Artificial Intelligence and Robotics Commons^™

An Activity System-Based Perspective Of Generative Ai: Challenges And Research Directions, Fiona Fui-Hoon Nah, Jingyuan Cai, Ruilin Zheng, Natalie Pang

Research Collection School Of Computing and Information Systems

With its remarkable ability to generate content, generative artificial intelligence (GAI) has been recognized as a milestone in the development of artificial general intelligence. To understand the challenges, potential impact, and implications associated with GAI, we adopt a socio-technical perspective to analyze them. First, we identify the key characteristics of GAI, which include content generation, generalization ability, and reinforcement learning based on human feedback. Next, we address technological, ethical, societal, economic, regulatory, and governance challenges. Finally, we deploy activity theory to explore research directions in GAI. Research questions that warrant further investigation include how GAI may impact the future of …

Literature Review In The Generative Ai Era: How To Make A Compelling Contribution, Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

As we write this editorial for this special issue, we are amidst the significant technological changes that are continuing to shape society. Since the emergence of ChatGPT in November 2022, humanity has become aware of the potential of generative AI (i.e., AI that can generate content) and large language models (LLMs) (i.e., AI models trained on a massive corpus of unstructured data). There is growing debate and discussion about the promise and perils of generative AI for the future of work, and academia is not immune. Premier journals in the IS domain, such as Information Systems Research, have published editorials …

A Learning‑Based Approach For Estimating Inertial Properties Of Unknown Objects From Encoder Discrepancies, Zizhou Lao, Yuanfeng Han, Yunshan Ma, Gregory S. Chirikjian

Research Collection School Of Computing and Information Systems

Many robots utilize commercial force/torque sensors to identify inertial properties of unknown objects. However, such sensors can be difficult to apply to small-sized robots due to their weight, size, and cost. In this letter, we propose a learning-based approach for estimating the mass and center of mass (COM) of unknown objects without using force/torque sensors at the end effector or on the joints. In our method, a robot arm carries an unknown object as it moves through multiple discrete configurations. Measurements are collected when the robot reaches each discrete configuration and stops. A neural network then estimates joint torques from …

Personalized Fashion Outfit Generation With User Coordination Preference Learning, Yujuan Ding, P.Y. Mok, Yunshan Ma, Yi Bin

Research Collection School Of Computing and Information Systems

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users …

Log-Based Anomaly Detection Based On Evt Theory With Feedback, Jinyang Liu, Junjie Huang, Yintong Huo, Zhihan Jiang, Jiazhen Gu, Zhuangbin Chen, Cong Feng, Minzhi Yan, R. Michael Lyu

Research Collection School Of Computing and Information Systems

System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable …

Quantifying Balance: Computational And Learning Frameworks For The Characterization Of Balance In Bipedal Systems, Kubra Akbas

Dissertations

In clinical practice and general healthcare settings, the lack of reliable and objective balance and stability assessment metrics hinders the tracking of patient performance progression during rehabilitation; the assessment of bipedal balance plays a crucial role in understanding stability and falls in humans and other bipeds, while providing clinicians important information regarding rehabilitation outcomes. Bipedal balance has often been examined through kinematic or kinetic quantities, such as the Zero Moment Point and Center of Pressure; however, analyzing balance specifically through the body's Center of Mass (COM) state offers a holistic and easily comprehensible view of balance and stability.

Building upon …

Learning Representations For Effective And Explainable Software Bug Detection And Fixing, Yi Li

Dissertations

Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a 'bridge,' known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning.

This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning …

Fortifying Robustness: Unveiling The Intricacies Of Training And Inference Vulnerabilities In Centralized And Federated Neural Networks, Guanxiong Liu

Dissertations

Neural network (NN) classifiers have gained significant traction in diverse domains such as natural language processing, computer vision, and cybersecurity, owing to their remarkable ability to approximate complex latent distributions from data. Nevertheless, the conventional assumption of an attack-free operating environment has been challenged by the emergence of adversarial examples. These perturbed samples, which are typically imperceptible to human observers, can lead to misclassifications by the NN classifiers. Moreover, recent studies have uncovered the ability of poisoned training data to generate Trojan backdoored classifiers that exhibit misclassification behavior triggered by predefined patterns.

