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Articles 33481 - 33510 of 302565

Full-Text Articles in Physical Sciences and Mathematics

State Graph Reasoning For Multimodal Conversational Recommendation, Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, Tat-Seng Chua Mar 2022

State Graph Reasoning For Multimodal Conversational Recommendation, Yuxia Wu, Lizi Liao, Gangyi Zhang, Wenqiang Lei, Guoshuai Zhao, Xueming Qian, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational recommendation system (CRS) attracts increasing attention in various application domains such as retail and travel. It offers an effective way to capture users’ dynamic preferences with multi-turn conversations. However, most current studies center on the recommendation aspect while over-simplifying the conversation process. The negligence of complexity in data structure and conversation flow hinders their practicality and utility. In reality, there exist various relationships among slots and values, while users’ requirements may dynamically adjust or change. Moreover, the conversation often involves visual modality to facilitate the conversation. These actually call for a more advanced internal state representation of the dialogue …

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Revisiting Neuron Coverage Metrics And Quality Of Deep Neural Networks, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo Mar 2022

Revisiting Neuron Coverage Metrics And Quality Of Deep Neural Networks, Zhou Yang, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo

Research Collection School Of Computing and Information Systems

Deep neural networks (DNN) have been widely applied in modern life, including critical domains like autonomous driving, making it essential to ensure the reliability and robustness of DNN-powered systems. As an analogy to code coverage metrics for testing conventional software, researchers have proposed neuron coverage metrics and coverage-driven methods to generate DNN test cases. However, Yan et al. doubt the usefulness of existing coverage criteria in DNN testing. They show that a coverage-driven method is less effective than a gradient-based method in terms of both uncovering defects and improving model robustness. In this paper, we conduct a replication study of …

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On The Influence Of Biases In Bug Localization: Evaluation And Benchmark, Ratnadira Widyasari, Stefanus Agus Haryono, Ferdian Thung, Jieke Shi, Constance Tan, Fiona Wee, Jack Phan, David Lo Mar 2022

On The Influence Of Biases In Bug Localization: Evaluation And Benchmark, Ratnadira Widyasari, Stefanus Agus Haryono, Ferdian Thung, Jieke Shi, Constance Tan, Fiona Wee, Jack Phan, David Lo

Research Collection School Of Computing and Information Systems

Bug localization is the task of identifying parts of thesource code that needs to be changed to resolve a bug report.As this task is difficult, automatic bug localization tools havebeen proposed. The development and evaluation of these toolsrely on the availability of high-quality bug report datasets. In2014, Kochhar et al. identified three biases in datasets used toevaluate bug localization techniques: (1) misclassified bug report,(2) already localized bug report, and (3) incorrect ground truthfile in a bug report. They reported that already localized bugreports statistically significantly and substantially impact buglocalization results, and thus should be removed. However, theirevaluation is still limited, …

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Interpretable Knowledge Tracing: Simple And Efficient Student Modeling With Causal Relations, Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Feida Zhu Mar 2022

Interpretable Knowledge Tracing: Simple And Efficient Student Modeling With Causal Relations, Sein Minn, Jill-Jênn Vie, Koh Takeuchi, Feida Zhu

Research Collection School Of Computing and Information Systems

Intelligent Tutoring Systems have become critically important in future learning environments. Knowledge Tracing (KT) is a crucial part of that system. It is about inferring the skill mastery of students and predicting their performance to adjust the curriculum accordingly. Deep Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value Memory Network (DKVMN) have shown significant predictive performance compared with traditional models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis (PFA). However, it is difficult to extract psychologically meaningful explanations from the tens of thousands of parameters in neural networks, that would relate to cognitive theory. There are …

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Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang Mar 2022

Heterogeneous Attentions For Solving Pickup And Delivery Problem Via Deep Reinforcement Learning, Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, there is an emerging trend to apply deep reinforcement learning to solve the vehicle routing problem (VRP), where a learnt policy governs the selection of next node for visiting. However, existing methods could not handle well the pairing and precedence relationships in the pickup and delivery problem (PDP), which is a representative variant of VRP. To address this challenging issue, we leverage a novel neural network integrated with a heterogeneous attention mechanism to empower the policy in deep reinforcement learning to automatically select the nodes. In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the …

