Computer Sciences Commons^™

A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington

Theses and Dissertations

In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …

Improving Automatic Transcription Using Natural Language Processing, Anna Kiefer

Master's Theses

Digital Democracy is a CalMatters and California Polytechnic State University initia-
tive to promote transparency in state government by increasing access to the Califor-
nia legislature. While Digital Democracy is made up of many resources, one founda-
tional step of the project is obtaining accurate, timely transcripts of California Senate
and Assembly hearings. The information extracted from these transcripts provides
crucial data for subsequent steps in the pipeline. In the context of Digital Democracy,
upleveling is when humans verify, correct, and annotate the transcript results after
the legislative hearings have been automatically transcribed. The upleveling process
is done with the …

Artificial Intelligence And/Or Machine Learning (Ai &| Ml), George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

These slides are from an invited panel presentation at my home institution, Loyola University Chicago, organized by the Loyola University Chicago Retiree Association (LUCRA). I was asked to give a broad historical overview of AI and ML and speak about its societal impacts.

"The Loyola University Chicago Retiree Association embraces the Vision of Loyola University Chicago and will assist students, faculty, and administrators as they strive to serve humanity. The group values freedom of inquiry, the pursuit of truth, and care of others and embraces a commitment to excellence, service that promotes social justice, values based leadership, and global awareness."

Generative Ai In Finance: Risks And Potential Solutions, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

Generative Artificial Intelligence captured the attention of academics, policymakers, the private sector, and some regulators in 2022 after the launch of ChatGPT and its widespread adoption worldwide. Then, during the World Economic Forum session in 2023, Microsoft Chairman and Chief Executive Officer Satya Nadella said that the ‘golden age’ of AI is underway and generative AI is set to play a big role in it. Accordingly, Microsoft has invested billions of dollars into OpenAI, the company behind the launch of ChatGPT. Additionally, Google and Meta have also created their own generative AI models. Given the multiplicity of potential use cases …

Math Word Problem Generation Via Disentangled Memory Retrieval, Wei Qin, Xiaowei Wang, Zhenzhen Hu, Lei Wang, Yunshi Lan, Richang Hong

Research Collection Lee Kong Chian School Of Business

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as …

Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Research Collection School Of Computing and Information Systems

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …

Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability. Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated …

Conditional Neural Heuristic For Multiobjective Vehicle Routing Problems, Mingfeng Fan, Yaoxin Wu, Zhiguang Cao, Wen Song, Guillaume Sartoretti, Huan Liu, Guohua Wu

Research Collection School Of Computing and Information Systems

Existing neural heuristics for multiobjective vehicle routing problems (MOVRPs) are primarily conditioned on instance context, which failed to appropriately exploit preference and problem size, thus holding back the performance. To thoroughly unleash the potential, we propose a novel conditional neural heuristic (CNH) that fully leverages the instance context, preference, and size with an encoder–decoder structured policy network. Particularly, in our CNH, we design a dual-attention-based encoder to relate preferences and instance contexts, so as to better capture their joint effect on approximating the exact Pareto front (PF). We also design a size-aware decoder based on the sinusoidal encoding to explicitly …

Temporal Implicit Multimodal Networks For Investment And Risk Management, Meng Kiat Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a …

T-Sciq: Teaching Multimodal Chain-Of-Thought Reasoning Via Large Language Model Signals For Science Question Answering, Lei Wang, Yi Hu, Jiabang He, Xing Xu, Ning Liu, Hui Liu, Heng Tao Shen

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently demonstrated exceptional performance in various Natural Language Processing (NLP) tasks. They have also shown the ability to perform chain-of-thought (CoT) reasoning to solve complex problems. Recent studies have explored CoT reasoning in complex multimodal scenarios, such as the science question answering task, by fine-tuning multimodal models with high-quality human-annotated CoT rationales. However, collecting high-quality COT rationales is usually time-consuming and costly. Besides, the annotated rationales are hardly accurate due to the external essential information missed. To address these issues, we propose a novel method termed T-SciQ that aims at teaching science question answering with …

Path-Bigbird: An Ai-Driven Transformer Approach To Classification Of Cancer Pathology Reports, Mayanka Chandrashekar, Isaac Lyngaas, Heidi A. Hanson, Shang Gao, Xiao Cheng Wu, John Gounley

School of Public Health Faculty Publications

PURPOSE: Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS: We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an off-the-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. …

Brain-Inspired Continual Learning: Robust Feature Distillation And Re-Consolidation For Class Incremental Learning, Hikmat Khan, Nidhal Carla Bouaynaya, Ghulam Rasool

Henry M. Rowan College of Engineering Departmental Research

Artificial intelligence and neuroscience have a long and intertwined history. Advancements in neuroscience research have significantly influenced the development of artificial intelligence systems that have the potential to retain knowledge akin to humans. Building upon foundational insights from neuroscience and existing research in adversarial and continual learning fields, we introduce a novel framework that comprises two key concepts: feature distillation and re-consolidation. The framework distills continual learning (CL) robust features and rehearses them while learning the next task, aiming to replicate the mammalian brain's process of consolidating memories through rehearsing the distilled version of the waking experiences. Furthermore, the proposed …

Comprehensive Survey On Applications Of Internet Of Things, Machine Learning And Artificial Intelligence In Precision Agriculture, Paul Stone Stone Brown Macheso S.B.