In recent years, significant research efforts have been …

On Explainability Of Neural Networks, Cem Benar

Dissertations

It is widely reported that deep neural networks outperform most competitors for a range of applications. The state-of-the-art neural networks have built-in inductive bias of architectural choices, regularizations, optimizer types, and initialization methods. Using inductive bias is intuitive to enhance the model approximation. Deep neural networks are mostly dense and heavily overparameterized. They tend to be biased towards low-rank solutions to reduce complexity and improve generalization performance, known as implicit regularization. The implicit regularization as observed in specific architectures and various real-world data sets suggests to overparameterize neural networks judiciously and learn compressed representations (lower rank approximation) with improved performance. …

Ai-Supported Academic Advising: Exploring Chatgpt’S Current State And Future Potential Toward Student Empowerment, Daisuke Akiba, Michelle C. Fraboni

Publications and Research

Artificial intelligence (AI), once a phenomenon primarily in the world of science fiction, has evolved rapidly in recent years, steadily infiltrating into our daily lives. ChatGPT, a freely accessible AI-powered large language model designed to generate human-like text responses to users, has been utilized in several areas, such as the healthcare industry, to facilitate interactive dissemination of information and decision-making. Academic advising has been essential in promoting success among university students, particularly those from disadvantaged backgrounds. Unfortunately, however, student advising has been marred with problems, with the availability and accessibility of adequate advising being among the hurdles. The current study …

Artificial Intelligence In Cardiology: An Australian Perspective, Biyanka Jaltotage, Abdul R. Ihdayhid, Nick S. R. Lan, Faraz Pathan, Sanjay Patel, Clare Arnott, Gemma Figtree, Leonard Kritharides, Syed M. S. Islam, Clara K. Chow, James M. Rankin, Stephen J. Nicholls, Girish Dwivedi

Research outputs 2022 to 2026

Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, …

The Library & Generative Ai, Nat Gustafson-Sundell, Mark Mccullough

Library Services Publications

A demonstration of several AI tools, including ChatGPT, ChatPDF, Consensus, and more. The focus of the session is on potential student uses of the tools and related library initiatives, so we address the limits of ChatGPT as an information source. Librarians can help students learn how to use these tools responsibly and provide leadership on campus as AI is integrated into assignments.

Short-Term Vehicle Speed Prediction With Spatiotemporal Convolution Fused With Variational Modal Decomposition, Kai Zhang, Haipeng Lu, Ying Han, Lingyun Zhang, Yujie Ding

Journal of System Simulation

Abstract: Accurate short-term vehicle speed prediction helps to resolve city traffic congestion problems. Focusing on the defect that CNN cannot process non-Euclidean geometric data, GCN and BiLSTM are combined to fully process the spatiotemporal characteristics of road network information, in which the advantages of GCN integrating global features and the ability of BiLSTM to extract temporal features are considered. In order to reduce the interference of noise to the data, variational modal decomposition (VMD) is introduced and short-term vehicle speed prediction model based on VMD-GCN-BiLSTM (VGBLSTM) is proposed . Simulation results show that the prediction accuracy of VGBLSTM model is …

Two New Maneuvering Target Simulation Methods, Yingxuan Li, Zhongxun Wang, Yunlong Dong

Journal of System Simulation

Abstract: To verify the performance of maneuvering target tracking algorithm, it's necessary to build a complex motion simulation model similar to the actual target motion situation. A simulation model of maneuvering target with controllable time correlation coefficient is constructed based on the idea of Singer model, and the suitability of Singer's maneuvering target tracking algorithm is verified when the time correlation coefficient does not match. Aiming at the problem that the traditional coordinated turning model only considers the change of normal acceleration, and the tangential acceleration is always assumed to be 0, which is not highly consistent with the actual …

