Maple: Multi-Modal Prompt Learning,
2023
Mohamed Bin Zayed University of Artificial Intelligence
Maple: Multi-Modal Prompt Learning, Muhammad Uzair Khattak, Hanoona Rasheed, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan
Computer Vision Faculty Publications
Pre-trained vision-language (V-L) models such as CLIP have shown excellent generalization ability to downstream tasks. However, they are sensitive to the choice of input text prompts and require careful selection of prompt templates to perform well. Inspired by the Natural Language Processing (NLP) literature, recent CLIP adaptation approaches learn prompts as the textual inputs to fine-tune CLIP for downstream tasks. We note that using prompting to adapt representations in a single branch of CLIP (language or vision) is sub-optimal since it does not allow the flexibility to dynamically adjust both representation spaces on a downstream task. In this work, we …
Ml-Based Surrogates And Emulators,
2023
Old Dominion University
Ml-Based Surrogates And Emulators, Tareq Alghamdi, Yaohang Li, Nobuo Sato
College of Sciences Posters
No abstract provided.
Differentially Private Stochastic Convex Optimization In (Non)-Euclidean Space Revisited,
2023
Mohamed Bin Zayed University of Artificial Intelligence
Differentially Private Stochastic Convex Optimization In (Non)-Euclidean Space Revisited, Jinyan Su, Changhong Zhao, Di Wang
Machine Learning Faculty Publications
In this paper, we revisit the problem of Differentially Private Stochastic Convex Optimization (DP-SCO) in Euclidean and general `dp spaces. Specifically, we focus on three settings that are still far from well understood: (1) DP-SCO over a constrained and bounded (convex) set in Euclidean space; (2) unconstrained DP-SCO in `dp space; (3) DP-SCO with heavy-tailed data over a constrained and bounded set in `dp space. For problem (1), for both convex and strongly convex loss functions, we propose methods whose outputs could achieve (expected) excess population risks that are only dependent on the Gaussian width of the constraint set, rather …
Metaenhance: Metadata Quality Improvement For Electronic Theses And Dissertations,
2023
Old Dominion University
Metaenhance: Metadata Quality Improvement For Electronic Theses And Dissertations, Muntabir H. Choudhury, Lamia Salsabil, Himarsha R. Jayanetti, Jian Wu
College of Sciences Posters
Metadata quality is crucial for digital objects to be discovered through digital library interfaces. Although DL systems have adopted Dublin Core to standardize metadata formats (e.g., ETD-MS v1.11), the metadata of digital objects may contain incomplete, inconsistent, and incorrect values [1]. Most existing frameworks to improve metadata quality rely on crowdsourced correction approaches, e.g., [2]. Such methods are usually slow and biased toward documents that are more discoverable by users. Artificial intelligence (AI) based methods can be adopted to overcome this limit by automatically detecting, correcting, and canonicalizing the metadata, featuring quick and unbiased responses to document metadata. …
Nudyclr: Nuclear Dynamic Co-Learned Representations,
2023
Center for Astrophysics, Harvard & Smithsonian
Nudyclr: Nuclear Dynamic Co-Learned Representations, Víctor Samuel Pérez-Díaz
2023 REYES Proceedings
NuCLR (Nuclear Co-Learned Representations) is a cutting-edge multi-task deep learning framework designed to predict essential nuclear observables, including binding energies, decay energies, and nuclear charge radii. As part of the REYES Mentorship Program, we investigated the application of dynamic loss weighting to further refine NuCLR’s predictive performance. Our findings indicate that while weighting strategies can enhance accuracy in specific tasks, such as binding energy prediction, they may underperform in others. Equal Weighting (EW), the original method employed by NuCLR, demonstrated consistent performance across multiple tasks, affirming its robustness. This report succinctly presents the developments and results of the mentorship program …
Artificial Emotional Intelligence In Socially Assistive Robots,
2023
University of Denver
Artificial Emotional Intelligence In Socially Assistive Robots, Hojjat Abdollahi
Electronic Theses and Dissertations
Artificial Emotional Intelligence (AEI) bridges the gap between humans and machines by demonstrating empathy and affection towards each other. This is achieved by evaluating the emotional state of human users, adapting the machine’s behavior to them, and hence giving an appropriate response to those emotions. AEI is part of a larger field of studies called Affective Computing. Affective computing is the integration of artificial intelligence, psychology, robotics, biometrics, and many more fields of study. The main component in AEI and affective computing is emotion, and how we can utilize emotion to create a more natural and productive relationship between humans …
Terrain Cost Learning From Human Preferences For Robot Path Planning Using A Visual User Interface,
2023
University of Denver
Terrain Cost Learning From Human Preferences For Robot Path Planning Using A Visual User Interface, Kaivalya Velagapudi
Electronic Theses and Dissertations
Robot navigation in terrains with limited exploration and limited knowledge has been a problem of interest in robotics due to the potential dangers that may arise during traversal. Due to the large number of path permutations within a complex and feature-rich real-world environment, and in the interest of saving time and ensuring safety, the robot should learn the optimal path without repeated exploration of the terrain. This can be accomplished by leveraging the path preferences of a human operator so that, with selective inputs, the agent can effectively learn a terrain-cost mapping in order to determine the optimal route, thereby …
Leveraging Explainable Artificial Intelligence (Xai) To Understand Performance Deviations In Load Tests Of Large Software Systems,
2023
Marshall University
Leveraging Explainable Artificial Intelligence (Xai) To Understand Performance Deviations In Load Tests Of Large Software Systems, Eric Shoemaker
Theses, Dissertations and Capstones
Performance testing generates vast amounts of data, making it challenging for human analysts to process within a reasonable timeframe. Therefore, black-box machine learning models are often used to determine pass/fail status, but these models lack transparency and cannot explain why a test has failed, leading to a time-consuming manual analysis process. To address this issue, this thesis proposes using Explainable Artificial Intelligence (XAI) to improve the trustworthiness of black-box and interpretable models in performance testing. The proposed approach leverages the Shapley Additive Explanation (SHAP) algorithm as a surrogate model to help performance analysts understand the decision-making process of black-box machine …
Establishing The Legal Framework To Regulate Quantum Computing Technology,
2023
Catholic University of America (Student)
Establishing The Legal Framework To Regulate Quantum Computing Technology, Kaya Derose
Catholic University Journal of Law and Technology
No abstract provided.
