Autonomous Driving Trajectory Prediction,
2024
University of Nevada, Las Vegas
Autonomous Driving Trajectory Prediction, Carlos Funes
Undergraduate Research Symposium Lightning Talks
Autonomous driving is undoubtedly one of the world's most revolutionary technologies, opening the door to a more secure traffic environment. This innovation has led to vehicles being able to drive by themselves without the necessity of a person behind the wheel, as well as cruise control, lane-keeping assist, and automatic emergency braking. Unfortunately, there is still plenty of work before autonomous driving becomes more popular among drivers. While at UNLV as an undergraduate student/research assistant, one of my goals is to learn how these technologies work to bring ideas into the automotive industry by refining solutions to problems within these …
It's Not As Bad As You Think: Detecting Ai-Generated Voices,
2024
University of Nevada, Las Vegas
It's Not As Bad As You Think: Detecting Ai-Generated Voices, Yong Qin Xu
Undergraduate Research Symposium Lightning Talks
Advances in machine learning have opened up the world to a brand new frontier of fraudulent phone calls which the average person may not be in any way prepared for. From imitations of a loved one's voice to lifelike mimicry of human callers, telephone scams may become harder than ever to anticipate or prevent now that criminals have the help of AI on their side. This is why in my research paper, I aim to analyze and compare two existing methods of detecting the authenticity of human voice recordings in order to demonstrate and explain currently available technology that's capable …
Vision-Language Integration For Enhanced Locomotion Mode Prediction,
2024
Louisiana State University and Agricultural and Mechanical College
Vision-Language Integration For Enhanced Locomotion Mode Prediction, Ehsan Ahmadi
LSU Master's Theses
Wearable exoskeletons offer significant potential in enhancing human mobility in industrial environments. However, their adaptability to dynamic, task-intensive settings presents challenges, especially in accurately predicting locomotion modes such as ladder climbing, stair navigation, low-space movement, and obstacle navigation. This research proposes a multimodal framework that integrates visual data and speech commands to improve locomotion mode prediction in unpredictable environments. Multimodal data was collected using smart glasses, capturing both the user’s perspective (field-of-view, FOV) and voice during locomotion tasks. State-of-the-art models—CLIP, ImageBind, and GPT-4o—process these visual and linguistic inputs to predict locomotion activities. The models were evaluated in zero-shot and fine-tuned …
Ethical Risks And Challenges Of Chatgpt Applications In Education,
2024
School of Government, University of International Business and Economics, Beijing 100029, China
Ethical Risks And Challenges Of Chatgpt Applications In Education, Jingbo Fan, Hui Liang
Bulletin of Chinese Academy of Sciences (Chinese Version)
ChatGPT is a typical application in the field of natural language processing, with the potential to empower and revolutionize education. It can serve not only as a digital tutor for students but also as a virtual assistant for teachers, driving the transformation of student learning methods and teaching paradigms. Additionally, ChatGPT shows a wide range of applications in the research field. However, while bringing opportunities for educational development, ChatGPT also poses ethical risks and challenges to educational equity. Firstly, ChatGPT may exacerbate the digital divide, leading to unequal educational opportunities. Secondly, it presents risks such as knowledge alienation, algorithmic black-box …
Artificial Intelligence Foundation Model Risk Identification And Governance Model From Esg Perspective,
2024
AI Safety and Trustworthy Center, Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Artificial Intelligence Foundation Model Risk Identification And Governance Model From Esg Perspective, Jincheng Shi, Guoyu Wang, Yingchun Wang
Bulletin of Chinese Academy of Sciences (Chinese Version)
The application ecology of artificial intelligence foundation model is rapidly expanding. The environment, society, and governance are facing new challenges and opportunities. Exploring the construction of a governance framework for the development risks of foundation model has important theoretical value and practical significance for promoting the healthy and sustainable development of artificial intelligence. Based on the theories of ESG and artificial intelligence governance, this study analyzes the development benefits and typical risks of foundation model from the perspective of ESG and then constructs a risk governance framework and implementation strategies for artificial intelligence foundation models. This study shows that a …
Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic,
2024