Tanzania Journal of Engineering and Technology (TJET)

A comprehensive, multidisciplinary analysis of the latest developments in digital agriculture is conducted with the use of artificial intelligence (AI), machine learning (ML), and the Internet of Things. By automation and the use of modern, scalable technology solutions that reduce risks, support sustainability, and give farmers predictive advice, traditional agricultural processes are being updated and improved to maximize production. In this paper, the applications of AI, IoT, and ML in agricultural production systems are discussed in detail. The applications that have been explored can be broadly categorized into three areas: soil management, livestock management, and crop management. Weed detection, disease …

Generative Mechanisms Of Ai Implementation: A Critical Realist Perspective On Predictive Maintenance, Alexander Stohr, Philip Ollig, Robert Keller, Alexander Rieger

Information Systems Faculty Publications and Presentations

Artificial intelligence (AI) promises various new opportunities to create and appropriate business value. However, many organizations – especially those in more traditional industries – struggle to seize these opportunities. To unpack the underlying reasons, we investigate how more traditional industries implement predictive maintenance, a promising application of AI in manufacturing organizations. For our analysis, we employ a multiple-case design and adopt a critical realist perspective to identify generative mechanisms of AI implementation. Overall, we find five interdependent mechanisms: experimentation; knowledge building and integration; data; anxiety; and inspiration. Using causal loop diagramming, we flesh out the socio-technical dynamics of these mechanisms …

A New Scientific Research Paradigm Driven By Ai And Its Applications In Academic Disciplines, Jiang Yu, Yue Zhang, Yi Zhou

Bulletin of Chinese Academy of Sciences (Chinese Version)

The artificial intelligence (AI) -driven paradigm of scientific research, deeply embedded through the integration of data, computing power, and algorithms, has triggered profound changes in the research process, thinking logic, and organizational patterns. This study systematically summarizes the main characteristics and forms of the AI-driven new scientific research paradigm. It proposes that the evolution of the AI-driven research paradigm is shifting from “automated research” to “model-based research” and “intelligent research.” The depth and scope of AI applications in scientific research continue to expand, and this will drive significant changes in research organization and governance models. In addition, this study discusses …

Data And Intelligent Driven Space Science Experimental Research: New Exploration Under Ai4s Paradigm, Shengyang Li, Kang Liu, Yunfei Liu, Chufan Lai

Bulletin of Chinese Academy of Sciences (Chinese Version)

As artificial intelligence (AI) technology continues to advance, it is revolutionizing various scientific fields, giving rise to a new research paradigm known as AI for Science (AI4S). This study highlights the unique multidisciplinary advantages of AI in space science experiments conducted under microgravity conditions. It provides a comprehensive analysis of AI-driven approaches to multimodal space science experiment data pattern mining, domain knowledge extraction, interdisciplinary knowledge integration, and cognitive intelligence. The study reveals AI’s substantial potential to enhance intelligent scientific research, cognition, and discovery within the realm of space science experiments. The findings suggest that data-driven space science research, as a …

Path And Strategy Of Pollution And Carbon Reduction By Digitization In Electric Power Enterprises, Xiaohong Chen, Runcheng Tang, Dongbin Hu, Xuesong Xu, Xiangbo Tang, Guodong Yi, Weiwei Zhang

Bulletin of Chinese Academy of Sciences (Chinese Version)

With the extensive application and innovation of digital technology in the energy sector, digital technology has become increasingly crucial for the power industry to achieve the goal of reducing pollution and carbon emissions. How digital technology enables electric power enterprises to achieve this goal has attracted much attention. Firstly, the study analyzes the progress of digital technology applications in pollution reduction and carbon reduction in electric power enterprises. Then, it identifies the existing problems in the current application of digital technology in the power industry for reducing pollution and carbon emissions. Finally, it explores the potential ways and approaches of …

Analysis And Recommendations For Energy Conservation And Carbon Emission Reduction In Industry Boosted By Digital Energy Management Systems, Duanyang Geng, Tong Xu, Qinghua Zhu, Steve Evans

Bulletin of Chinese Academy of Sciences (Chinese Version)