Towards Explainable Ai Using Attribution Methods And Image Segmentation,
2023
University of Central Florida
Towards Explainable Ai Using Attribution Methods And Image Segmentation, Garrett J. Rocks
Honors Undergraduate Theses
With artificial intelligence (AI) becoming ubiquitous in a broad range of application domains, the opacity of deep learning models remains an obstacle to adaptation within safety-critical systems. Explainable AI (XAI) aims to build trust in AI systems by revealing important inner mechanisms of what has been treated as a black box by human users. This thesis specifically aims to improve the transparency and trustworthiness of deep learning algorithms by combining attribution methods with image segmentation methods. This thesis has the potential to improve the trust and acceptance of AI systems, leading to more responsible and ethical AI applications. An exploratory …
Introduction: Imagined And Real Ai,
2023
Seattle Pacific University
Introduction: Imagined And Real Ai, Michael Paulus
SPU Works
The increasing role and power of artificial intelligence in our lives and world require us to imagine and shape a desirable future with this technology. Since visions of AI often draw from Christian apocalyptic narratives, current discussions about technological hopes and fears present an opportunity for a deeper engagement with Christian eschatological resources. This book argues that the apocalyptic imagination can transform how we think about and use AI, helping us discover ways artificial agency may help us create a better world.
Scheduling Electric Vehicle Charging For Grid Load Balancing,
2023
Western University
Scheduling Electric Vehicle Charging For Grid Load Balancing, Zhixin Han, Katarina Grolinger, Miriam Capretz, Syed Mir
Electrical and Computer Engineering Publications
In recent years, electric vehicles (EVs) have been widely adopted because of their environmental benefits. However, the increasing volume of EVs poses capacity issues for grid operators as simultaneously charging many EVs may result in grid instabilities. Scheduling EV charging for grid load balancing has a potential to prevent load peaks caused by simultaneous EV charging and contribute to balance of supply and demand. This paper proposes a user-preference-based scheduling approach to minimize costs for the user while balancing grid loads. The EV owners benefit by charging when the electricity cost is lower, but still within the user-defined preferred charging …
A Machine Learning Approach To Deepfake Detection,
2023
Minnesota State University, Mankato
A Machine Learning Approach To Deepfake Detection, Delaney Conrad
All Undergraduate Theses and Capstone Projects
The ability to manipulate videos has been around for decades but a process that once would take time, money, and professionals, can now be created by anyone due to the rapid advancement of deepfake technology. Deepfakes use deep learning artificial intelligence to make fake digital content, typically in the form of swapping a person’s face in a video or image. This technology could easily threaten and manipulate individuals, corporations, and political organizations, so it is essential to find methods for detecting deepfakes. As the technology for creating deepfakes continues to improve, these manipulated videos are becoming increasingly undetectable. It is …
A Multistage Framework For Detection Of Very Small Objects,
2023
Kennesaw State University
A Multistage Framework For Detection Of Very Small Objects, Duleep Rathgamage Don, Ramazan Aygun, Mahmut Karakaya
Published and Grey Literature from PhD Candidates
Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our …
How Ai Can Learn From The Law: Putting Humans In The Loop Only On Appeal,
2023
Penn State Dickinson Law
How Ai Can Learn From The Law: Putting Humans In The Loop Only On Appeal, I. Glenn Cohen, Boris Babic, Sara Gerke, Qiong Xia,, Theodoros Evgeniou, Klaus Wertenbroch
Faculty Scholarly Works
While the literature on putting a “human in the loop” in artificial intelligence (AI) and machine learning (ML) has grown significantly, limited attention has been paid to how human expertise ought to be combined with AI/ML judgments. This design question arises because of the ubiquity and quantity of algorithmic decisions being made today in the face of widespread public reluctance to forgo human expert judgment. To resolve this conflict, we propose that human expert judges be included via appeals processes for review of algorithmic decisions. Thus, the human intervenes only in a limited number of cases and only after an …
Inventions In The Area Of Nanotechnologies And Nanomaterials. Part I,
2023
Old Dominion University
Inventions In The Area Of Nanotechnologies And Nanomaterials. Part I, Leonid A. Ivanov, Li Da Xu, Zhanna V. Pisarenko, Svetlana R. Muminova, Nadezda G. Miloradova
Information Technology & Decision Sciences Faculty Publications