Department of Sociology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Chinese Association of Development Strategy Studies, Beijing 100190, China
Participatory Ethical Regulations: Risk Challenges Of Artificial Intelligence Era And Construction Of Governance Logic, Chenggang Zhang, Lu Pan
Bulletin of Chinese Academy of Sciences (Chinese Version)
Participatory ethical norms emphasize the involvement of diverse stakeholders, aiming to construct a more comprehensive and balanced ethical governance framework. The rapid development of artificial intelligence (AI) technology is leading society through unprecedented transformations, significantly impacting ethical perspectives, social governance models, and the symbiotic relationship between humans and technology. The participatory ethical norms, characterized by multi-stakeholder participation, interactivity, and openness, represent a crucial pathway for addressing the challenges posed by the rapid development of AI technology. Constructing an AI governance framework based on participatory ethical norms provides solutions for the sustainable, fair, and transparent development of AI from multiple aspects …
Overview On Autonomous Machine Computing,
2024
Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518129, China
Overview On Autonomous Machine Computing, Shaoshan Liu, Yiming Gan, Yinhe Han
Bulletin of Chinese Academy of Sciences (Chinese Version)
Autonomous machine computing, an innovative blend of algorithms, software, and cutting-edge computing hardware, is poised to be the next major paradigm shift in the global economy, following personal, mobile, and cloud computing. This study delves into the research and commercialization of the robotics industry, underscoring the critical importance of establishing a comprehensive autonomous machine computing ecosystem. This study argues that autonomous machine computing necessitates a complete ecosystem that encompasses applications, programming languages, and the foundational hardware architectures, and presents a comprehensive review of significant research contributions across these areas. Moreover, the study explores the synergy between autonomous machine computing and …
Enlightenment Of Us Nairr To Construction Of Artificial Intelligence Innovation Ecosystem In China,
2024
National Science Library, Chinese Academy of Sciences, Beijing 100190, China
Enlightenment Of Us Nairr To Construction Of Artificial Intelligence Innovation Ecosystem In China, Tian Jiang, Li Qian
Bulletin of Chinese Academy of Sciences (Chinese Version)
The National Artificial Intelligence Research Resource (NAIRR) of the United States is of great significance for addressing the new challenges faced by the innovation of artificial intelligence technology. For China, the experience of NAIRR provides valuable references in resource optimization allocation and efficient utilization, which helps us to overcome resource bottlenecks and promote the rapid development of artificial intelligence technology. This study analyzes the enlightenment of NAIRR to the innovation and development of artificial intelligence in China from two key aspects. The first is the innovation-driven elements, where the integration strategies of NAIRR in computing and storage power, data resource, …
Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation,
2024
Kennesaw State University
Dynamic Knowledge Elicitation: Leveraging Student Feedback For Improved Language Model Distillation, Reuven Muller
Master's Theses
Large Language Models (LLMs) have significantly advanced the field of natural language processing but remain resource-intensive and impractical for many organizations. Specialist models offer a viable alternative, often developed through Knowledge Distillation (KD) techniques. However, traditional KD methods rely on predefined static datasets to elicit knowledge from the teacher model, failing to dynamically address the weaknesses of the student model during training. This research introduces two novel methods for adaptive knowledge elicitation: Feedback-Driven Question Generation and Agent-Based Targeted Question Generation. These methods iteratively expand the training dataset based on the student model’s performance, leveraging a teacher model to generate targeted …
First Amendment Roadblock? Regulating The Misuse Of Generative Ai Technologies: Impersonation And Appropriation Of Likeness Without Permission,
2024
Old Dominion University
First Amendment Roadblock? Regulating The Misuse Of Generative Ai Technologies: Impersonation And Appropriation Of Likeness Without Permission, Muhammad Rabiu
Cybersecurity Undergraduate Research Showcase
This paper initially explores the misuse of generative AI technologies, particularly their role in impersonating or appropriating individuals' likeness without consent. It will then analyze technical and legal mitigation strategies and propose recommendations to address this issue in light of the First Amendment’s constitutional Freedom of Speech provision.
