Artificial Intelligence and Robotics Commons^™

Golden Eagle Optimizer Algorithm Combining Levy Flight And Brownian Motion, Jiaxin Deng, Damin Zhang, Qing He, Jianping Zhao

Journal of System Simulation

Aiming at the slow attenuation and low convergence precision of golden eagle optimization algorithm, a new algorithm combining Levy fight and Brownian motion is proposed.In order to increase the diversity, Fuch chaotic map is introduced to initialize the golden eagle individuals. Levy flight mechanism and Brownian motion mechanism are introduced into the position update formula of golden eagle individual to improve the search accuracy and help to the jump out of local optimum. The reduction factor is introduced into the overall position update formula of the golden eagle individual to improve the convergence speed. Compared with 9 original …

Agent-Based Ecosystem Simulation Research Under Forest Fire, Ying Li, Nisuo Du, Zhi Ouyang

Journal of System Simulation

An Agent-based multi-species simulation model under forest fireis proposed to study the effect of forest fire on the balance of animal species population.By abstracting elements of each type of species and fire in the forest fire process as agents, the attributes and behavior rules of each type of agents according to the real characteristics of each type of species and forest fire are refined. ABM model is used to show the characteristics of multi-agent interaction in complex systems, and construct a multi-species forest ecological model and a forest fire model. On the basis of validating the rationality of …

Opto-Mechanical-Thermal Coupling Analysis Method And Implementation Of High-Precision Optical System, Liang Zhao, Zhigang Zhang, Yao Sun

Journal of System Simulation

High-precision optical system is easy to be affected by space environment. Under the condition of high temperature, structural load, etc., the image quality of the optical system becomes poor, and the opto-mechanical-thermal coupling analysis is needed. Due to the independent development of the optical simulation, structure simulation, thermal simulation and others, the simulation data can not be effectively coupled and transferred.An interdisciplinary coupling analysis method is proposed, in which the integrated analysis idea is adopted and the polynomial fitting is used as the interface to solve the irregular deformation of optical element surface. Through the implement of the best …

Fine-Tuned Clip Models Are Efficient Video Learners, Hanoona Rasheed, Muhammad Uzair Khattak, Muhammad Maaz, Salman Khan, Fahad Shahbaz Khan

Computer Vision Faculty Publications

Large-scale multi-modal training with image-text pairs imparts strong generalization to CLIP model. Since training on a similar scale for videos is infeasible, recent approaches focus on the effective transfer of image-based CLIP to the video domain. In this pursuit, new parametric modules are added to learn temporal information and inter-frame relationships which require meticulous design efforts. Furthermore, when the resulting models are learned on videos, they tend to overfit on the given task distribution and lack in generalization aspect. This begs the following question: How to effectively transfer image-level CLIP representations to videos? In this work, we show that a …

Person Image Synthesis Via Denoising Diffusion Model, Ankan Kumar Bhunia, Salman Khan, Hisham Cholakkal, Rao Muhammad Anwer, Jorma Laaksonen, Mubarak Shah, Fahad Shahbaz Khan

Computer Vision Faculty Publications

The pose-guided person image generation task requires synthesizing photorealistic images of humans in arbitrary poses. The existing approaches use generative adversarial networks that do not necessarily maintain realistic textures or need dense correspondences that struggle to handle complex deformations and severe occlusions. In this work, we show how denoising diffusion models can be applied for high-fidelity person image synthesis with strong sample diversity and enhanced mode coverage of the learnt data distribution. Our proposed Person Image Diffusion Model (PIDM) disintegrates the complex transfer problem into a series of simpler forward-backward denoising steps. This helps in learning plausible source-to-target transformation trajectories …

Attention Visual, Baris Dingil

College of Computing and Digital Media Dissertations

This research presents an innovative approach to improving visual-spatial attention using a research tool based on the web. Recognizing the significant role visual-spatial attention plays in everyday life and cognitive function for humans, this research was undertaken with the aim of developing a user-friendly, accessible web-based tool called Attention Visual (attentionvisual.com) to enhance this crucial cognitive skill. This tool also facilitates data collection, potentially accelerating the pace and enhancing the quality of related research. Both qualitative and quantitative methods were utilized for data collection and analysis. In order to stimulate improvements in visual-spatial attention, the tool’s algorithm was structured to …

Digital Twin Haptic Robotic Arms: Towards Handshakes In The Metaverse, Mohd Faisal, Fedwa Laamarti, Abdulmotaleb El Saddik

