Artificial Intelligence and Robotics Commons^™

Bridging The Gap: Ai And The Hidden Structure Of Consciousness, Emily Barnes, James Hutson

Faculty Scholarship

The quest to develop Artificial Intelligence (AI) systems that possess human-like consciousness necessitates a deep dive into both theoretical and practical aspects underpinning this ambitious goal. This article builds on initial philosophical explorations of AI consciousness by examining the intricate and often hidden structures that may facilitate conscious experiences in AI. Drawing from concepts in cognitive science and neuroscience, the article elucidates how AI systems can be designed to replicate the structural and functional aspects of human consciousness. The discussion includes the Hierarchy of Spatial Belongings proposed by Forti (2024), frameworks like the Integrated Information Theory (IIT), and models linking …

Physics-Informed Machine Learning Methods For Inverse Design Of Multi-Phase Materials With Targeted Mechanical Properties, Yunpeng Wu

All Dissertations

Advances in machine learning algorithms and applications have significantly enhanced engineering inverse design capabilities. This work focuses on the machine learning-based inverse design of material microstructures with targeted linear and nonlinear mechanical properties. It involves developing and applying predictive and generative physics-informed neural networks for both 2D and 3D multiphase materials.

The first investigation aims to develop a machine learning method for the inverse design of 2D multiphase materials, particularly porous materials. We first develop machine learning methods to understand the implicit relationship between a material's microstructure and its mechanical behavior. Specifically, we use ResNet-based models to predict the elastic …

We Train Ai, Why Not Humans, Too? An Exploration Of Human-Ai Team Training For Future Workplace Viability, Caitlin M. Lancaster

All Dissertations

The integration of Artificial Intelligence (AI) in the workforce is transforming team dynamics, leading to the emergence of Human-AI Teams (HATs). These teams offer opportunities to capitalize on human strengths with AI's prowess, offering significant opportunities for innovation and efficiency. Effective HAT functioning requires aligning human expectations with AI capabilities and bridging knowledge gaps between teammates. Despite this potential, key integration challenges remain, such as developing shared mental models, addressing skill limitations, and overcoming negative AI perceptions. Existing training efforts often apply human-human teaming principles directly to HATs, overlooking AI's role as a teammate and limiting the development of HAT-specific …

Exploring A Multimodal Fusion-Based Deep Learning Network For Detecting Facial Palsy, Heng Yim Nicole Oo, Min Hun Lee, J. H. Lim

Research Collection School Of Computing and Information Systems

Algorithmic detection of facial palsy offers the potential to improve current practices, which usually involve labor-intensive and subjective assessment by clinicians. In this paper, we present a multimodal fusion-based deep learning model that utilizes unstructured data (i.e. an image frame with facial line segments) and structured data (i.e. features of facial expressions) to detect facial palsy. We then contribute to a study to analyze the effect of different data modalities and the benefits of a multimodal fusion-based approach using videos of 21 facial palsy patients. Our experimental results show that among various data modalities (i.e. unstructured data - RGB images …

Deep Representation Learning For Time Series Forecasting, Gerald Woo

Dissertations and Theses Collection (Open Access)

Time series forecasting has critical applications across business and scien- tific domains, such as demand forecasting, capacity planning and management, and anomaly detection. Being able to predict the future yields immense value, allowing us to make downstream decisions with more confidence. Deep learning for time series forecasting is a burgeoning area of research, moving away from simple linear models found in classical time series analysis literature, towards more expressive, data hungry neural network architectures.

In this thesis, we develop methods leveraging deep representation learning for time series forecasting, from exploring neural network architecture designs which encode inductive biases specific to …

Integration Of Matlab And Machine Learning To Accelerate Evaluation Of Biological Activity In Agricultural Soils And Promote Soil Health Improvement Goals, Andrew Stiven Ortiz Balsero

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Traditionally, assessments of soil biological activity have been confined to laboratory settings, creating a disconnect with practical in-field methods. To bridge this gap, cotton fabric degradation has been used to illustrate soil microbial activity under different management practices. While effective, these demonstrations are subjective and labor-intensive.

Researchers have explored using image processing software like ImageJ and Adobe Photoshop to streamline this process. Although these tools accurately quantified fabric degradation under varying soil conditions, the methods remained labor-intensive and complex. Consequently, these methods were still not ideal for on-farm use by agricultural practitioners.