Between Copyright And Computer Science: The Law And Ethics Of Generative Ai,
2024
Georgia Institute of Technology
Between Copyright And Computer Science: The Law And Ethics Of Generative Ai, Devin R. Desai, Mark Riedl
Northwestern Journal of Technology and Intellectual Property
Copyright and computer science continue to intersect and clash, but they can coexist. The advent of new technologies such as digitization of visual and aural creations, sharing technologies, search engines, social media offerings, and more, challenge copyright-based industries and reopen questions about the reach of copyright law. Breakthroughs in artificial intelligence research, especially Large Language Models that leverage copyrighted material as part of training, are the latest examples of the ongoing tension between copyright and computer science. The exuberance, rush-to-market, and edge problem cases created by a few misguided companies now raises challenges to core legal doctrines and may shift …
Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing,
2024
Louisiana Tech University
Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed
Master's Theses
In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …
Robotic Multi-Object Grasping From A Pile: Techniques And Algorithms For Enhanced Dexterity,
2024
University of South Florida
Robotic Multi-Object Grasping From A Pile: Techniques And Algorithms For Enhanced Dexterity, Tianze Chen
USF Tampa Graduate Theses and Dissertations
As robots become increasingly integrated into real-world applications such as warehousing, fulfillment centers, and manufacturing, the need for efficient and adaptable robotic systems grows. One of the key challenges is enabling robots to grasp multiple objects simultaneously, as this significantly boosts the efficiency of tasks like batch picking, sorting, and object transferring, reducing both time and energy consumption. This dissertation presents a comprehensive multi-object grasping (MOG) pipeline that includes pre-grasp selection, end-pose selection, grasping synergy calculation, and a data-driven model for estimating the number of objects being grasped. Central to this work is the development of the Experience Forest structure, …
Exploring Factors That Influence Artificial Intelligence Adoption In Banks And Credit Unions,
2024
University of South Florida
Exploring Factors That Influence Artificial Intelligence Adoption In Banks And Credit Unions, Vijaya S. Tumma
USF Tampa Graduate Theses and Dissertations
The importance of Artificial Intelligence (AI) is exploding in the banking sector, fueled by enhanced productivity, improved efficiencies, and personalized services to the consumers. For credit unions, the adoption of AI technologies presents opportunities and challenges. This research explores the factors influencing AI adoption in the banking sector through the lens of Unified Theory of Acceptance and Use of Technology (UTAUT) framework. This study aims to explore the influence of key aspects of UTAUT model, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), and Facilitating Conditions (FC) on the intention of AI adoption among credit unions, banks, and their …
Intersections Of Living And Machine Agencies: Art-Based Models Of Adaptive Conversation With The More-Than-Human World,
2024
Rochester Institute of Technology
Intersections Of Living And Machine Agencies: Art-Based Models Of Adaptive Conversation With The More-Than-Human World, Carlos Castellanos
Tradition Innovations in Arts, Design, and Media Higher Education
Today’s AI systems are not built to have reciprocal interplay with their environments and thus they demonstrate little interest in emergence, adaptation or developing mutually productive relationships with the natural world. Is a different kind of AI possible? How can artists contribute to its development? In this essay, I will discuss ways in which the arts might help guide us towards a new kind of AI, built upon adaptive conversation (i.e. shared construction of meaning) with nature. I will discuss how can work with AI while also challenging its prevailing ontology, and even suggest alternative ontologies and epistemologies. I will …
Platform Path Optimization Method Based On Cumulative Detection Probability Of Sonar Search,
2024
PLA Naval Submarine Academy, Qingdao 266000, China
Platform Path Optimization Method Based On Cumulative Detection Probability Of Sonar Search, Xiang Wei, Xingxuan Liu, Dianzheng Fu, Tianji Yang, Jiaxuan Yang
Journal of System Simulation
