Computer Sciences Commons^™

Why The Ethical Use Of Ai Matters For Your Career, Jack Mcguire, David De Cremer, Yorck Hesselbarth, Leander De Schutter

Research Collection Lee Kong Chian School Of Business

In the contemporary digital era, innovations such as artificial intelligence (AI) are profoundly transforming the business landscape (De Cremer, 2020). The buzz surrounding ChatGPT, coupled with recent assertions about the sentience of Google’s LaMDA, a large language model, underscore the prominence of chatbot technology in these advancements (Adamopoulou & Moussiades, 2020; Ryu & Lee, 2018; Tiku, 2022). Customer-oriented chatbots, an emergent application of this tech, offer unparalleled efficiency and cost-effectiveness, operating ceaselessly and responding to client inquiries in real time (Salesforce, Research, 2019). Yet, amidst these advantages lies an ethical conundrum. Customers cherish genuine human interaction and can become quickly …

Ai Fairness In Action: A Human-Computer Perspective On Ai Fairness In Organizations And Society, David De Cremer, Jack Mcguire, Jack Mcguire

Research Collection Lee Kong Chian School Of Business

Artificial intelligence (AI) systems are being increasingly adopted by society, governments, and organizations in various decision-making contexts. For example, organizations use AI systems to decide whether applicants can be considered for a job, whether bonuses and other rewards should be allocated, or whether promotions and further training need to be invested in. In fact, as AI is seen as an important catalyst of economic growth, organizations today seem to know no boundaries in their AI adoption efforts, making employees and society more dependent on and thus also more vulnerable to the decisions made by or in partnership with AI (De …

A Use Case Of Chatgpt: Summary Of An Expert Panel Discussion On Electronic Health Records And Implementation Science, Seppo T Rinne, Julian Brunner, Timothy P Hogan, Jacqueline M Ferguson, Drew A Helmer, Sylvia J Hysong, Grace Mckee, Amanda Midboe, Megan E Shepherd-Banigan, A Rani Elwy

Center for Medical Ethics and Health Policy Staff Publications

Objective: Artificial intelligence (AI) is revolutionizing healthcare, but less is known about how it may facilitate methodological innovations in research settings. In this manuscript, we describe a novel use of AI in summarizing and reporting qualitative data generated from an expert panel discussion about the role of electronic health records (EHRs) in implementation science.

Materials and methods: 15 implementation scientists participated in an hour-long expert panel discussion addressing how EHRs can support implementation strategies, measure implementation outcomes, and influence implementation science. Notes from the discussion were synthesized by ChatGPT (a large language model-LLM) to generate a manuscript summarizing the discussion, …

Generative Artificial Intelligence: Basic Terminology And Concepts, Kincaid Brown

Law Librarian Scholarship

Generative artificial intelligence (GenAI) has been a hard topic to avoid in the media for more than a year. But what do all of the terms mean and what are areas of concern with GenAI tools?

This column aims to provide a baseline explanation of terminology and concepts that are frequently in the media.

Foraging Economies: A Market Based Methodology For Robotic Swarm Foraging, John A. Little

Graduate Theses, Dissertations, and Problem Reports (ETD)

Swarm robotics involves coordinating large groups of autonomous agents to accomplish complex tasks through decentralized, adaptive behaviors, providing a robust and scalable approach suited to dynamic and unpredictable environments. While traditional swarm models frequently draw inspiration from biological systems such as ant colonies or bee foraging, other approaches use techniques from physics, control theory, and economics to achieve effective coordination. This study distinguishes itself by applying economic principles—specifically, market-driven mechanisms like auctions, utility functions based on opportunity cost, and supply-demand dynamics based on fluctuating resource values at a central base—to improve task allocation within a swarm foraging context. This approach …

Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …

Infusing Machine Learning And Computational Linguistics Into Clinical Notes, Funke V. Alabi, Onyeka Omose, Omotomilola Jegede

Mathematics & Statistics Faculty Publications

Entering free-form text notes into Electronic Health Records (EHR) systems takes a lot of time from clinicians. A large portion of this paper work is viewed as a burden, which cuts into the amount of time doctors spend with patients and increases the risk of burnout. We will see how machine learning and computational linguistics can be infused in the processing of taking clinical notes. We are presenting a new language modeling task that predicts the content of notes conditioned on historical data from a patient's medical record, such as patient demographics, lab results, medications, and previous notes, with the …

