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Articles 1 - 17 of 17
Full-Text Articles in Data Science
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Towards Robust Long-Form Text Generation Systems, Kalpesh Krishna
Doctoral Dissertations
Text generation is an important emerging AI technology that has seen significant research advances in recent years. Due to its closeness to how humans communicate, mastering text generation technology can unlock several important applications such as intelligent chat-bots, creative writing assistance, or newer applications like task-agnostic few-shot learning. Most recently, the rapid scaling of large language models (LLMs) has resulted in systems like ChatGPT, capable of generating fluent, coherent and human-like text. However, despite their remarkable capabilities, LLMs still suffer from several limitations, particularly when generating long-form text. In particular, (1) long-form generated text is filled with factual inconsistencies to …
Nviz: Unraveling Neural Networks Through Visualization, Kevin Hoffman
Nviz: Unraveling Neural Networks Through Visualization, Kevin Hoffman
Mathematics and Computer Science Presentations
The growing utility of artificial intelligence (AI) is attributed to the development of neural networks. These networks are a class of models that make predictions based on previously observed data. While the inferential power of neural networks is great, the ability to explain their results is difficult because the underlying model is automatically generated. The AI community commonly refers to neural networks as black boxes because the patterns they learn from the data are not easily understood. This project aims to improve the visibility of patterns that neural networks identify in data. Through an interactive web application, NVIZ affords the …
Thinking Local With Original Data In Ai And Machine Learning Research, David G. Taylor, Robert Mccloud
Thinking Local With Original Data In Ai And Machine Learning Research, David G. Taylor, Robert Mccloud
WCBT Working Papers
Sacred Heart University spent significant funds to establish an AI lab. Initially there is no ongoing research and no real plan for a research agenda. This paper details how the Jack Welch College of Business and Technology created and implemented an active meaningful research plan. It involves two key elements: thinking local and using business connections to foster active, impactful research. Surrounding communities, business connections, area environment, and other Sacred Heart University departments all played a part. The research plan also identifies a specific issue in working with local and business contact sources: the AI researcher almost never gets data …
Automating Intersection Marking Data Collection And Condition Assessment At Scale With An Artificial Intelligence-Powered System, Kun Xie, Huiming Sun, Xiaomeng Dong, Hong Yang, Hongkai Yu
Automating Intersection Marking Data Collection And Condition Assessment At Scale With An Artificial Intelligence-Powered System, Kun Xie, Huiming Sun, Xiaomeng Dong, Hong Yang, Hongkai Yu
Civil & Environmental Engineering Faculty Publications
Intersection markings play a vital role in providing road users with guidance and information. The conditions of intersection markings will be gradually degrading due to vehicular traffic, rain, and/or snowplowing. Degraded markings can confuse drivers, leading to increased risk of traffic crashes. Timely obtaining high-quality information of intersection markings lays a foundation for making informed decisions in safety management and maintenance prioritization. However, current labor-intensive and high-cost data collection practices make it very challenging to gather intersection data on a large scale. This paper develops an automated system to intelligently detect intersection markings and to assess their degradation conditions with …
Strategic Perspective Of Leveraging New Generation Information Technology To Enable Modernization Of Emergency Management, Haibo Zhang, Xinyu Dai, Depei Qian, Jian Lyu
Strategic Perspective Of Leveraging New Generation Information Technology To Enable Modernization Of Emergency Management, Haibo Zhang, Xinyu Dai, Depei Qian, Jian Lyu
Bulletin of Chinese Academy of Sciences (Chinese Version)
The application and development of the new generation information technology is a vital support to realize the modernization of emergency management. At present, the new generation information technology such as big data and artificial intelligence has been widely used in natural disasters, safe production, and other fields. It has improved the monitoring and early warning, regulation and law enforcement, command and decision support, rescue, and social mobilization capabilities of governments, promoted the level of intrinsic safety of enterprises, provided important support for the precise prevention and control of the COVID-19, and increased the efficiency of China’s emergency management and sense …
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche
Electronic Theses and Dissertations
The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …
New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene
New Debiasing Strategies In Collaborative Filtering Recommender Systems: Modeling User Conformity, Multiple Biases, And Causality., Mariem Boujelbene
Electronic Theses and Dissertations
Recommender Systems are widely used to personalize the user experience in a diverse set of online applications ranging from e-commerce and education to social media and online entertainment. These State of the Art AI systems can suffer from several biases that may occur at different stages of the recommendation life-cycle. For instance, using biased data to train recommendation models may lead to several issues, such as the discrepancy between online and offline evaluation, decreasing the recommendation performance, and hurting the user experience. Bias can occur during the data collection stage where the data inherits the user-item interaction biases, such as …