To further address labor and complexity limitations, the …

Solving Long-Run Average Reward Robust Mdps Via Stochastic Games, Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi, Petr Novotný, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities into account. Robust Markov decision processes (RMDPs) address this shortcoming of MDPs by assigning to each transition an uncertainty set rather than a single probability value. In this work, we consider polytopic RMDPs in which all uncertainty sets are polytopes and study the problem of solving long-run average reward polytopic RMDPs. We present a novel perspective on this problem and show that it can be reduced to solving long-run average reward turn-based stochastic games with finite state …

Task Scheduling Strategy For 3dpcp Considering Multidynamic Information Perturbation In Green Scene, Jianjia He, Jian Wu, Keng Siau

Research Collection School Of Computing and Information Systems

The 3D printing cloud platform (3DPCP) plays a pivotal role in breaking down the information silos between supply and demand, effectively reducing waste through information integration and intelligent production. However, due to the complexity of 3DPCP scheduling in green scenes and the multidynamic information perturbations, unveils problems in traditional task scheduling methods in 3DPCP. These issues manifest as incomplete considerations, subpar green performance, and weak adaptability to dynamic changes. There is an urgent need to design practical methods to realize the multidynamic information perturbations in green scenes within 3DPCP. Therefore, this article first defines the 3DPCP task scheduling problem for …

Optimization Of Customer Service And Driver Dispatch Areas For On-Demand Food Delivery, Jingfeng Yang, Hoong Chuin Lau, Hai Wang

Research Collection School Of Computing and Information Systems

With the rapid development and popularization of mobile and wireless communication technologies, on-demand food delivery (OFD) platforms have been able to connect restaurants, customers, and drivers in real time, drastically changing dining and food delivery services. Motivated by the critical need for supply and demand management in the on-demand food delivery market, we focus on the optimization of customer service area and driver dispatch area for on-demand food delivery services. Specifically, for each restaurant, the platform needs to decide the (1) customer service area (CSA), i.e., the surrounding area within which customers can see the restaurant’s information and order food …

Sibo : A Simple Booster For Parameter-Efficient Fine-Tuning, Zhihao Wen, Jie Zhang, Yuan Fang

Research Collection School Of Computing and Information Systems

Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time. Latest advancements in parameter-efficient fine-tuning (PEFT) techniques, such as Adapter tuning and LoRA, allow for adjustments to only a minor fraction of the parameters of these LLMs. Concurrently, it has been noted that the issue of over-smoothing diminishes the effectiveness of these Transformer-based LLMs, resulting in suboptimal performances in downstream tasks. In this paper, we present SIBO, which is a SImple BOoster to enhance PEFT, by injecting an initial residual. SIBO is straightforward and readily extensible to a range of state-of-the-art PEFT techniques to alleviate …

Tackling Stackelberg Network Interdiction Against A Boundedly Rational Adversary, Tien Mai, Avinandan Bose, Arunesh Sinha, Thanh Nguyen, Ayushman Kumar Singh

Research Collection School Of Computing and Information Systems

This work studies Stackelberg network interdiction games --- an important class of games in which a defender first allocates (randomized) defense resources to a set of critical nodes on a graph while an adversary chooses its path to attack these nodes accordingly. We consider a boundedly rational adversary in which the adversary's response model is based on a dynamic form of classic logit-based (quantal response) discrete choice models. The resulting optimization is non-convex and additionally, involves complex terms that sum over exponentially many paths. We tackle these computational challenges by presenting new efficient algorithms with solution guarantees. First, we present …

A Survey On Neural Question Generation : Methods, Applications, And Prospects, Shasha Guo, Lizi Liao, Cuiping Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

In this survey, we present a detailed examination of the advancements in Neural Question Generation (NQG), a field leveraging neural network techniques to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The survey begins with an overview of NQG’s background, encompassing the task’s problem formulation, prevalent benchmark datasets, established evaluation metrics, and notable applications. It then methodically classifies NQG approaches into three predominant categories: structured NQG, which utilizes organized data sources, unstructured NQG, focusing on more loosely structured inputs like texts or visual content, and hybrid NQG, drawing on diverse input modalities. This classification is followed …

Towards Gradient-Based Time-Series Explanations Through A Spatiotemporal Attention Network, Min Hun Lee

Research Collection School Of Computing and Information Systems

In this paper, we explore the feasibility of using a transformer-based, spatiotemporal attention network (STAN) for gradient-based time-series explanations. First, we trained the STAN model for video classifications using the global and local views of data and weakly supervised labels on time-series data (i.e. the type of an activity). We then leveraged a gradient-based XAI technique (e.g. saliency map) to identify salient frames of time-series data. According to the experiments using the datasets of four medically relevant activities, the STAN model demonstrated its potential to identify important frames of videos.