Energy consumption during production processes in the industry is a main source of carbon dioxide emissions. Therefore, for China’s dual-carbon goals, industrial enterprises need to focus on reducing energy waste to achieve energy-efficient production, thereby effectively reducing carbon emissions in industrial production. In recent years, with the continuous development and popularization of digital technology, digital energy management systems have played a crucial role in energy saving by visualizing invisible energy in the industry. In this context, this study first analyses the current status of digital energy management system applications in the UK, the US, Germany, and Sweden, summarizes their characteristics …

Development Path And Policy Guarantee Of China's Advanced Manufacturing Industry Under Background Of Fourth Industrial Revolution, Chang Wang, Siyuan Zhou, Hongjun Geng

Bulletin of Chinese Academy of Sciences (Chinese Version)

How to seize the opportunity window opened by the fourth industrial revolution and enhance the international competitive advantage of advanced manufacturing has become an important issue concerned by existing research and policy practitioners. This study analyzes the background, characteristics, and influence of the fourth industrial revolution on the development of advanced manufacturing industry. Based on this, it discusses the development status and problems of four types of advanced manufacturing industries, including digitally empowered new infrastructure industries, intelligent manufacturing high-end equipment industries, brand-oriented new consumption industries, and science-based industries. The development paths of “fusion innovation”, “intelligent manufacturing upgrade”, “quality improvement”, and …

Thoughts On Transformation Of Scientific And Technological Achievements In Field Of Information Technology, Ninghui Sun, Xiaojuan Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

To promote the transformation of scientific and technological achievements is one of the key points of China’s national science and technology innovation policy. Nevertheless, due to the particularity, complexity, and professionalism of technological achievements, being difficult to transform scientific and technological achievements is a worldwide common problem. There are many issues worth discussing and exploring in China’s transformation of scientific and technological achievements, especially when it comes to whether research institutes can transform their achievements by establishing enterprises, the answers remain controversial. The authors intend to take the field of information technology as an example, by analyzing the advantages, disadvantages, …

Key Elements, Mechanism Analysis And Evaluation Indicators Of Digital And Intelligent Integration Transformation And Development Of Manufacturing Industry, Xiaoqiang Sun, Xiuyun Gao, Yumei Wang

Bulletin of Chinese Academy of Sciences (Chinese Version)

The digital and intelligent integration transformation of manufacturing industry has become an important driving force for the high-quality development of traditional manufacturing enterprises. This study clarifies the main research context and key issues of scholars on the digital and intelligent integration transformation of manufacturing industry, refines the goals, main elements, and influencing factors of digital and intelligent integration transformation of manufacturing industry, builds a power network model for the transformation and development of digital and intelligent integration of manufacturing industry according to the system feedback principle of system dynamics, analyzes the mechanism of action between various elements of the system, …

Cancergpt For Few Shot Drug Pair Synergy Prediction Using Large Pretrained Language Models, Tianhao Li, Sandesh Shetty, Advaith Kamath, Ajay Jaiswal, Xiaoqian Jiang, Ying Ding, Yejin Kim

Faculty, Staff and Student Publications

Large language models (LLMs) have been shown to have significant potential in few-shot learning across various fields, even with minimal training data. However, their ability to generalize to unseen tasks in more complex fields, such as biology and medicine has yet to be fully evaluated. LLMs can offer a promising alternative approach for biological inference, particularly in cases where structured data and sample size are limited, by extracting prior knowledge from text corpora. Here we report our proposed few-shot learning approach, which uses LLMs to predict the synergy of drug pairs in rare tissues that lack structured data and features. …

Efficiency Optimization Method For Data Sampling In Power Grid Topology Scheduling Simulation, Yingying Zhao, Pusen Dong, Tianchen Zhu, Fan Li, Yun Su, Zhenying Tai, Qingyun Sun, Hang Fan

Journal of System Simulation

Abstract: To address the large simulation computational workload and low simulation speed caused by the scale and complexity of the new power system, a simulation acceleration method for topology scheduling based on the distributed and quantization mechanisms is proposed. The parallelization of topology scheduling models is used to increase the scale of data simulation sampling in unit time. The introduced quantization operators accelerate the computation speed of the topology scheduling model operators, reduces the time cost of the every single simulation. Case studies confirm the effectiveness of the topology simulation acceleration, in which the available transfer capacity of the simulated …

Runoff Intelligent Prediction Method Based On Broad-Deep Fusion Time-Frequency Analysis, Ying Han, Lehao Wang, Shumei Wang, Xiang Zhang, Xingxing Luo

Journal of System Simulation

Abstract: Broad learning system(BLS) is introduced to tackle the existed disadvantage that LSTM-based runoff prediction model is easy to fall into local optimization. To reduce the influence of noise on the prediction results, the variational mode decomposition (VMD) is adopted to transform the onedimensional time-domain runoff signal to the two-dimensional time-frequency plane. The runoff prediction model based on VMD-LSTM-BLS is proposed. The simulation results demonstrate that the prediction accuracy of the new model is more significantly improved compared with the baseline model and the existing LSTM-based runoff prediction model.