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Assessing Photogrammetry Artificial Intelligence In Monumental Buildings’ Crack Digital Detection, Said Maroun, Mostafa Khalifa, Nabil Mohareb
Architecture and Planning Journal (APJ)
Natural and human-made disasters have significant impacts on monumental buildings, threatening them from being deteriorated. If no rapid consolidations took into consideration traumatic accidents would endanger the existence of precious sites. In this context, Beirut's enormous 4th of August 2020 explosion damaged an estimated 640 historical monuments, many volunteers assess damages for more than a year to prevent the more crucial risk of demolitions. This research aims to assist the collaboration ability among photogrammetry science, Artificial Intelligence Model (AIM) and Architectural Coding to optimize the process for better coverage and scientific approach of data specific to the crack disorders to …
Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher
Assessing Feature Representations For Instance-Based Cross-Domain Anomaly Detection In Cloud Services Univariate Time Series Data, Rahul Agrahari, Matthew Nicholson, Clare Conran, Haythem Assem, John D. Kelleher
Articles
In this paper, we compare and assess the efficacy of a number of time-series instance feature representations for anomaly detection. To assess whether there are statistically significant differences between different feature representations for anomaly detection in a time series, we calculate and compare confidence intervals on the average performance of different feature sets across a number of different model types and cross-domain time-series datasets. Our results indicate that the catch22 time-series feature set augmented with features based on rolling mean and variance performs best on average, and that the difference in performance between this feature set and the next best …
Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil
Explainabilityaudit: An Automated Evaluation Of Local Explainability In Rooftop Image Classification, Duleep Rathgamage Don, Jonathan Boardman, Sudhashree Sayenju, Ramazan Aygun, Yifan Zhang, Bill Franks, Sereres Johnston, George Lee, Dan Sullivan, Girish Modgil
Published and Grey Literature from PhD Candidates
Explainable Artificial Intelligence (XAI) is a key concept in building trustworthy machine learning models. Local explainability methods seek to provide explanations for individual predictions. Usually, humans must check these explanations manually. When large numbers of predictions are being made, this approach does not scale. We address this deficiency for a rooftop classification problem specifically with ExplainabilityAudit, a method that automatically evaluates explanations generated by a local explainability toolkit and identifies rooftop images that require further auditing by a human expert. The proposed method utilizes explanations generated by the Local Interpretable Model-Agnostic Explanations (LIME) framework as the most important superpixels of …
Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He
Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He
Computer Science Faculty Publications
This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow …
Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard
Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard
James Madison Undergraduate Research Journal (JMURJ)
The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …
A Smarter Way To Manage Mass Transit In A Smart City: Rail Network Management At Singapore’S Land Transport Authority, Steven M. Miller, Thomas H. Davenport
A Smarter Way To Manage Mass Transit In A Smart City: Rail Network Management At Singapore’S Land Transport Authority, Steven M. Miller, Thomas H. Davenport
Research Collection School Of Computing and Information Systems
There is no widely agreed upon definition of a supposed “Smart City.” Yet, when you see city employees — in this case city-state employees — working in what are obviously smarter ways, “you know it when you see it.” One such example of a smarter way to work in a smart city setting is the way that employees of the Land Transport Authority (LTA) in Singapore are using a new generation of data driven, AI-enabled support systems to manage the city’s urban rail network. We spoke to LTA officers Kong Wai, Ho (Director of Integrated Operations and Planning) and Chris …
Pathways To The Native Storyteller: A Method To Enable Computational Story Understanding, Aramide O. Kehinde
Pathways To The Native Storyteller: A Method To Enable Computational Story Understanding, Aramide O. Kehinde
College of Computing and Digital Media Dissertations
The primary objective of this thesis is to develop a method that uses machine learning algorithms to enable computational story understanding. This research is conducted with the aim of establishing a system called the Native Storyteller that plans and creates storytelling experiences for human users. The paper first establishes the desired capabilities of the system and then deep dives into how to enable story understanding, which is the core ability the system needs to function. As such, the research places emphasis on natural language processing and its application to solving key problems in this context. Namely, machine representation of story …
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Theses and Dissertations
A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
Articles
This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …
Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye
Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye
Branch Mathematics and Statistics Faculty and Staff Publications
Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic. Neutrosophic sets and logic are gaining significant attention in solving many real life decision making problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. They have been applied in computational intelligence, multiple criteria decision making, image processing, medical diagnoses, etc. This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.