeneous and heterogeneous graph representation learning, yet their performance in an end-to-end supervised framework greatly depends on the availability of task-specific supervision. To reduce the labeling cost, pre-training on selfsupervised pretext tasks has become a popular paradigm, but there is often a gap between the pre-trained model and downstream tasks, stemming from the divergence in their objectives. To bridge the gap, prompt learning has risen as a promising direction especially in few-shot settings, without the need to fully fine-tune the pre-trained model. While there has been …
Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang
Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang
Research Collection School Of Computing and Information Systems
Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used …
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Research Collection School Of Computing and Information Systems
In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …
Unmasking Shadows: Unraveling Crime Patterns In Nyc's Boroughs, Jack Hachicho, Muhammad Hassan Butt
Unmasking Shadows: Unraveling Crime Patterns In Nyc's Boroughs, Jack Hachicho, Muhammad Hassan Butt
Publications and Research
New York City's crime dynamics have been on the rise for decades. Brooklyn and The Bronx have been disproportionately affected. This research aims to understand the crime landscape in these boroughs to formulate effective policies. Using crime data from official sources, statistical analyses, and data visualizations, the study identifies patterns and trends. The data encompasses over 400,000 reported incidents collected over the past 10 years, meticulously categorized by borough, crime type, and demographic information. Brooklyn has the highest overall crime rate, followed by The Bronx. Most shooting victims are Black. This highlights the need for holistic community programs to address …
Flowpg: Action-Constrained Policy Gradient With Normalizing Flows, Brahmanage Janaka Chathuranga Thilakarathna, Jiajing Ling, Akshat Kumar
Flowpg: Action-Constrained Policy Gradient With Normalizing Flows, Brahmanage Janaka Chathuranga Thilakarathna, Jiajing Ling, Akshat Kumar
Research Collection School Of Computing and Information Systems
Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-critical and resource-allocation related decision making problems. A major challenge in ACRL is to ensure agent taking a valid action satisfying constraints in each RL step. Commonly used approach of using a projection layer on top of the policy network requires solving an optimization program which can result in longer training time, slow convergence, and zero gradient problem. To address this, first we use a normalizing flow model to learn an invertible, differentiable mapping between the feasible action space and the support of a simple distribution on a latent variable, …
C³: Code Clone-Based Identification Of Duplicated Components, Yanming Yang, Ying Zou, Xing Hu, David Lo, Chao Ni, John C. Grundy, Xin: Xia
C³: Code Clone-Based Identification Of Duplicated Components, Yanming Yang, Ying Zou, Xing Hu, David Lo, Chao Ni, John C. Grundy, Xin: Xia
Research Collection School Of Computing and Information Systems
Reinventing the wheel is a detrimental programming practice in software development that frequently results in the introduction of duplicated components. This practice not only leads to increased maintenance and labor costs but also poses a higher risk of propagating bugs throughout the system. Despite numerous issues introduced by duplicated components in software, the identification of component-level clones remains a significant challenge that existing studies struggle to effectively tackle. Specifically, existing methods face two primary limitations that are challenging to overcome: 1) Measuring the similarity between different components presents a challenge due to the significant size differences among them; 2) Identifying …
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
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 …
Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh
Extending The Horizon By Empowering Government Customer Service Officers With Acqar For Enhanced Citizen Service Delivery, Hui Shan Lee, Shankararaman, Venky, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
A previous study on the use of the Empath library in the prediction of Service Level Agreements (SLA) reveals the quality levels required for meaningful interaction between government customer service officers and citizens. On the other hand, past implementation of the Citizen Question-Answer system (CQAS), a type of Question-Answer model, suggests that such models if put in place can empower government customer service officers to reply faster and better with recommended answers. This study builds upon the research outcomes from both arenas of studies and introduces an innovative system design that allows the officers to incorporate the outputs from Empath …
Generative Modelling Of Stochastic Actions With Arbitrary Constraints In Reinforcement Learning, Changyu Chen, Ramesha Karunasena, Thanh Hong Nguyen, Arunesh Sinha, Pradeep Varakantham
Generative Modelling Of Stochastic Actions With Arbitrary Constraints In Reinforcement Learning, Changyu Chen, Ramesha Karunasena, Thanh Hong Nguyen, Arunesh Sinha, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Many problems in Reinforcement Learning (RL) seek an optimal policy with large discrete multidimensional yet unordered action spaces; these include problems in randomized allocation of resources such as placements of multiple security resources and emergency response units, etc. A challenge in this setting is that the underlying action space is categorical (discrete and unordered) and large, for which existing RL methods do not perform well. Moreover, these problems require validity of the realized action (allocation); this validity constraint is often difficult to express compactly in a closed mathematical form. The allocation nature of the problem also prefers stochastic optimal policies, …