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Articles 331 - 360 of 705
Full-Text Articles in Physical Sciences and Mathematics
Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He
Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He
Computer Science Faculty Publications
This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow …
Deep Reinforcement Learning For Open Multiagent System, Tianxing Zhu
Deep Reinforcement Learning For Open Multiagent System, Tianxing Zhu
Honors Papers
In open multiagent systems, multiple agents work together or compete to reach the goal while members of the group change over time. For example, intelligent robots that are collaborating to put out wildfires may run out of suppressants and have to leave the place to recharge; the rest of the robots may need to change their behaviors accordingly to better control the fires. Thus, openness requires agents not only to predict the behaviors of others, but also the presence of other agents. We present a deep reinforcement learning method that adapts the proximal policy optimization algorithm to learn the optimal …
Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu
Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu
Computer Science Faculty Publications
Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …
Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski
Artificial Intelligence And Machine Learning In Optical Information Processing: Introduction To The Feature Issue, Khan Iftekharuddin, Chrysanthe Preza, Abdul Ahad S. Awwal, Michael E. Zelinski
Electrical & Computer Engineering Faculty Publications
This special feature issue covers the intersection of topical areas in artificial intelligence (AI)/machine learning (ML) and optics. The papers broadly span the current state-of-the-art advances in areas including image recognition, signal and image processing, machine inspection/vision and automotive as well as areas of traditional optical sensing, interferometry and imaging.
Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler
Defensive Distillation-Based Adversarial Attack Mitigation Method For Channel Estimation Using Deep Learning Models In Next-Generation Wireless Networks, Ferhat Ozgur Catak, Murat Kuzlu, Evren Catak, Umit Cali, Ozgur Guler
Engineering Technology Faculty Publications
Future wireless networks (5G and beyond), also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have dramatically grown with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those …
Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao
Security Hardening Of Intelligent Reflecting Surfaces Against Adversarial Machine Learning Attacks, Ferhat Ozgur Catak, Murat Kuzlu, Haolin Tang, Evren Catak, Yanxiao Zhao
Engineering Technology Faculty Publications
Next-generation communication networks, also known as NextG or 5G and beyond, are the future data transmission systems that aim to connect a large amount of Internet of Things (IoT) devices, systems, applications, and consumers at high-speed data transmission and low latency. Fortunately, NextG networks can achieve these goals with advanced telecommunication, computing, and Artificial Intelligence (AI) technologies in the last decades and support a wide range of new applications. Among advanced technologies, AI has a significant and unique contribution to achieving these goals for beamforming, channel estimation, and Intelligent Reflecting Surfaces (IRS) applications of 5G and beyond networks. However, the …
Smart Decision-Making Via Edge Intelligence For Smart Cities, Nathaniel Hudson
Smart Decision-Making Via Edge Intelligence For Smart Cities, Nathaniel Hudson
Theses and Dissertations--Computer Science
Smart cities are an ambitious vision for future urban environments. The ultimate aim of smart cities is to use modern technology to optimize city resources and operations while improving overall quality-of-life of its citizens. Realizing this ambitious vision will require embracing advancements in information communication technology, data analysis, and other technologies. Because smart cities naturally produce vast amounts of data, recent artificial intelligence (AI) techniques are of interest due to their ability to transform raw data into insightful knowledge to inform decisions (e.g., using live road traffic data to control traffic lights based on current traffic conditions). However, training and …
Long Term Predictive Modeling On Big Spatio-Temporal Data, Yong Zhuang
Long Term Predictive Modeling On Big Spatio-Temporal Data, Yong Zhuang
Graduate Doctoral Dissertations
In the era of massive data, one of the most promising research fields involves the analysis of large-scale Spatio-temporal databases to discover exciting and previously unknown but potentially useful patterns from data collected over time and space. A modeling process in this domain must take temporal and spatial correlations into account, but with the dimensionality of the time and space measurements increasing, the number of elements potentially contributing to a target sharply grows, making the target's long-term behavior highly complex, chaotic, highly dynamic, and hard to predict. Therefore, two different considerations are taken into account in this work: one is …
Robotic Olfactory-Based Navigation With Mobile Robots, Lingxiao Wang
Robotic Olfactory-Based Navigation With Mobile Robots, Lingxiao Wang
Doctoral Dissertations and Master's Theses
Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods.