Lidar Slam Mapping Method Adapted To Environmental Spatial Changes, Songming Jiao, Xin Yao, Hui Ding, Yufei Zhong

Journal of System Simulation

Abstract: In the environment with obvious changes in space size, aiming at the drift and other problems of the existing algorithm, Adp-lio-sam mapping method is proposed to adapt to the environment space changes, and improve the generality of lio-sam algorithm. Point cloud dewarping method is improved, and Kalman filter algorithm is used to carry out the motion compensation data by fusing lidar interframe pose interpolation and IMU interpolation. Fuzzy algorithm is used to adapt different points filtering thresholds for different spatial environments and the constraints of loop closure detection are optimized. Experimental results show that, compared with the existing …

Framing And Intentionality In Artificial Intelligence Poetry, Alyson Elaine Marie Nichols

Student Theses and Dissertations

As natural language generation expands, increasing attention has been paid to neural networks and other machine learning techniques. This study offers a cheaper, simpler approach in the field of poetry generation. The Empath system uses a random-grammar model to constrain creativity with syntactical rules in order to produce novel and valuable poetry after the style of Emily Dickinson. It pays specific attention to using related words to create a sense of coherence and implements a framing system to prove that its creative choices are intentional. A survey of 118 students and teachers at Arkansas State University shows that Empath’s products …

Research On Dynamic Simulation Technology For Satellite Internet, Xiaofeng Wang, Taiqian Shen, Yuan Liu

Journal of System Simulation

Abstract: Network simulation is an important support for new technology verification and security technology evaluation in the rolling construction of satellite internet. A dynamic simulation architecture which based on cloud platform is proposed to address the highly time-varying characteristics of satellite internet. An algorithm of distributed simulation technology of satellite links, which improves the synchronization of accuracy of dynamic simulation of links, is designed to tackle the time-varying of satellite links. Aiming at the topology changes in the process of satellites motion, an algorithm of realtime topology simulation based on time splice is raised, which realizes real-time and accurate changes …

Target Search Planning And Algorithm For Monitoring Of Polar Disaster Areas, Fei Ding, Meinan Zhang, Hengheng Zhuang, Hairong Ma

Journal of System Simulation

Abstract: Aiming at improving the ability of safe navigation route planning and risk assessment of ships in polar waters, a target search model and method based on clustering and efficient indexing of monitoring center are proposed. By constructing a disaster monitoring scenario based on the current navigation area of the ship, a virtual electronic fence is introduced to define the monitoring area. Spectral clustering algorithm is used to divide the risk level of the fence area, extract high-risk areas, and optimize the generation of target search scenarios; Efficient determination of the matching relationship between the target vessel and the fence …

Modeling And Analysis Of Metro Emergency Decision Based On Logical Game Probability Petri Net, Zhe Yan, Wei Liu, Yuyue Du

Journal of System Simulation

Abstract: In order to solve the problem that logical Petri net can not describe dynamic game process well, logical game probabilistic Petri net is proposed. The four elements of the game are integrated into the logical Petri net, and the players of the game are defined as an attribute of Token, for which the strategy set and utility function are defined, and the information database is introduced. Probability change and vector are introduced to represent the transformation relationship of empirical probability in the process of game, and fuzzy theory is introduced on the basis of Bayes formula to solve the …

Two-Stage Robust Optimization-Based Economic Dispatch Of Virtual Power Plants Considering Cogeneration, Jinpeng Liu, Peng Jinchun, Jiaming Deng, Hushihan Liu

Journal of System Simulation

Abstract: With continuous enrichment of resources of the supply side and flexible and changeable load of the demand side of energy system to effectively cope with the complexity of system operation optimization and resource allocation, a robust optimization model of virtual power plant considering the interaction between electric and thermal units is proposed. Considering the uncertainty of renewable energy and load in virtual power plant, a two-stage robust optimization model of min-max-min structure is established, and the optimal operation economy dispatching scheme in the worst scenario is obtained. Robustness coefficient is introduced to flexibly adjust the conservativeness of the optimization …