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Estimating Financial Information Asymmetry In Real Estate Transactions In China: An Application Of Two-Tier Frontier Model, Ganlin Pu, Ying Zhang, Li-Chen Chou Mar 2022

Estimating Financial Information Asymmetry In Real Estate Transactions In China: An Application Of Two-Tier Frontier Model, Ganlin Pu, Ying Zhang, Li-Chen Chou

Research Collection School Of Computing and Information Systems

This study applies the two-tier stochastic frontier model to estimate the distribution of housing transaction information in Hangzhou, Wenzhou, Ningbo, and Jinhua (four cities in Zhejiang Province, China) during the year 2018, to analyze the difference in the price information acquired by the buyers and sellers in the transaction, and the effect of information asymmetry on the transaction price. The empirical results show that in each city, during the housing transaction process, the supplier has more complete information about house prices than consumers, and can therefore implement price discrimination strategies in setting service prices. Due to the disadvantage in acquired …

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The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo Mar 2022

The Impact Of Ride-Hail Surge Factors On Taxi Bookings, Sumit Agarwal, Ben Charoenwong, Shih-Fen Cheng, Jussi Keppo

Research Collection School Of Computing and Information Systems

We study the role of ride-hailing surge factors on the allocative efficiency of taxis by combining a reduced-form estimation with structural analyses using machine-learning-based demand predictions. Where other research study the effect of entry on incumbent taxis, we use higher frequency granular data to study how location-time-specific surge factors affect taxi bookings to bound the effect of customer decisions while accounting for various confounding variables. We find that even in a unique market like Singapore, where incumbent taxi companies have app-based booking systems similar to those from ride-hailing companies like Uber, the estimated upper bound on the cross-platform substitution between …

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Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel Mar 2022

Deep Learning For Anomaly Detection: A Review, Guansong Pang, Chunhua Shen, Longbing Cao, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Anomaly detection, a.k.a. outlier detection or novelty detection, has been a lasting yet active research area in various research communities for several decades. There are still some unique problem complexities and challenges that require advanced approaches. In recent years, deep learning enabled anomaly detection, i.e., deep anomaly detection, has emerged as a critical direction. This article surveys the research of deep anomaly detection with a comprehensive taxonomy, covering advancements in 3 high-level categories and 11 fine-grained categories of the methods. We review their key intuitions, objective functions, underlying assumptions, advantages, and disadvantages and discuss how they address the aforementioned challenges. …

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Androevolve: Automated Android Api Update With Data Flow Analysis And Variable Denormalization, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller Mar 2022

Androevolve: Automated Android Api Update With Data Flow Analysis And Variable Denormalization, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller

Research Collection School Of Computing and Information Systems

The Android operating system is frequently updated, with each version bringing a new set of APIs. New versions may involve API deprecation; Android apps using deprecated APIs need to be updated to ensure the apps’ compatibility with old and new Android versions. Updating deprecated APIs is a time-consuming endeavor. Hence, automating the updates of Android APIs can be beneficial for developers. CocciEvolve is the state-of-the-art approach for this automation. However, it has several limitations, including its inability to resolve out-of-method variables and the low code readability of its updates due to the addition of temporary variables. In an attempt to …

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Analyzing The Impact Of Digital Payment On Efficiency And Productivity Of Commercial Banks: A Case Study In China, Haopeng Wang, Aldy Gunawan Mar 2022

Analyzing The Impact Of Digital Payment On Efficiency And Productivity Of Commercial Banks: A Case Study In China, Haopeng Wang, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Digital payment has become one of the most popular payment methods all around the world, especially in countries that witnessed the rapid development of internet. As a traditional financial institution, commercial banks have been impacted by newly developed payment technology since third payment platforms have attracted customers to use the digital payment for daily consumption, transferring, and even investment. This paper focuses on analyzing whether and how the commercial banks in China have been affected by digital payment by using empirical methods. Systematic Generalized Method of Moments (SYS-GMM) is used to test the relationship between the productivity of commercial banks …

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Ispray: Reducing Urban Air Pollution With Intelligent Water Spraying, Yun Cheng, Zimu Zhou, Lothar Thiele Mar 2022

Ispray: Reducing Urban Air Pollution With Intelligent Water Spraying, Yun Cheng, Zimu Zhou, Lothar Thiele