Sparse Representer Theorems For Learning In Reproducing Kernel Banach Spaces, Rui Wang, Yuesheng Xu, Mingsong Yan

Mathematics & Statistics Faculty Publications

Sparsity of a learning solution is a desirable feature in machine learning. Certain reproducing kernel Banach spaces (RKBSs) are appropriate hypothesis spaces for sparse learning methods. The goal of this paper is to understand what kind of RKBSs can promote sparsity for learning solutions. We consider two typical learning models in an RKBS: the minimum norm interpolation (MNI) problem and the regularization problem. We first establish an explicit representer theorem for solutions of these problems, which represents the extreme points of the solution set by a linear combination of the extreme points of the subdifferential set, of the norm function, …

Latent Space Dynamics Learning For Stiff Collisional-Radiative Models, Xuping Xie, Qi Tang, Xianzhu Tang

Mathematics & Statistics Faculty Publications

In this work, we propose a data-driven method to discover the latent space and learn the corresponding latent dynamics for a collisional-radiative (CR) model in radiative plasma simulations. The CR model, consisting of high-dimensional stiff ordinary differential equations, must be solved at each grid point in the configuration space, leading to significant computational costs in plasma simulations. Our method employs a physics-assisted autoencoder to extract a low-dimensional latent representation of the original CR system. A flow map neural network is then used to learn the latent dynamics. Once trained, the reduced surrogate model predicts the entire latent dynamics given only …

Implementing Unmanned Aerial Vehicles To Collect Human Gait Data At Distance And Altitude For Identification And Re-Identification, Donn E. Bartram

Graduate Theses, Dissertations, and Problem Reports (ETD)

Gait patterns are a class of biometric information pertaining to the way a person moves and poses. Gait information is unique to each person and can be used to identify and reidentify people. Historically, this task has been achieved through the use of multiple ground-based imaging sensors. However, as Unmanned Aerial Vehicles (UAVs) advance, they present the opportunity to evolve the process of persons identification and re-identification. Collecting human gait data using UAVs at distances ranging from 20m to 500m and altitudes ranging from 0m to 120m is a challenging task. The current biometric data collection methods, primarily designed for …

Machine Learning And Rna Bioinformatics, Jason Rafe Miller

Graduate Theses, Dissertations, and Problem Reports (ETD)

The applied science of bioinformatics encompasses computational analysis of molecular biology data. Advances in genomics and DNA sequencing technology have enabled computational analysis of ribonucleic acids (RNAs), which play diverse and critical roles in most cells. To assist the study of human RNA, we trained machine learning models on RNA nucleotide sequences, devoid of domain knowledge. We built models that distinguish long non-coding lncRNA from protein-coding mRNA, and models that predict the cytoplasmic vs. nuclear preferences of lncRNAs. In a review of published lncRNA subcellular localization classifiers, we show that the commonly used validation protocol generates optimistic performance measures, and …

Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this …

Continual Learning, Fast And Slow, Quang Anh Pham, Chenghao Liu, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

According to the Complementary Learning Systems (CLS) theory (McClelland et al. 1995) in neuroscience, humans do effective continual learning through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics, individual experiences; and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose DualNets (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via …

Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara

Research Collection School Of Computing and Information Systems

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …

Applications Of Ai/Ml In Maritime Cyber Supply Chains, Rafael Diaz, Ricardo Ungo, Katie Smith, Lida Haghnegahdar, Bikash Singh, Tran Phuong

School of Cybersecurity Faculty Publications

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Classification Of Beef And Pork Images Based On Color Features And Pseudo Nearest Neighbor Rule, Ahmad Awaluddin Baiti, Muhammad Fachrie, Saucha Diwandari

Elinvo (Electronics, Informatics, and Vocational Education)

This research is motivated by the need for halal foods in Muslim society with the purpose of avoiding non-halal foods, such as pork, that are sold in the market. Although beef and pork basically have different characteristics, not all Muslims know the differences. Moreover, people nowadays sell beef mixed with pork to obtain more profits. Hence, this paper proposed the implementation of the Pseudo-Nearest Neighbor Rule (PNNR) in classifying images of beef and pork slices based on color features. Based on the image dataset that has been collected, the very significant difference that can be identified visually between beef and …