Causvsr: Causality Inspired Visual Sentiment Recognition, Xinyue Zhang, Zhaoxia Wang, Hailing Wang, Jing Xiang, Chunwei Wu, Guitao Cao

Research Collection School Of Computing and Information Systems

Visual Sentiment Recognition (VSR) is an evolving field that aims to detect emotional tendencieswithin visual content. Despite its growing significance, detecting emotions depicted in visual content,such as images, faces challenges, notably the emergence of misleading or spurious correlationsof the contextual information. In response to these challenges, we propose a causality inspired VSRapproach, called CausVSR. CausVSR is rooted in the fundamental principles of Emotional Causalitytheory, mimicking the human process from receiving emotional stimuli to deriving emotional states.CausVSR takes a deliberate stride toward conquering the VSR challenges. It harnesses the power of astructural causal model, intricately designed to encapsulate the dynamic causal …

Beyond Automation: Ai As A Catalyst For New Job Creation In Software Development, Jill Willard, James Hutson

Faculty Scholarship

As artificial intelligence (AI) continues to evolve, its impact on software development and programming is profound, drawing parallels to the shift from assembler to object-oriented programming. This article explores how AI is reshaping the landscape of software jobs, creating new opportunities rather than diminishing them. By simplifying complex tasks and lowering barriers to coding, AI is expanding the technology "pie," introducing new use cases, and enhancing efficiency. The transition from monolithic services to microservices has reduced risks and accelerated deployment processes, and AI is poised to further this evolution by managing the complexities of service interactions through advanced orchestration layers. …

Leveraging Generative Ai For Sustainable Farm Management Techniques Correspond To Optimization And Agricultural Efficiency Prediction, Samira Samrose

All Graduate Reports and Creative Projects, Fall 2023 to Present

Sustainable farm management practice is a multifaceted challenge. Uncovering the optimal state for production while reduction of environmental negative impacts and guaranteed inter-generational assets supervision needs balanced management. Also, considering lots of different factors (cost, profit, employment etc), the agricultural based management technique requires rigorous concentration. In this project machine learning models are applied to develop, achieve and improve the farm management techniques. This experiment ensures the resultant impacts being environment friendly and necessary resource availability and efficiency. Predicting the type of crop and rotational recommendations will disclose potentiality of productive agricultural based farming. Additionally, this project is designed to …

Cross-Problem Learning For Solving Vehicle Routing Problems, Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou, Zhiguang Cao, Wen Song, Yingqian Zhang, Senthilnath Jayavelu

Research Collection School Of Computing and Information Systems

Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP variants. This paper proposes the cross-problem learning to assist heuristics training for different downstream VRP variants. Particularly, we modularize neural architectures for complex VRPs into 1) the backbone Transformer for tackling the travelling salesman problem (TSP), and 2) the additional lightweight modules for processing problem-specific features in complex VRPs. Accordingly, we propose to pre-train the backbone Transformer for TSP, and then apply it in the process of fine-tuning the Transformer models for each target VRP variant. …

Certified Policy Verification And Synthesis For Mdps Under Distributional Reach-Avoidance Properties, S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Markov Decision Processes (MDPs) are a classical model for decision making in the presence of uncertainty. Often they are viewed as state transformers with planning objectives defined with respect to paths over MDP states. An increasingly popular alternative is to view them as distribution transformers, giving rise to a sequence of probability distributions over MDP states. For instance, reachability and safety properties in modeling robot swarms or chemical reaction networks are naturally defined in terms of probability distributions over states. Verifying such distributional properties is known to be hard and often beyond the reach of classical state-based verification techniques. In …

Fuel-Saving Route Planning With Data-Driven And Learning-Based Approaches: A Systematic Solution For Harbor Tugs, Shengming Wang, Xiaocai Zhang, Jing Li, Xiaoyang Wei, Hoong Chuin Lau, Bing Tian Dai, Binbin Huang Huang, Zhe Xiao, Xiuju Fu, Zheng Qin

Research Collection School Of Computing and Information Systems

In recent years, there are trends toward cleaner port environments through enforcement by imposed legislation. Transit optimisation of fuel-based port service boats like harbour tugs has emerged as a critical task to reduce fuel consumption and carbon emission. In this paper, an innovative learning-based method, comprising a Reinforcement Learning (RL) model together with a fuel consumption prediction model, was proposed to formulate fuel-saving transit routes. Firstly, an ensemble model is established by combining a Long Short-Term Memory (LSTM) model with a Multilayer Perceptron (MLP) model, predicting fuel use based on tugboat movement and environment factors. Subsequently, an innovative RL based …

Is Aggregation The Only Choice? Federated Learning Via Layer-Wise Model Recombination, Ming Hu, Zhihao Yue, Xiaofei Xie, Cheng Chen Chen

Research Collection School Of Computing and Information Systems

Although Federated Learning (FL) enables global model training Xiaofei Xie [email protected] Singapore Management University Singapore, Singapore Xian Wei [email protected] East China Normal University Shanghai, China Mingsong Chen∗ [email protected] East China Normal University Shanghai, China • Computing methodologies → Distributed artificial intelligence. across clients without compromising their raw data, due to the unevenly distributed data among clients, existing Federated Averaging (FedAvg)-based methods suffer from the problem of low inference performance. Specifically, different data distributions among clients lead to various optimization directions of local models. Aggregating local models usually results in a low-generalized global model, which performs worse on most of the …