Artificial Intelligence and Robotics Commons^™

Optimal Trajectory Of Full-Duplex Uav Relaying Over Hybrid Probability Channels, Tao Wang, Ji Xiaodong

Journal of System Simulation

Abstract: A fixed-wing UAV being the full-duplex moving relay, and a hybrid probability channel being the source to send data to the destination, through the flight optimal trajectory design. On the basis of ensuring the total data amount of source-destination communication, the energy consumption of the system is minimized. Two optimization problems of runway shape and mixed trajectory are established, which are non-convex and are difficult to get the closed-form solution. The hybrid probability channels gains are replaced by the average channel gains, and the lower bounds of the received data at the UAV and the destination are calculated by …

Arterial Coordination Optimization Method Based On Vehicle Speed Guidance And Inductive Control, Mingjun Deng, Xinxia Hu, Xiang Li, Liping Xu

Journal of System Simulation

Abstract: Arterial signal coordination is usually based on fixed belt speeds and time-of-day statistical flows. Actually, vehicle speeds and traffic flows are fluctuating, which causes to the mismatch between the signal scheme and the actual optimal belt speeds and traffic flow demands, and affects the intersection's traffic efficiency. Based on the vehicle infrastructure cooperation, by applying Maxband model and the maximum green wave bandwidth, the minimum number of arterial vehicle delays, arterial stops and the minor direction delays being the optimization objectives, a multi-objective optimization model for arterial signal coordination is established. Through using an improved multi-objective particle swarm algorithm …

Curriculum Learning-Based Simulation Of Uav Air Combat Under Sparse Rewards, Jingyu Zhu, Hongli Zhang, Minchi Kuang, Heng Shi, Jihong Zhu, Zhi Qiao, Wenqing Zhou

Journal of System Simulation

Abstract: To address the limited exploration capabilities and sparse rewards of conventional reinforcement learning methods in air combat environment, a curriculum learning distributed proximal policy optimization (CLDPPO) reinforcement learning algorithm is proposed. A reward function informed by professional empirical knowledge is integrated, a discrete action space is developed, and a global observation and local value and decision network featuring separated global and local observations is established. A methodology for unmanned aerial vehicles UAVs is presented to acquire combat expertise through a sequence of fundamental courses that progressively intensify in their offensive, defensive, and comprehensive content. The experimental results show that …

Hybrid Flow Shop Scheduling With Limited Buffers Considering Energy Consumption And Transportation, Tingxin Wen, Tingyu Guan

Journal of System Simulation

Abstract: Aiming at the untimely production scheduling and excessive energy consumption during processing, a limited buffer hybrid flow shop scheduling optimization model is constructed. To minimize the makespan and total energy consumption of the workshop, the transport time, generalized energy consumption and buffer capacity being the constraints, and the on/off energy saving strategy applied to reduce the standby energy consumption, the feasibility of the optimization model are verified. A lion swarm optimization algorithm is designed, in which a population initialization method combining random generation and greedy selection is used to improve the initial solution quality and solution efficiency, the lion …

Development And Application Of Simulation Platform For Aquatic Movement Of An Amphibious Armored Vehicle, Mingzhe Chen, Yunzheng Song, Pei Wang, Lei Zhang

Journal of System Simulation

Abstract: In order to design and verify the fire control system(FCS) algorithm of amphibious assault vehicle under heavy wind and wave conditions, a real-time simulation platform is developed. The traditional single rigid body dynamic model can't describe the body-turret-barrel dynamic coupling relationship and it is not suitable for FCS simulation with high dynamic characteristics. Twist-wrench method is used to establish the multiple rigid body dynamic model of vehicle, the buoyancy and the hydrodynamic calculation is carried out according to the body and the moving relationship between visual generated waves, and the hydrodynamic coefficient is obtained by the computational fluid dynamic …

Application Of Driving Simulation Technology In Calibration Of Traffic Simulation Parameters, Shikun Liu, Yi Tang, Yonghong Liu