Research Collection School Of Computing and Information Systems

Despite regulations and policies to improve city-level air quality in the long run, there lack precise control measures to protect critical urban spots from heavy air pollution. In this work, we propose iSpray, the first-of-its-kind data analytics engine for fine-grained PM2.5 and PM10 control at key urban areas via cost-effective water spraying. iSpray combines domain knowledge with machine learning to profile and model how water spraying affects PM25 and PM10 concentrations in time and space. It also utilizes predictions of pollution propagation paths to schedule a minimal number of sprayers to keep the pollution concentrations at key spots under control. …

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Ecosystem Duties, Green Infrastructure, And Environmental Injustice In Los Angeles, Sayd Randle Mar 2022

Ecosystem Duties, Green Infrastructure, And Environmental Injustice In Los Angeles, Sayd Randle

Research Collection College of Integrative Studies

In Los Angeles, water managers and environmentalist NGOs champion green infrastructure retrofits, installations intended to maximize the water-absorbing capacity of the urban landscape. In such arrangements, the work of water management is necessarily spread among a more-than-human community, including (but certainly not limited to) humans, plants, soils, and gravels. This article analyzes the human labor within these collaborations, tracking when and how this work gets enrolled in networks of water management and circuits of value. I develop the term ecosystem duties to characterize these exertions and as a useful analytic for assessing emergent dynamics of environmental justice.

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Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang Mar 2022

Neuron Coverage-Guided Domain Generalization, Chris Xing Tian, Haoliang Li, Xiaofei Xie, Yang Liu, Shiqi Wang

Research Collection School Of Computing and Information Systems

This paper focuses on the domain generalization task where domain knowledge is unavailable, and even worse, only samples from a single domain can be utilized during training. Our motivation originates from the recent progresses in deep neural network (DNN) testing, which has shown that maximizing neuron coverage of DNN can help to explore possible defects of DNN (i.e.,misclassification). More specifically, by treating the DNN as a program and each neuron as a functional point of the code, during the network training we aim to improve the generalization capability by maximizing the neuron coverage of DNN with the gradient similarity regularization …

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Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke Mar 2022

Sdac: A Slow-Aging Solution For Android Malware Detection Using Semantic Distance Based Api Clustering, Jiayun Xu, Yingjiu Li, Robert H. Deng, Xu Ke

Research Collection School Of Computing and Information Systems

A novel slow-aging solution named SDAC is proposed to address the model aging problem in Android malware detection, which is due to the lack of adapting to the changes in Android specifications during malware detection. Different from periodic retraining of detection models in existing solutions, SDAC evolves effectively by evaluating new APIs' contributions to malware detection according to existing API's contributions. In SDAC, the contributions of APIs are evaluated by their contexts in the API call sequences extracted from Android apps. A neural network is applied on the sequences to assign APIs to vectors, among which the differences of API …

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Update Recovery Attacks On Encrypted Database Within Two Updates Using Range Queries Leakage, Jianting Ning, Geong Sen Poh, Xinyi Huang, Robert H. Deng, Shuwei Cao, Ee-Chien Chang Mar 2022

Update Recovery Attacks On Encrypted Database Within Two Updates Using Range Queries Leakage, Jianting Ning, Geong Sen Poh, Xinyi Huang, Robert H. Deng, Shuwei Cao, Ee-Chien Chang

Research Collection School Of Computing and Information Systems

Recently, reconstruction attacks on static encrypted database supporting range queries have been proposed. However, attacks on encrypted database within two updates in the similar setting have not been studied extensively. As far as we know, the only work is the update recovery attack presented by Grubbs et al. (CCS 2018). Following their seminal work, we present new update recovery attacks for dense dataset (i.e. at least one record corresponding to each value in the range), which enable a deeper understanding of the impact caused by leakages due to updates on dynamic encrypted database. Our first attack aims at recovering the …

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Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang Mar 2022

Mg2vec: Learning Relationship-Preserving Heterogeneous Graph Representations Via Metagraph Embedding, Wentao Zhang, Yuan Fang, Zemin Liu, Min Wu, Xinming Zhang

Research Collection School Of Computing and Information Systems

Given that heterogeneous information networks (HIN) encompass nodes and edges belonging to different semantic types, they can model complex data in real-world scenarios. Thus, HIN embedding has received increasing attention, which aims to learn node representations in a low-dimensional space, in order to preserve the structural and semantic information on the HIN. In this regard, metagraphs, which model common and recurring patterns on HINs, emerge as a powerful tool to capture semantic-rich and often latent relationships on HINs. Although metagraphs have been employed to address several specific data mining tasks, they have not been thoroughly explored for the more general …