Abstract: To address the lack of research on the optimal path of mobile search platform to search for moving targets, this paper proposes a path optimization method of mobile search platform based on cumulative search probability theory. Based on the cumulative detection probability (CDP), one of the important criteria of sensor performance evaluation, a single-peak CDP calculation formula is constructed by using a time series correlation model, namely the (λ, σ) process model. A set of target motion scenarios are constructed, and the trajectory probability of target scenarios and their CDP at different time are corrected by Bayesian posterior probability. …
Modeling For Decision Support Of Flight Ground Support Process,
2024
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
Modeling For Decision Support Of Flight Ground Support Process, Zhiwei Xing, Ruiwen Yu, Biao Li, Zhaoxin Chen
Journal of System Simulation
Abstract: Aiming at the problems of insufficient decision-making ability and low operational efficiency of the flight ground support process, a decision support model of the flight ground support process based on the department of defense architecture framework (DoDAF) is proposed. Starting from the support operation, support resources, and the relationship between them, the quantitative description of the flight ground support process is performed. DoDAF and the model-based systems engineering (MBSE) modeling method are combined to establish a decision support model of the flight ground support process. The decision utility function is established to analyze the utility value of the comprehensive …
End-To-End Motion Planning Of Unmanned Vehicles Based On Multimodal Deep Reinforcement Learning,
2024
School of Computer Science and Technology, Xinjiang University, Urumqi 830017, China; Xinjiang Key Laboratory of Signal Detection and Processing, Urumqi 830017, China
End-To-End Motion Planning Of Unmanned Vehicles Based On Multimodal Deep Reinforcement Learning, Kaiyuan Ding, Askar Hamdulla, Bin Zhu, Eksan Firkat, Zhengtang Ma
Journal of System Simulation
Abstract: Since the agent cannot sense the surrounding environment and cannot successfully avoid obstacles, reinforcement learning fails to be generalized to robot motion planning in difficult terrain. Therefore, a solution based on multimodal deep reinforcement learning, which learns to blend proprioceptive states with high-dimensional depth sensor inputs, is proposed for the motion planning of unmanned vehicles. To be specific, proprioceptive states offer contact measurement for immediate reaction, and the unmanned vehicle can learn and forecast environmental changes with its attached visual sensors, proactively navigating around obstacles and uneven terrains numerous time steps ahead. TransProAct (transformer-based proactive action), a unique end-to-end …
Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm,
2024
School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471000, China; Henan Collaborative Innovation Center for Advanced Manufacturing of Mechanical Equipment, Luoyang 471000, China
Flexible Job Shop Scheduling Method Based On Collaborative Agent Reinforcement Learning Algorithm, Jian Li, Huankun Li, Pengbo He, Huabei Wang, Liping Xu, Kui He
Journal of System Simulation
Abstract: To enhance the efficiency of flexible job shop scheduling, this paper develops a Markov decision process with specific constraints tailored to the scheduling problem. A cooperative agent reinforcement learning method is proposed to solve the problem of concurrent selection of workpieces and machines. During the construction of the Markov decision process, a disjunctive graph is introduced to represent the state characteristics. Two agents are introduced to select the workpieces and machines. The reward parameters governing the entire scheduling process are established by predicting variations in the minimum-maximum completion time across different time points. A GIN(graph isomorphic network) graph neural …
Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components,
2024
School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, China; Jiangsu Engineering Research Center on Meteorological Energy Using and Control, Nanjing 210044, China
Dual-Resource Constrained Distributed Flexible Scheduling For Aerospace Structural Components, Yufang Wang, Dianqing Zhang, Xiaolin Hua, Binbin Yao, Fan Chen
Journal of System Simulation
Abstract: A dual-resource constrained distributed flexible job-shop scheduling model was established by taking into account the worker constraints of the finishing process and the requirements of distributed multi-factory collaboration in the production of aerospace structural components. A hybrid grey wolf optimization algorithm based on the critical factory was proposed to solve this problem. The model contained four subproblems: factory selection, operation sequencing, machine selection, and worker selection. In view of these four sub-problems, a four-layer coding and a new decoding method were designed to avoid the use conflict of machines and workers. In addition, a new mechanism for hunting and …