Computer Vision Faculty Publications

More daily interactions are happening in the digital world of the metaverse. Providing individuals with means to perform a handshake during these interactions can enhance the overall user experience. In this paper, we put forward the design and implementation of two right-handed underactuated Digital Twin robotic arms to mediate the physical handshake interaction between two individuals. This allows them to perform a handshake while they are in separate locations. The experimental findings are very promising as our evaluation shows that the participants were highly interested in using our system to shake hands with their loved ones when they are physically …

Utilizing Few-Shot Meta Learning Algorithms For Medical Image Segmentation, Nick Littlefield

Thinking Matters Student Research Symposium

Deep learning models can be difficult to train because they require large amounts of data, which we usually do not have or are too expensive to get or annotate. To overcome this problem, we can use few-shot meta-learning, which allows us to train deep learning models with little data. Using a few examples, meta-learning, or learning-to-learn, aims to use the experience learned during training to generalize to unknown tasks. Medical imaging is an industry where it is particularly useful, as there is limited publicly available data due to patient privacy concerns and annotating costs.

This project examines how meta-learning performs …

Joint Flood Risks In The Grand River Watershed, Poornima Unnikrishnan, Kumaraswamy Ponnambalam, Nirupama Agrawal, Fakhri Karray

Machine Learning Faculty Publications

According to the World Meteorological Organization, since 2000, there has been an increase in global flood-related disasters by 134 percent compared to the previous decades. Efficient flood risk management strategies necessitate a holistic approach to evaluating flood vulnerabilities and risks. Catastrophic losses can occur when the peak flow values in the rivers in a basin coincide. Therefore, estimating the joint flood risks in a region is vital, especially when frequent occurrences of extreme events are experienced. This study focuses on estimating the joint flood risks due to river flow extremes in the Grand River watershed in Canada. For this purpose, …

Adversary Aware Continual Learning, Muhammad Umer

Theses and Dissertations

Continual learning approaches are useful as they help the model to learn new information (classes) sequentially, while also retaining the previously acquired information (classes). However, these approaches are adversary agnostic, i.e., they do not consider the possibility of malicious attacks. In this dissertation, we have demonstrated that continual learning approaches are extremely vulnerable to the adversarial backdoor attacks, where an intelligent adversary can introduce small amount of misinformation to the model in the form of imperceptible backdoor pattern during training to cause deliberate forgetting of a specific class at test time. We then propose a novel defensive framework to counter …

Machine Learning Data Feature Reduction And Model Optimization, Francisco P. Maturana, Phillip M. Lacasse

AFIT Patents

For machine learning data reduction and model optimization, a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from …

Towards An Experimental Bibliography Of Hemispheric Reconstruction Newspapers, Joshua Ortiz Baco, Benjamin Charles Germain Lee, Jim Casey, Sarah H. Salter

Criticism

Digital collections of newspapers have drawn broader attention to the fragmented and scattered print histories of minoritized communities. Attempts to survey these histories through bibliography, however, quickly meet with a fundamental problem: the practice of bibliographic description calls for creating a static record of social affiliations. Given the overwhelming scholarly consensus that categories such as race, ethnicity, and language are socially constructed, this article introduces an experimental bibliographic method for mapping the vast landscape of historical newspapers. This method extends the machine learning affordances of a recent project called Newspaper Navigator to enumerate the newspapers in Chronicling America according to …

Poly-Gan: Regularizing Polygons With Generative Adversarial Networks, Lasith Niroshan, James Carswell

Conference Papers

Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a data-driven and Deep Learning (DL) based approach for regularizing OpenStreetMap building polygon edges. The study introduces a building footprint regularization technique (Poly-GAN) that utilises a Generative Adversarial Network model trained on irregular building footprints and OSM vector data. The proposed method is particularly relevant for map features …

Stereotypes And Language Models: Understanding How Language Models Encode Stereotypes, Debiasing Language Models, And Examining How Stereotypes Affect Conversations, Brian C. Wang

Computer Science Senior Theses

This thesis describes a variety of approaches in examining how language models encode stereotypes (understanding stereotypes from a model point-of-view), debiasing language models, and using language models to understand how stereotypes affect conversations (understanding stereotypes from a conversational point-of-view). We present a novel approach for textual clues analysis that makes language models more interpretable, combining the understanding of what stereotypes the internal structures of language models have encoded during their initial training (via attention-based analysis) and understanding what textual clues are most relevant to identifying stereotypes for models trained to detect stereotypes (via SHAP-based analysis). We find that different pre-trained …