Journal of System Simulation

Abstract: To address the insufficient accuracy in traffic simulation modeling due to the lack of in-depth consideration of complex driving behaviors, a calibration method for traffic simulation parameters based on driving simulation technology is proposed. The reconstruction and expansion project of Shenzhen Bao'an International Airport Expressway is selected as the case. Using VISSIM simulation software, a comprehensive traffic simulation model of the entire route is constructed, and UC-winRoad software is employed to create the highly realistic driving simulation scenarios. Driving simulation experiments are conducted to extract the typical driving behavior characteristics in complex scenarios. Calibration functions for simulation parameters are …

How Do Preservice Teachers Learn To Teach Integrated Computational Thinking?: Evidence From Planning, Enactment, And Reflection, Rachael Dektor, Samuel Severance, Kip Téllez

Journal of Computer Science Integration

This study examines pre-service teachers’ (PSTs) beliefs and understandings about computational thinking (CT) integration and lesson implementation over time. Utilizing a design-based research approach, 3 PSTs led the co-design of integrated CT lessons with support from researchers and enacted these CT integrated lessons with K-5 students. All PSTs participated in a whole-group CT workshop and engaged in one-on-one lesson design sessions with a researcher. We utilized a grounded theory approach to qualitatively analyze pre-surveys, semi-structured interviews, and video data of three PSTs enacting their lessons. We found that PSTs’ initial beliefs about CT instruction – including the importance of it …

Addressing Social Inequalities Using Ai, Big Data, And Machine Learning, Erica L. Jensen, Lakell Archer, Sumaya Ali

Journal of Nonprofit Innovation

No abstract provided.

From Graph Theory For Robust Deep Networks To Graph Learning For Multimodal Cancer Analysis, Asim Waqas

USF Tampa Graduate Theses and Dissertations

This dissertation explores the intersection of graph theory and deep learning, focusing on enhancing the robustness of deep neural networks (DNNs) and applying these advancements to complex problems like cancer diagnosis and treatment. We investigate the structural properties of graphs and their influence on neural network performance, particularly in multimodal learning. The work delves into the design space of DNN architectures using graph-theoretic measures, transforming graphs into DNN architectures for various tasks, and examining their robustness against noise and adversarial attacks. The study extends to medical imaging, highlighting advanced DNN architectures like U-Net for brain tumor segmentation. It addresses the …

Optimizing Cybersecurity Operations Using Data-Driven Intelligence, Jalal Ghadermazi

USF Tampa Graduate Theses and Dissertations

Cybersecurity operations centers (CSOCs) play a crucial role in safeguarding organizations from cyber threats. CSOC operations are divided into two main areas: Intrusion detection systems (IDS) and security response team (SRT) operations. Machine learning (ML) and deep learning (DL) advancements have significantly improved IDSs. IDS can be either flow-based, suitable for offline analysis, or packet-based, which analyze traffic in real-time. However, packet-based IDS often treat packets independently, ignoring the sequential nature of network communication. Additionally, recent ML/DL approaches also struggle with capturing global and structural information and novel attack detection due to their reliance on labeled data. The SRT within …

Anonymized Identity Recognition And Classification Using Privacy Preserving Facial Encoding, Manas Sanjay Pakalapati

USF Tampa Graduate Theses and Dissertations

The need for sharing large-scale datasets to train deep neural network models, particularly in healthcare, raises significant data security and privacy concerns. To address these issues, methods such as data encryption or encoding are utilized. These techniques can encrypt the data and make it unreadable to humans, while still retaining its usefulness for training models.

In this study, we investigate various image encoding techniques designed to protect privacy by making images unrecognizable while still retaining their usefulness for model training. Our investigation utilized publicly available facial databases and focused on evaluating the trade-offs inherent in image encoding techniques, with a …

Do You Say Please Or Thank You To Chatgpt? The Subtle Influence Of Prompt Engineering On Digital Civility, Essraa Nawar

Library Articles and Research

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In the dynamic and rapidly evolving landscape of artificial intelligence, tools like ChatGPT have become integral to our daily digital interactions. These advanced AI systems assist us with a myriad of tasks, from generating text and answering complex questions to providing creative solutions. However, as we engage more frequently with these non-human entities, an intriguing question arises: Are we inadvertently becoming ruder in our digital communications, or are we consciously maintaining our ingrained habits of politeness?"