Simulation Platform Of Agv System Scheduling Algorithms In Uncertain Environment, Zhihao Shi, Haihui Shen

Journal of System Simulation

Abstract: As a complete automated guided vehicle(AGV) system scheduling algorithm, it must include conflict-handling strategy, together with common dispatching strategy and routing algorithm. However, due to the uncertainty, characteristics of such scheduling algorithm are difficult to be analyzed theoretically, and the relevant study is lacking. An AGV system simulation platform based on the discreteevent simulation technique is designed and developed, which can flexibly set the scheduling problem, choose the dispatching strategy, routing algorithm, and conflict-handling strategy for the scheduling algorithm, to run the simulation. The platform has a visual interface, from which the running status of AGVs and the performance …

Research On Motion Planning Of Hexapod Robot Based On Drl And Free Gait, Xinpeng Wang, Huiqiao Fu, Guizhou Deng, Kaiqiang Tang, Chunlin Chen, Canghao Liu

Journal of System Simulation

Abstract: To improve the passability and the motion performance of the hexapod robot in the unstructured environment, a multi-contact motion planning algorithm based on DRL and free gait planner is proposed. Firstly, the free gait planner obtains the reachable footholds under the target state and outputs the optimal gait sequence. The center of mass motion policy of the hexapod robot in the randomly generated plum blossom pile environment is obtained by using deep reinforcement learning training. To ensure the reachability between adjacent states of the robot in motion, the state transition feasibility model is used to judge the state transition …

Robust Predictive Control Of Nonplanar Fully-Actuated Uavs, Yun Ma, Yuan Wang, Meng Li, Peng Wang, Yanling Tang

Journal of System Simulation

Abstract: Targeting the problem that nonplanar fully-actuated unmanned aerial vehicles (UAVs) are susceptible to external winds and unmodeled dynamics, the predictive control system with good robustness is designed. A nonlinear motion model with six degrees of freedom is established through the Newton-Euler approach. A linear extended state observer is designed to estimate the state variables by transforming the system affected by matched and unmatched disturbances into an equivalent system only affected by the matched disturbances. A predictive controller is designed for the equivalent system to reduce the output oscillation and input surging and a disturbance compensator is also designed to …

Reconnaissance Mission Planning Method For Air-Ground Heterogeneous Unmanned Systems, Guohui Zhang, Ya'nan Zhang, Ang Gao, Aoyu Xu

Journal of System Simulation

Abstract: Compared with the air-based homogeneous unmanned system, the motion capabilities, resource payloads, and combat scenes in the air-ground heterogeneous unmanned system increase the number of constraint conditions and significantly increase the computational complexity of the solution model. The modeling of collaborative combat missions and the efficient solution of large-scale problems are the key issues. With the time, path cost, and reconnaissance benefit as the objective functions, considering the constraints such as the endurance of unmanned platforms, a multi-objective programming model for the reconnaissance missions of an air-ground heterogeneous unmanned system is constructed. Aiming at the urban combat environments with …

A Simplification Method Of Large-Scale Unit Commitment Model Based On Boundary Method, Yanping Xu, Mingxin Zhao, Xiaohui Qin, Keyou He, Xiaohan Wu, Pei Zhang

Journal of System Simulation

Abstract: As the scale of power grid expands, in the market environment, the variables and constraints in the security-constrained unit commitment (SCUC) model considering power grid security constraints increase significantly and the solvability of the model reduces. When the model scale is too large, even the existing commercial solvers cannot solve it. Aiming at the model rapid solving, from the perspective of reducing the number of model constraints, a linear constraint simplification method based on the boundary method is proposed. The proposed method can effectively reduce the model's size by eliminating the redundant linear constraints. The IEEE-39, WECC 179 and …

Optimal Scheduling Strategy Of Virtual Power Plant With Carbon Emission And Carbon Penalty Considering Uncertainty Of Wind Power And Photovoltaic Power, Jijun Shui, Daogang Peng, Yankan Song, Qiang Zhou

Journal of System Simulation

Abstract: To better meet the development needs of China's new power system, an optimal scheduling strategy of virtual power plant(VPP) with carbon emission and carbon penalty considering the uncertainty of wind power and photovoltaic power is proposed. The mathematical description of photovoltaic(PV), wind turbine(WT), combined heat and power(CHP) unit and energy storage system (ESS) is carried out, and a wind-solar output model considering the uncertainty is established. The scenario generation and reduction method is used to generate the typical scenario. To maximize the overall operation benefit of VPP, considering carbon emission cost and carbon penalty, an optimal scheduling model of …