Sarcasm Detection In English And Arabic Tweets Using Transformer Models, Rishik Lad

Computer Science Senior Theses

This thesis describes our approach toward the detection of sarcasm and its various types in English and Arabic Tweets through methods in deep learning. There are five problems we attempted: (1) detection of sarcasm in English Tweets, (2) detection of sarcasm in Arabic Tweets, (3) determining the type of sarcastic speech subcategory for English Tweets, (4) determining which of two semantically equivalent English Tweets is sarcastic, and (5) determining which of two semantically equivalent Arabic Tweets is sarcastic. All tasks were framed as classification problems, and our contributions are threefold: (a) we developed an English binary classifier system with RoBERTa, …

Ai Art: Artists’ Best Friend Or Mortal Enemy?, Ethan Gabrys

Tredway Library Prize for First-Year Research

This paper analyzes the impacts and implications of generative AI software on art and examines the ethics of using such tools. Through the argument that careless use of these tools presents a danger to the art world as they risk devaluing human expression, Gabrys states that “as what it means to be human changes with each generation, new artists express sentiment through their art. Art has the ability to tell us about the human experience.” He concludes that the use of AI tools takes the skill and sentiment of human artists out of the equation, begging the question: if the …

Suitability Of Sdn And Mec To Facilitate Digital Twin Communication Over Lte-A, Hikmat Adhami, Mohammad Alja'afreh, Mohamed Hoda, Jiaqi Zhao, Yong Zhou, Abdulmotaleb Elsaddik

Computer Vision Faculty Publications

Haptic is the modality that complements traditional multimedia, i.e., audiovisual, to evolve the next wave of innovation at which the Internet data stream can be exchanged to enable remote skills and control applications. This will require ultra-low latency and ultra-high reliability to evolve the mobile experience into the era of Digital Twin and Tactile Internet. While the 5th generation of mobile networks is not yet widely deployed, Long-Term Evolution (LTE-A) latency remains much higher than the 1 ms requirement for the Tactile Internet and therefore the Digital Twin. This work investigates an interesting solution based on the incorporation of Software-defined …

An Investigation Into Machine Learning Techniques For Designing Dynamic Difficulty Agents In Real-Time Games, Ryan Adare Dunagan

Electronic Theses and Dissertations

Video games are an incredibly popular pastime enjoyed by people of all ages world wide. Many different kinds of games exist, but most games feature some elements of the player overcoming some challenge, usually through gameplay. These challenges are insurmountable for some people and may turn them off to video games as a pastime. Games can be made more accessible to players of little skill and/or experience through the use of Dynamic Difficulty Adjustment (DDA) systems that adjust the difficulty of the game in response to the player’s performance. This research seeks to establish the effectiveness of machine learning techniques …

Human-Ai Collaboration For Smart Education: Reframing Applied Learning To Support Metacognition, James Hutson, Daniel Plate

Faculty Scholarship

This chapter investigates the profound influence of intelligent virtual assistants (IVAs) on the educational domain, specifically in the realm of individualized learning and the instruction of writing abilities and content creation. IVAs, incorporating generative AI technologies such as ChatGPT and Stable Diffusion, hold the potential to bring about a paradigm shift in educational programs, emphasizing the enhancement of advanced metacognitive capacities rather than the fundamentals of communication. The subsequent recommendations stress the need to cultivate enduring proficiencies and ascertain tailored learning approaches for each learner, which will be indispensable for success in the evolving job market. In this context, prompt …

Exploring The Educational Potential Of Ai Generative Art In 3d Design Fundamentals: A Case Study On Prompt Engineering And Creative Workflows, James Hutson, Bryan Robertson

Faculty Scholarship

AI will be increasingly integrated into artistic practices and creative workflows with prompt engineering assuming an increasingly important role in the process. With readilyavailable generative AI, such as Midjourney, DALL-E 2, and Craiyon (formerly DALLE-mini), anyone can seemingly create "art,” prompting questions about the future necessity of art and design education. However, whereas the ease with which content can be created has seen an outcry from the traditional artmaking community, fears over widespread adoption replacing the need for a firm foundation in art and design principles and fundamentals is unfounded. Instead, these tools should be seen and adopted as other …