Large-Scale Research Infrastructure Empowers High-Quality Development Of Private Enterprises: Current Situation, Challenges, And Policy Recommendations, Lingling Zhang, Fuqiang Wang, Mingze Zhang, Zexia Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

Private enterprises have become an important source of innovation in China’s economic development. Large-scale research infrastructure, as a crucial strategic support and innovation element for breaking through key technologies, provides a platform for the innovative development of private enterprises. At present, some large-scale research infrastructures in China, such as the China Spallation Neutron Source and the Shanghai Synchrotron Radiation Facility, have begun actively exploring mechanisms to serve private enterprises. These efforts have helped a number of private companies overcome critical technological bottlenecks and achieve original innovations. Nevertheless, in the current process of opening up large-scale research infrastructures to private enterprises …

Context-Driven: Logic And Pathway Of Industrial Intelligence To Accelerate Development Of New Quality Productive Forces, Ximing Yin, Yaxin Su, Tailun Chen, Jin Chen, Jiang Yu

Bulletin of Chinese Academy of Sciences (Chinese Version)

The development of new productive forces hinges on industrial intelligence, which is driven by the deep integration of technological innovation and industrial innovation, and the accelerated construction of a modern industrial system. Industrial intelligence refers to the process of transforming traditional industrial development models and cultivating new pillar industries through the empowerment of intelligent technologies and data elements. It serves as a pivotal engine for promoting new industrialization and developing new productive forces. However, existing research has largely overlooked the critical theoretical and practical issues of how to leverage China’s ultra-large market and its vast contextual advantages to improve the …

Integration Of Digital And Real Economies To Shape New Advantages In Development: New Form Of Human-Cyber-Physical Ternary Fusion, Jiaofeng Pan, Jing Wu

Bulletin of Chinese Academy of Sciences (Chinese Version)

Facing the accelerating of scientific and technological revolution and industrial transformation, expediting the deep integration of digital economy and real economy is a crucial pathway to enhance industrial competitiveness and address social development challenges. The integration of digital and real economies, with the linkage of human-cyber-physical ternary fusion, brings about an increase in production factors and a reduction in uncertainty. This integration permeates through the whole process and channels of the real economy, including R&D innovation, manufacturing, collaborative integration, and supply services, triggering systemic transformations in the real economy. Looking to the future, the deep connection of technology, data, and …

Building Engineering Ecology For Industrial Change In Digital Age, Zhengzhong Xu, Jian Chan

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the digital civilization era, scientific and technological innovation has emerged as a crucial pathway to unleash new quality productive forces and spearhead the ascendancy of great powers. It has become a vital pillar for major nations to engage in international competition and reshape the global order. Furthermore, it acts as a key instrument to smooth out economic cycles and overcome the limitations imposed by these cycles. The rise model, marked by advanced scientific and technological innovation and a commitment to scientific self-sufficiency and enhancement, significantly boosts the viability and acceptance of China’s approach to international governance. At the same …

Study On Data Mining Of Hydrogen Energy Policy In China Based On Natural Language Processing Technology, Dongling Huang, Yuan Liu, Xiaoshuai Yuan, Guozhong Jin, Yuanhang Cai, Li Liu, Heng Cao, Wanjun Li, Rui Cai

Bulletin of Chinese Academy of Sciences (Chinese Version)

Report to the 20th National Congress of the CPC emphasized the importance of “working actively and prudently towards the goals of reaching peak carbon emissions and carbon neutrality”, as well as “speeding up the planning and development of a system for new energy sources”. As a green and low-carbon secondary energy source, hydrogen energy has multiple applications in promoting the large-scale and efficient use of renewable energy as well as energy substitution in the field of transportation. It can also accelerate decarbonization in industry, and as such, is an indispensable part of building a new energy system, reaching peak carbon …

A Pathway For Digital Economy To Enable The Development Of Low-Carbon Transition Under “Technology-Organization-Environment” Framework, Wendong Wei, Yang Sun, Bei Liu, Hui Wang, Yong Geng