Introduction. Advanced technologies inspire people by demonstrating the latest achievements (materials, methods, systems, technologies, devices etc.) that dramatically change the world. This, first of all, concerns nanotechnological inventions designed by scientists, engineers and specialists from different countries. Main part. The article provides an abstract overview of inventions of scientists, engineers and specialists from different countries: Germany, Russia, China, USA et al. The results of the creative activity of scientists, engineers and specialists, including inventions in the field of nanotechnology and nanomaterials allow, when introduced to industry, achieving a significant effect in construction, housing and communal services, and related sectors of …
Deep Learning Neural Machine Translation Conversational Agent,
2023
San Jose State University
Deep Learning Neural Machine Translation Conversational Agent, Abhishek Vaid
Master's Projects
Neural Machine Translation (NMT) is a prominent natural language processing technique that is being used to develop conversational AI technology. However, most chatbots do not provide live API features and have list-based scripted responses. Most chatbots are majorly restricted by the training data on which they were trained on and have no knowledge of current events. This research project intends to research and develop an approach to providing live information. We experiment with various techniques in terms of the type of data being used to harness live capabilities. We optimize the hyperparameters that are needed for a Conversational AI agent …
The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie,
2023
Northern Illinois University
The Role Of Machine Learning And Network Analyses In Understanding Microbial Composition In An Experimental Prairie, Ali Eastman Oku
Graduate Research Theses & Dissertations
Machine learning and network analyses are powerful modern tools can process and map out connections between large amount of ecological data from complex environmental communities. Random forests, an ensemble machine learning algorithm, are particularly powerful as they can capture complex patterns in data while remaining easily interpretable. These tools are specifically useful in experimental settings where different types of data are collected. The aim of this study was to demonstrate the utility of machine learning models and network analyses at analyzing diverse ecological data from dynamic plant-soil microbial communities in a prairie ecosystem. Our experimental system is an experimental prairie …
Speculative Futures On Chatgpt And Generative Artificial Intelligence (Ai): A Collective Reflection From The Educational Landscape,
2023
Old Dominion University
Speculative Futures On Chatgpt And Generative Artificial Intelligence (Ai): A Collective Reflection From The Educational Landscape, Aras Bozkurt, Junhong Xiao, Sarah Lambert, Angelica Pazurek, Helen Crompton, Suzan Koseoglu, Robert Farrow, Melissa Bond, Chrissi Nerantzi, Sarah Honeychurch, Maha Bali, Jon Dron, Kamran Mir, Bonnie Stewart, Eamon Costello, Jon Mason, Christian M. Stracke, Enilda Romero-Hall, Apostolos Koutropoulos, Cathy Mae Toquero, Lenandlar Singh, Ahmed Tlili, Kyungmee Lee, Mark Nichols, Ebba Ossiannilsson, Mark Brown, Valerie Irvine, Juliana Elisa Raffaghelli, Gema Santos-Hermosa, Orna Farrell, Taskeen Adam, Ying Li Thong, Sunagul Sani-Bozkurt, Ramesh C. Sharma, Stefan Hrastinski, Petar Jandrić
Teaching & Learning Faculty Publications
While ChatGPT has recently become very popular, AI has a long history and philosophy. This paper intends to explore the promises and pitfalls of the Generative Pre-trained Transformer (GPT) AI and potentially future technologies by adopting a speculative methodology. Speculative future narratives with a specific focus on educational contexts are provided in an attempt to identify emerging themes and discuss their implications for education in the 21st century. Affordances of (using) AI in Education (AIEd) and possible adverse effects are identified and discussed which emerge from the narratives. It is argued that now is the best of times to define …
Equitable Ecosystem: A Two-Pronged Approach To Equity In Artificial Intelligence,
2023
University of Pennsylvania Law School
Equitable Ecosystem: A Two-Pronged Approach To Equity In Artificial Intelligence, Rangita De Silva De Alwis, Amani Carter, Govind Nagubandi
Michigan Technology Law Review
Lawmakers, technologists, and thought leaders are facing a once-in-a-generation opportunity to build equity into the digital infrastructure that will power our lives; we argue for a two-pronged approach to seize that opportunity. Artificial Intelligence (AI) is poised to radically transform our world, but we are already seeing evidence that theoretical concerns about potential bias are now being borne out in the market. To change this trajectory and ensure that development teams are focused explicitly on creating equitable AI, we argue that we need to shift the flow of investment dollars. Venture Capital (VC) firms have an outsized impact in determining …