Outsourcing Voting To Ai: Can Chatgpt Advise Index Funds On Proxy Voting Decisions?, Chen Wang

Fordham Journal of Corporate & Financial Law

Released in November 2022, Chat Generative Pre-training Transformer (“ChatGPT”), has risen rapidly to prominence, and its versatile capabilities have already been shown in a variety of fields. Due to ChatGPT’s advanced features, such as extensive pre-training on diverse data, strong generalization ability, fine-tuning capabilities, and improved reasoning, the use of AI in the legal industry could experience a significant transformation. Since small passive funds with low-cost business models generally lack the financial resources to make informed proxy voting decisions that align with their shareholders’ interests, this Article considers the use of ChatGPT to assist small investment funds, particularly small passive …

Research On Digital Twin Data Modeling And Evaluation Method Of Automated Container Terminal, Guoxuan Xu, Daofang Chang, Jiaqi Li, Qiang Ling

Journal of System Simulation

Abstract: To make full use of the massive operation data of automated container terminals and further realize the digital and intelligent transformation of terminals driven by digital twin, a method for digital twin data modeling and effect verification and evaluation of automated container terminals is proposed. The application framework and operation mechanism based on digital twin are studied. Based on the data processing logic of digital twin framework, a method of terminal operation process evolution and dynamic data modeling based on digital twin is proposed. To verify whether the data could meet the effective operation of the digital twin, a …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Development Of Several Typical Virtual Reality Fusion Technologies, Qiqi Feng, Zhiming Dong, Wencheng Peng, Yi Dai, Bingshan Si

Journal of System Simulation

Abstract: Virtual reality fusion can realize the two-way interaction, mapping and linkage between virtual world and physical world, which attracts the attention of countries in the world. In order to sort out and make statistics on concept connotation, academic status and application of the related new technologies, digital twin, cyber-physical systems, metaverse and live-virtual-constructive simulation are taken as representatives. The comparison on the development process, functional characteristics, target trends, etc. is carried out.

Unrelated Parallel Machine Scheduling With Additional Resource And Learning Effect, Youlian Zheng, Deming Lei

Journal of System Simulation

Abstract: To solve unrelated parallel machine scheduling problem(UPMSP) with additional resource and learning effect, a dynamical artificial bee colony(DABC) algorithm is proposed to minimize the makespan. A new representation and decoding process is given and two initial bee swarms are constructed. A swarm evaluation method is applied to dynamically decide employed bee swarms and onlooker bee swarms. Employed bee phase and onlooker bee phase are implemented in different ways to increase exploration ability. The experimental results show that the new strategies of DABC are effective and reasonable, and can obtain results with better convergence, average value and stability, which d …

Research And Design Of Etc Simulation Platform For Expressway, Fumin Zou, Feng Guo, Sijie Luo, Lüchao Liao, Nan Li, Yue Xing

Journal of System Simulation

Abstract: It is difficult to quantitatively calculate and display the real-time traffic situation of expressway ETC system, and there is no simulation system for ETC to optimize the operating situation. A simulation system based on ETC data in proposed, in witch there are three key algorithms. ETC data feature extraction algorithm provides the feature of generating simulation data for the simulation platform. The improved multitask scheduling algorithm has the computing ability of multitasks in simulation environment. The algorithm of expressway traffic flow control strategy provides the decision index for traffic flow control on the way. The experimental results show that …

Research And Development Of Simulation Training Platform For Multi-Agent Collaborative Decision-Making, Cheng Cheng, Zhijie Chen, Ziming Guo, Ni Li

Journal of System Simulation

Abstract: Reinforcement learning simulation platform can be an interactive and training environment for reinforcement learning. In order to make the simulation platform compatible with the multi-agent reinforcement learning algorithms and meet the needs of simulation in military field, the similar processes in multi-agent reinforcement learning algorithms are refined and a unified interface is designed to embed and verify different types of deep reinforcement learning algorithms on the simulation platform and to optimize the back-end service of the simulation platform to accelerate the training process of the algorithm model. The experimental results show that, by unifing the interface, the simulation platform …

Reliable Emergency Rescue Model Of Uavs Based On Blockchain, Mengyao Du, Kai Xu, Miao Zhang, Xiang Fu, Quanjun Yin