Bulletin of Chinese Academy of Sciences (Chinese Version)

The booming development of the digital economy has provided robust technical, management, and institutional means for the in-depth promotion of low-carbon transition. However, at present, there are still problems, such as the limited level of digital technological innovation, the insufficient supply of digital talents, and the urgent need for a sound digital governance system, which constrains the empowering effect of the digital economy on the development of China’s low-carbon transition. Based on the Technology-Organization-Environment (TOE) framework, this study discusses the technology iteration, management transform, and system optimization of the influence of digital economy on the development of low carbon transformation. …

Comparative Analysis And Insights Into R&D Mode Of Top Artificial Intelligence Companies In China And The Us, Xiyi Yang, Jia Jia, Xiaoyu Zhou, Shouyang Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

Artificial intelligence (AI) is currently one of the most prominent fields in the technology industry, with China and the US being two global centers for AI research and development. However, the two countries differ in their development levels of the AI industry. In particular, the emergence of ChatGPT in 2022 has sparked extensive discussions regarding the capabilities and competitiveness of Chinese AI companies. This study analyzes over 120 000 AI invention patents approved in the past five years in both China and the US. Firstly, it constructs a multidimensional index based on AI patent features to identify the top 10 …

Reinforcement Learning For Robotic Tasks: Analyzing And Understanding The Learning Process Using Explainable Artificial Intelligence Methods, Brian J. Campana

Theses and Dissertations

As deep reinforcement learning (RL) models gain traction across more industries, there is a growing need for reliable agent-explanation techniques to understand these models. Researchers have developed explainable artificial intelligence (XAI) methods to help understand these 'black boxes'. While these models have been tested on many supervised learning tasks, there is a lack of examination of how these well these methods can explain hard reinforcement learning problems like robotic control. The sequential nature of learning RL policies and testing episodes create fundamentally different policies over time compared to more traditional supervised learning models. In this thesis, two important questions are …

Liability For Use Of Artificial Intelligence In Medicine, Nicholson W. Price Ii, Sara Gerke, I. Glenn Cohen

Book Chapters

While artificial intelligence (AI) has substantial potential to improve medical practice, errors will certainly occur, sometimes resulting in injury. Who will be liable? Questions of liability for AI-related injury raise not only immediate concerns for potentially liable parties but also broader systemic questions about how AI will be developed and adopted. The landscape of liability is complex, involving healthcare providers and institutions and the developers of AI systems. In this chapter, we consider these three principal loci of liability. At the outset, we note a few issues that shape our analysis.

Scaling Expertise: A Note On Homophily In Online Discourse And Content Moderation, Dylan Weber

New England Journal of Public Policy

It is now empirically clear that the structure of online discourse tends toward homophily; users strongly prefer to interact with content and other users that are similar to them. I review the evidence for the ubiquity of homophily in discourse and highlight some of its worst effects including narrowed information landscape for users and increased spread of misinformation. I then discuss the current state of moderation frameworks at large social media platforms and how they are ill-equipped to deal with structural trends in discourse such as homophily. Finally, I sketch a moderation framework based on a principal of “scaling expertise” …

Henna Chatbot Capstone Review, Kobe Norcross

University Honors Theses

This thesis reviews the development of the Henna Chatbot, an AI-powered DEI consultant designed to provide personalized feedback to organizations. Sponsored by DEI consultant Arsh Haque, the project aims to address gaps in current DEI software, which often lacks team-specific feedback. The Henna Chatbot leverages GPT-3.5 Turbo to create an affordable SaaS platform where organizations can train Henna with their DEI values, and Henna will help organizations stay aligned with those values. The project spanned twenty weeks and was completed by a team of eight computer science students at Portland State University. The development process followed Agile methodologies, emphasizing effective …

Bridging Design And Perception: Novel Tools And Technologies For Creating Effective Human-Robot Interactions, Benjamin Dossett