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Match In My Way: Fine-Grained Bilateral Access Control For Secure Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng Mar 2022

Match In My Way: Fine-Grained Bilateral Access Control For Secure Cloud-Fog Computing, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Cloud-fog computing is a novel paradigm to extend the functionality of cloud computing to provide a variety of on demand data services via the edge network. Many cryptographic tools have been introduced to preserve data confidentiality against the untrustworthy network and cloud servers. However, how to efficiently identify and retrieve useful data from a large number of ciphertexts without a costly decryption mechanism remains a challenging problem. In this paper, we introduce a cloud fog-device data sharing system (CFDS) with data confidentiality and data source identification simultaneously based on a new cryptographic primitive named matchmaking attribute-based encryption (MABE) by extending …

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Natural Phaeosphaeride A Derivatives Overcome Drug Resistance Of Tumor Cells And Modulate Signaling Pathways, Victoria Abzianidze, Natalia Moiseeva, Diana Suponina, Sofya Zakharenkova, Nadezhda Rogovskaya, Lidia Laletina, Alvin A. Holder, Denis Krivorotov, Alexander Bogachenkov, Alexander Garabadzhiu, Anton Ukolov, Vyacheslav Kosorukov Mar 2022

Natural Phaeosphaeride A Derivatives Overcome Drug Resistance Of Tumor Cells And Modulate Signaling Pathways, Victoria Abzianidze, Natalia Moiseeva, Diana Suponina, Sofya Zakharenkova, Nadezhda Rogovskaya, Lidia Laletina, Alvin A. Holder, Denis Krivorotov, Alexander Bogachenkov, Alexander Garabadzhiu, Anton Ukolov, Vyacheslav Kosorukov

Chemistry & Biochemistry Faculty Publications

n the present study, natural phaeosphaeride A (PPA) derivatives are synthesized. Anti-tumor studies are carried out on the PC3, K562, HCT-116, THP-1, MCF-7, A549, NCI-H929, Jurkat, and RPMI8226 tumor cell lines, and on the human embryonic kidney (HEK293) cell line. All the compounds synthesized turned out to have better efficacy than PPA towards the tumor cell lines listed. Among them, three compounds exhibited an ability to overcome the drug resistance of tumor cells associated with the overexpression of the P-glycoprotein by modulating the work of this transporter. Luminex xMAP technology was used to assess the effect of five synthesized compounds …

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Gender Influence On Communication Initiated Within Student Teams, Rita Garcia, Chieh-Ju Trinity Liao, Ariane Pearce, Christoph Treude Mar 2022

Gender Influence On Communication Initiated Within Student Teams, Rita Garcia, Chieh-Ju Trinity Liao, Ariane Pearce, Christoph Treude

Research Collection School Of Computing and Information Systems

Collaboration is important during software development, but related work has found gender differences can influence the collaboration process, creating inequality in the team’s dynamics. In this paper, we present a gender analysis study that involved 39 students, examining their teams’ online collaborations while contributing to a large open-source software project. Eight teams of 4-6 Software Engineering (SE) students communicated over an online messaging platform, Slack, to complete an eight-week project. The goal of this study is to identify gender differences emerging from team collaboration. A mixed-methods approach was used to collect students’ teamwork experiences and analyse their collaboration. Our research …

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Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc. Mar 2022

Silver Bow Creek/Butte Area Npl Site Butte Priority Soils Operable Unit, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

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Comparative Assessment Of Pollutant Concentrations And Meteorological Parameters From Tceq Cams Sites At Houston And Rio Grande Valley Regions Of Texas, Usa In 2016, Esmeralda Mendez, Jonathan Rodriguez, August Luna, Amit U. Raysoni Mar 2022

Comparative Assessment Of Pollutant Concentrations And Meteorological Parameters From Tceq Cams Sites At Houston And Rio Grande Valley Regions Of Texas, Usa In 2016, Esmeralda Mendez, Jonathan Rodriguez, August Luna, Amit U. Raysoni

School of Earth, Environmental, and Marine Sciences Faculty Publications and Presentations