Journal of System Simulation

Abstract: Natural disasters may unpredictably disrupt ground communication infrastructure and transportation systems, and UAVs emergency response can deal with such uncertainties and highly dynamic scenarios. Aiming at the robustness requirements of decentralized rescue systems. UAV emergency rescue chain (UERChain) based on blockchain technology is proposed. By deploying UAV backbone nodes within a layered local network, the smart contracts for managing reputation considering UAV social relationships are designed. The blockchain is employed as a trust mechanism to realize the trustworthy interactions among distributed UAVs. Experimental results show that, UERChain has higher robustness, and within controllable resource constraints, the reputation management and …

Optimized Scheduling Of Distribution Network With Distributed Generation Based On Coronavirus Herd Immunity Optimizer Algorithm, Xiaomeng Wu, Rongze Yuan, Yingliang Li, Qi Zhu

Journal of System Simulation

Abstract: Following the large-scale entry of distributed new energy into the network, the uncertainty factor of the distribution network increases significantly, and the difficulty of reactive power optimization scheduling increases accordingly. Traditional optimization solutions have many limitations and shortcomings, and a dynamic reactive power optimization scheme for active distribution networks based on a multi-scenario approach is proposed. The mathematical modeling is carried out separately for the uncertainty of new energy and load, and the multi-scenario method is used to transform the uncertainty problem into a deterministic problem. A mathematical model is constructed on the distribution network side to pursue the …

Data Simulation Testing Framework For Complex Process Equipment Software, Jinkun Zhang, Longfei Shi, Chi Hu, Hao Zhang, Yonghui Yang

Journal of System Simulation

Abstract: Due to the complex task, tight coupling, strict timing, and a large amount of interchange data, the technical threshold of automated testing of bus communication equipment software is high, and the implementation is difficult. The ideas of data-driven testing and keyword-driven testing are introduced, and a data simulation testing framework is proposed. Configuration rules are formulated and implemented in the framework. Testers can simulate peripheral data for complex process equipment software and implement automated testing by only focusing on the task analysis, and configuring interchange data and keywords. There is no need to develop test scripts, which reduces the …

Urban Uav Path Planning Based On Improved Beetle Search Algorithm, Qingqing Yang, Minyi Deng, Yi Peng

Journal of System Simulation

Abstract: An improved SABAS is proposed to improve the safety and path smoothing of UAV missions in urban multi-obstacle environments and to obtain the shortest path. The algorithm no longer completely depends on the difference of odor concentration between the left and the right tentacles of beetle when exploring the path for position update. Instead, it makes full use of the strong searching ability of BAS algorithm, and introduces the annealing algorithm to add the neighborhood position solution of the next position, and finally selects the next best position from the neighborhood position solution. Metropolis criterion of annealing algorithm is …

Task Scheduling For Internet Of Vehicles Based On Deep Reinforcement Learning In Edge Computing, Xiang Ju, Shengchao Su, Chaojie Xu, Beibei He

Journal of System Simulation

Abstract: Aiming at the offloading and execution of delay-constrained computing tasks for internet of vehicles in edge computing, a task scheduling method based on deep reinforcement learning is proposed. In multi-edge server scenario, a software-defined network-aided internet of vehicles task offloading system is built. On this basis, the task scheduling model of vehicle computation offloading is given. According to the characteristics of task scheduling, a scheduling method based on an improved pointer network is designed. Considering the complexity of task scheduling and computing resource allocation, the deep reinforcement learning algorithm is used to train the pointer network. The vehicle offloading …

Airport Operational Efficiency Evaluation Based On Combined Weighting-Topsis Model, Jie Hu, Fan Bao

Journal of System Simulation

Abstract: In order to improve the scientificity and comprehensiveness of the airport operational efficiency evaluation, a new method based on the combined weighting-TOPSIS model is proposed. From 4 dimensions of stand operational efficiency, passenger boarding efficiency, aircraft taxiing efficiency, and coordination efficiency, a new airport operational efficiency evaluation system consisting of 11 indicators, such as flight approach rate, corridor bridge turnover rate, stand change ratio, etc., are constructed. G1 method and entropy weight method are implemented respectively to calculate the subjective and objective weights of the evaluation indicators, and the combined weights are calculated by minimizing the deviation of subjective …