A. …
Regulating New Tech: Problems, Pathways, And People, Cary Coglianese
Regulating New Tech: Problems, Pathways, And People, Cary Coglianese
All Faculty Scholarship
New technologies bring with them many promises, but also a series of new problems. Even though these problems are new, they are not unlike the types of problems that regulators have long addressed in other contexts. The lessons from regulation in the past can thus guide regulatory efforts today. Regulators must focus on understanding the problems they seek to address and the causal pathways that lead to these problems. Then they must undertake efforts to shape the behavior of those in industry so that private sector managers focus on their technologies’ problems and take actions to interrupt the causal pathways. …
Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport
Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport
Research Collection School Of Computing and Information Systems
To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.
From Mdp To Alphazero, David Robert Sewell
From Mdp To Alphazero, David Robert Sewell
Dissertations and Theses
In this paper I will explain the AlphaGo family of algorithms starting from first principles and requiring little previous knowledge from the reader. The focus will be upon one of the more recent versions AlphaZero but I hope to explain the core principles that allowed these algorithms to be so successful. I will generally refer to AlphaZero as theses [sic] core set of principles and will make it clear when I am referring to a specific algorithm of the AlphaGo family. AlphaZero in short combines Monte Carlo Tree Search (MCTS) with Deep learning and self-play. We will see how these …
Exploratory Data Mining Techniques (Decision Tree Models) For Examining The Impact Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Machine Learning Approach, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah
Exploratory Data Mining Techniques (Decision Tree Models) For Examining The Impact Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Machine Learning Approach, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah
School of Mathematical and Statistical Sciences Faculty Publications and Presentations
Background: There is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment …
The Ratio Method: Addressing Complex Tort Liability In The Fourth Industrial Revolution, Harrison C. Margolin, Grant H. Frazier
The Ratio Method: Addressing Complex Tort Liability In The Fourth Industrial Revolution, Harrison C. Margolin, Grant H. Frazier
St. Mary's Law Journal
Emerging technologies of the Fourth Industrial Revolution show fundamental promise for improving productivity and quality of life, though their misuse may also cause significant social disruption. For example, while artificial intelligence will be used to accelerate society’s processes, it may also displace millions of workers and arm cybercriminals with increasingly powerful hacking capabilities. Similarly, human gene editing shows promise for curing numerous diseases, but also raises significant concerns about adverse health consequences related to the corruption of human and pathogenic genomes.
In most instances, only specialists understand the growing intricacies of these novel technologies. As the complexity and speed of …
Check Your Tech - The Ethics Of Deepfakes In A Political Context, Dympna O'Sullivan, Damian Gordon, Ioannis Stavrakakis, Michael Collins
Check Your Tech - The Ethics Of Deepfakes In A Political Context, Dympna O'Sullivan, Damian Gordon, Ioannis Stavrakakis, Michael Collins
Conference papers
No abstract provided.
Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University
Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University
Perspectives@SMU
Artificial intelligence presents significant opportunities for business – as well as not insignificant threats to humanity – and governance frameworks are urgently needed to create a fair and equitable future under AI
Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe
Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe
Research Collection School Of Computing and Information Systems
Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance …
Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard
Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard
James Madison Undergraduate Research Journal (JMURJ)
The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …
How ‘Human’ Should Robots Be?, Singapore Management University
How ‘Human’ Should Robots Be?, Singapore Management University
Perspectives@SMU
Hotel guests like interaction with devices that look and sound like them, but they can spark displeasure after service failures, new CUHK study shows
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris
Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris
Theses and Dissertations
This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …
Artificial Intelligence And Work: Two Perspectives, Steven Miller, Thomas H. Davenport
Artificial Intelligence And Work: Two Perspectives, Steven Miller, Thomas H. Davenport
Research Collection School Of Computing and Information Systems
One of the most important issues in contemporary societies is the impact of intelligent technologies on human work. For an empirical perspective on the issue, we recently completed 30 case studies of people collaborating with AI-enabled smart machines. Twenty-four were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. We compare some of our observations to one of the broadest academic examinations of the issue. In particular, we focus on our case study observations with regard to key findings from the MIT Task Force on the Work of the Future report.
Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi
Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi
Dissertations
Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions.