Electronic Theses and Dissertations

This thesis explores human perception of robots through the use of novel tools and technologies. First, the impact of Augmented Reality (AR) data presentation on human perception of robots is investigated. A study conducted with the AR human-robot teaming system found that robot performance significantly influenced participants’ perceptions, overshadowing the impact of matching or mismatching robot confidence feedback. Second, the DU Want to Build-A-Bot platform is presented, which enables participatory robot design and opens the door for novel research of how robot design affects human perception. The Build-A-Bot platform enables the collection of diverse robot designs, facilitating machine learning analysis …

Data Visualization, Licensing, And Other Generative Ai Initiatives At Minnesota State University Mankato, Evan Rusch, Nat Gustafson-Sundell

Library Services Publications

At Minnesota State University Mankato (MNSU), we’ve undertaken several experiments and initiatives focused on Generative Artificial Intelligence. At the start of the fall semester, we collaborated with university Information Technology Services to present a professional development session for returning faculty through the MNSU Center for Excellence in Teaching & Learning on “5 Tips for Teaching with AI.” We also presented to librarians across the regional consortium, Minitex, on “The Library & Generative AI.” This presentation included several demonstrations. It was offered as an introduction to Generative AI focused on topics most relevant to librarians, including information literacy, as well as …

Machine Learning Multimodal Framework For Fake News Detection And Mitigation, Nada A. Gaballah

Theses and Dissertations

Social media has become our new reality, people wake up every morning and the first thing they do before getting out of bed, is check their social media. Nowadays, people rarely read newspapers, they even rarely watch TV news or listen to radio broadcasts. In recent years, we have witnessed lots of fake news roaming social media every second, with people simply believing it and spreading it even more without checking the credibility of this news. This fake news affected several domains like what happened in the US election in 2016 and again in 2020, the false information about Covid-19 …

Achieving Domain-Independent Certified Robustness Via Knowledge Continuity, Alan Wenyuan Sun

Computer Science Senior Theses

We present knowledge continuity, a novel definition inspired by Lipschitz continuity which aims to certify the robustness of neural networks across input domains (such as continuous and discrete domains in vision and language, respectively). Most existing approaches that seek to certify robustness, especially Lipschitz continuity, lie within the continuous domain with norm and distribution-dependent guarantees. In contrast, our proposed definition yields certification guarantees that depend only on the loss function and the intermediate learned metric spaces of the neural network. These bounds are independent of domain modality, norms, and distribution. We further demonstrate that the expressiveness of a model …

Artificial Intelligence As The Next Front In The Class War, Christopher Hill

Dissertations and Theses

For many years, artificial intelligence has been confined to the realm of science fiction, and while the technology has been in development, predicting the effects AI will have on our society has been a challenging endeavor. The release of ChatGPT in 2022, the subsequent mass adoption of the AI chatbot, and the response by other private firms in the field announced AI's permanent entrance into the public sphere. These recent strides made in the field of artificial intelligence reveal that the pace of technological development has outstripped the rate at which we are able to politically examine and understand these …

A Meta-Ensemble Predictive Model For The Risk Of Lung Cancer, Sideeqoh Oluwaseun Olawale-Shosanya, Olayinka Olufunmilayo Olusanya, Adeyemi Omotayo Joseph, Kabir Oluwatobi Idowu, Oyelade Babatunde Eriwa, Adedeji Oladimeji Adebare, Morufat Adebola Usman

Al-Bahir

The lungs play a vital role in supplying oxygen to every cell, filtering air to prevent harmful substances, and supporting defense mechanisms. However, they remain susceptible to the risk of diseases such as infections, inflammation, and cancer that affect the lungs. Meta-ensemble techniques are prominent methods used in machine learning to enhance the accuracy of classifier learning systems in making predictions. This work proposes a robust predictive model using a meta-ensemble method to identify high-risk individuals with lung cancer, thereby taking early action to prevent long-term problems benchmarked upon the Kaggle Machine Learning practitioners' Lung Cancer Dataset. Three machine learning …

Ai's Ethical Frontier

DePaul Magazine

Artificial intelligence (AI) is affecting every aspect of the university and society. Experts from across DePaul share their insights on artificial intelligence's advantages and pitfalls. Learn about DePaul's new Artificial Intelligence Institute and research projects that use AI for societal benefit.