Spatial and temporal heterogeneity in pollutant concentrations exists at the intra-urban level. In this research work, the concentrations of various pollutants and meteorological parameters are characterized between various central ambient monitoring sites at Houston, TX, and the Rio Grande Valley Regions of South Texas. Meteorological (temperature, relative humidity, wind speed and direction) and pollutant (O3, SO2, CO, NO2, and various PM species) concentrations were downloaded from the appropriate Texas Commission on Environmental Quality (TCEQ) Central Ambient Monitoring Station (CAMS) sites for the year 2016. Correlation Analyses and Coefficient of Divergence (COD) analyses suggest that statistically significant differences occur between the …

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Constructing Prediction Intervals With Neural Networks: An Empirical Evaluation Of Bootstrapping And Conformal Inference Methods, Alexander N. Contarino Mar 2022

Constructing Prediction Intervals With Neural Networks: An Empirical Evaluation Of Bootstrapping And Conformal Inference Methods, Alexander N. Contarino

Theses and Dissertations

Artificial neural networks (ANNs) are popular tools for accomplishing many machine learning tasks, including predicting continuous outcomes. However, the general lack of confidence measures provided with ANN predictions limit their applicability, especially in military settings where accuracy is paramount. Supplementing point predictions with prediction intervals (PIs) is common for other learning algorithms, but the complex structure and training of ANNs renders constructing PIs difficult. This work provides the network design choices and inferential methods for creating better performing PIs with ANNs to enable their adaptation for military use. A two-step experiment is executed across 11 datasets, including an imaged-based dataset. …

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Incorporating Armed Escorts To The Military Medical Evacuation Dispatching Problem Via Stochastic Optimization And Reinforcement Learning, Andrew G. Gelbard Mar 2022

Incorporating Armed Escorts To The Military Medical Evacuation Dispatching Problem Via Stochastic Optimization And Reinforcement Learning, Andrew G. Gelbard

Theses and Dissertations

The military medical evacuation (MEDEVAC) dispatching problem seeks to determine high-quality dispatching policies to maximize the survivability of casualties within contingency operations. This research leverages applied operations research and machine learning techniques to solve the MEDEVAC dispatching problem and evaluate system performance. More specifically, we develop an infinite-horizon, continuous-time Markov decision process (MDP) model and approximate dynamic programming (ADP) solution approach to generate high-quality policies. The ADP solution approach utilizes an approximate value iteration algorithm strategy incorporating gradient descent Q-learning to approximate the value function. A notional, synthetically-generated scenario in Africa based around the capital city of Niger, Niamey is …

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Formal Spark Verification Of Various Resampling Methods In Particle Filters, Osiris J. Terry Mar 2022

Formal Spark Verification Of Various Resampling Methods In Particle Filters, Osiris J. Terry

Theses and Dissertations

The software verification in this thesis concentrates on verifying a particle filter for use in tracking and estimation, a key application area for the Air Force. The development and verification process described in this thesis is a demonstration of the power, limitation, and compromises involved in applying automated software verification tools to critical embedded software applications.

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Simulating Autonomous Cruise Missile Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Kyle W. Goggins Mar 2022

Simulating Autonomous Cruise Missile Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Kyle W. Goggins

Theses and Dissertations

The increasingly sophisticated anti-access area denial (A2AD) threat imposed by the modern integrated air defense system (IADS), coupled with the decreasingly potent advantage provided by high-end stealth platforms, has prompted Air Force senior leaders to invest in radically changing the nature of air power for the year 2030 and beyond. A prominent element of this new vision is weapon swarming, which aims to address this challenge by overwhelming the IADS with huge numbers of low-cost, attritable aerial assets emboldened by autonomous capabilities. This research proposes a framework for classifying the different levels of autonomous capability along three independent dimensions—namely ability …

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Securing Infiniband Networks With End-Point Encryption, Noah B. Diamond Mar 2022

Securing Infiniband Networks With End-Point Encryption, Noah B. Diamond

Theses and Dissertations

The NVIDIA-Mellanox Bluefield-2 is a 100 Gbps high-performance network interface which offers hardware offload and acceleration features that can operate directly on network traffic without routine involvement from the ARM CPU. This allows the ARM multi-core CPU to orchestrate the hardware to perform operations on both Ethernet and RDMA traffic at high rates rather than processing all the traffic directly. A testbed called TNAP was created for performance testing and a MiTM verification process called MiTMVMP is used to ensure proper network configuration. The hardware accelerators of the Bluefield-2 support a throughput of nearly 86 Gbps when using IPsec to …