First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule …
Predicting Human Behavior In Repeated Games With Attitude Vectors, Brian L. James
Predicting Human Behavior In Repeated Games With Attitude Vectors, Brian L. James
Theses and Dissertations
As Artificial Intelligence systems are used by human users at an increasing frequency, the need for such systems to understand and predict human behavior likewise increases. In my work, I have considered how to predict human behavior in repeated games. These repeated games can be applied as a foundation to many situations where a person may interact with an AI, In an attempt to create such a foundation, I have built a system using Attitude Vectors used in automata to predict actions based on prior actions and communications. These Attitude Vector Automata (AVA) can transform information from actions in one …
Innovative Computational Methods For Pharmaceutical Problem Solving A Review Part I: The Drug Development Process, Heather R. Campbell, Robert A. Lodder
Innovative Computational Methods For Pharmaceutical Problem Solving A Review Part I: The Drug Development Process, Heather R. Campbell, Robert A. Lodder
Pharmaceutical Sciences Faculty Publications
Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what's possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges.
Application Of Artificial Intelligence And Machine Learning In Libraries: A Systematic Review, Rajesh Kumar Das, Mohammad Sharif Ul Islam
Application Of Artificial Intelligence And Machine Learning In Libraries: A Systematic Review, Rajesh Kumar Das, Mohammad Sharif Ul Islam
Library Philosophy and Practice (e-journal)
As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were …
Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Harris, Martin Zwick
Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Harris, Martin Zwick
Systems Science Faculty Publications and Presentations
Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and …
Dealing With Classification Irregularities In Real-World Scenarios., Payel Sadhukhan Dr.
Dealing With Classification Irregularities In Real-World Scenarios., Payel Sadhukhan Dr.
Doctoral Theses
Data processing by the human sensory system comes naturally. This processing, commonly denoted as pattern recognition and analysis are carried out spontaneously by humans. In day to day life, in most cases, decision making by humans come without any conscious effort. From the middle of the past century, humans have shown interest to render their abstraction capabilities (pattern recognition and analysis) to the machine. The abstraction capability of the machine is ’machine intelligence’ or ’machine learning’ [87].The primary goal of machine learning methods is to extract some meaningful information from the ’data’. Data refers to the information or attributes that …
Beyond Spatial Reasoning: Challenges For Ecological Problem Solving, Christian Freksa
Beyond Spatial Reasoning: Challenges For Ecological Problem Solving, Christian Freksa
Journal of Spatial Information Science
This vision piece reflects upon virtues of early computer science due to scarcity and high cost of computational resources. It critically assesses divergences between real-world problems and their computational counterparts in commonsense problem solving. The paper points out the different objectives of commonsense versus scientific approaches to problem solving. It describes how natural cognitive systems exploit space and time without explicitly representing their properties and why purely computational approaches are less efficient than their natural role models, as they depend on explicit representations. We argue for investigating spatio-temporally integrated methods to spatial problem solving. We contrast these methods to sequential …
Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis, William A. Beldman
Predicting Stock Market Sector Sentiment Through News Article Based Textual Analysis, William A. Beldman
Electronic Thesis and Dissertation Repository
Investors seek to take advantage of computer technology to gain an edge on their investments. This can be done through quantitative (historical number-based) analysis or qualitative (natural language-based) analysis. Subject matter experts have been known to make predictions between 70 and 79% accuracy at best and less than 50% accuracy on average. Sophisticated algorithms through qualitative analysis are known to demonstrate more successful market predictions for specific stocks. It stands to reason that the same technique could work just as well or better for attempting to predict entire sectors of the stock market. By using indices and exchange traded funds, …
Methods For Detecting Floodwater On Roadways From Ground Level Images, Cem Sazara
Methods For Detecting Floodwater On Roadways From Ground Level Images, Cem Sazara
Computational Modeling & Simulation Engineering Theses & Dissertations
Recent research and statistics show that the frequency of flooding in the world has been increasing and impacting flood-prone communities severely. This natural disaster causes significant damages to human life and properties, inundates roads, overwhelms drainage systems, and disrupts essential services and economic activities. The focus of this dissertation is to use machine learning methods to automatically detect floodwater in images from ground level in support of the frequently impacted communities. The ground level images can be retrieved from multiple sources, including the ones that are taken by mobile phone cameras as communities record the state of their flooded streets. …