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Dds-Cerberus: Improving Security In Dds Middleware Using Kerberos Tickets, Andrew T. Park Mar 2022

Dds-Cerberus: Improving Security In Dds Middleware Using Kerberos Tickets, Andrew T. Park

Theses and Dissertations

The military deploys many IoT in battlefield operations to provide information on terrain and enemy combatants. It also deploys automated robots or UAVs where securing and trusting collected data is essential. Choosing the middleware that handles this message transfer is crucial for real-time operations. Networks with multiple entities, including IoT devices, UAVs, and small computers, require robust middleware facilitating message sending in real-time. Ideally, the middleware would provide QoS to handle lost packets and retransmissions in lossy environments, especially between low-power machines. DDS is a middleware that implements real-time and QoS capabilities by sending messages, not based on endpoints but …

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Exploring Learning Classifier System Behaviors In Multi-Action, Turn-Based Wargames, Garth J.S. Terlizzi Iii Mar 2022

Exploring Learning Classifier System Behaviors In Multi-Action, Turn-Based Wargames, Garth J.S. Terlizzi Iii

Theses and Dissertations

State of the art game-playing Artificial Intelligence research focuses heavily on non-symbolic learning methods. These methods offer little explainable insight into their decision-making processes. Learning Classifier Systems (LCSs) provide an alternative. LCSs use rule-based learning, guided by a Genetic Algorithm (GA), to produce a human-readable rule-set. This thesis explores LCS usefulness in game-playing agents for multi-agent wargames. Several Multi-Agent Learning Classifier System (MALCS) variants are implemented in the wargame Stratagem MIST: a Zeroeth-Level Classifier System (ZCS), an extended Classifier System (XCS), and an Adaptive Pittsburgh Classifier System (APCS). These algorithms were tested against baseline agents as well as the Online …

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Intercomparison Of Four Microphysics Schemes In Simulating Persistent Arctic Mixed-Phase Stratocumulus Clouds, Zachary A. Cleveland Mar 2022

Intercomparison Of Four Microphysics Schemes In Simulating Persistent Arctic Mixed-Phase Stratocumulus Clouds, Zachary A. Cleveland

Theses and Dissertations

Persistent Arctic mixed-phase stratocumulus clouds (AMPS) are important to the surface radiation budget of the Arctic. Their presence produces warming within the boundary layer and at the surface and inaccurately forecasting AMPS can lead to large, erroneous temperature forecasts. A Large Eddy Simulation of a case study of a persistent AMPS cloud was conducted using the Advanced Research Weather Research and Forecasting (WRF-ARW) model. The case examined occurred near Oliktok Point, AK between 26 and 27 April, 2017. The produced cloud pattern and properties of four different microphysics schemes -- P3, Thompson, Morrison, and WSM6 -- are compared to observations. …

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Thermo-Fluidic Transport Process In A Novel M-Shaped Cavity Packed With Non-Darcian Porous Medium And Hybrid Nanofluid: Application Of Artificial Neural Network (Ann), Dipak Kumar Mandal, Nirmalendu Biswas, Nirmal K. Manna, Dilip Kumar Gayen, Rama S. R. Gorla, Ali J. Chamkha Mar 2022

Thermo-Fluidic Transport Process In A Novel M-Shaped Cavity Packed With Non-Darcian Porous Medium And Hybrid Nanofluid: Application Of Artificial Neural Network (Ann), Dipak Kumar Mandal, Nirmalendu Biswas, Nirmal K. Manna, Dilip Kumar Gayen, Rama S. R. Gorla, Ali J. Chamkha

Faculty Publications

In this work, an attempt has been made to explore numerically the thermo-fluidic transport process in a novel M-shaped enclosure filled with permeable material along with Al2O3-Cu hybrid nanoparticles suspended in water under the influence of a horizontal magnetizing field. To exercise the influence of geometric parameters, a classical trapezoidal cavity is modified with an inverted triangle at the top to construct an M-shaped cavity. The cavity is heated isothermally from the bottom and cooled from the top, whereas the inclined sidewalls are insulated. The role of geometric parameters on the thermal performance is scrutinized thoroughly …

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