Artificial Intelligence and Robotics Commons^™

Correlated Learning For Aggregation Systems, Tanvi Verma, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Aggregation systems (e.g., Uber, Lyft, FoodPanda, Deliveroo) have been increasingly used to improve efficiency in numerous environments, including in transportation, logistics, food and grocery delivery. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. Due to optimizing a metric of importance to the centralized entity, the interests of individuals (e.g., drivers, delivery boys) can be sacrificed. Therefore, in this paper, we focus on the problem of serving individual interests, i.e., learning revenue maximizing policies for individuals in the presence …

On True Language Understanding, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Despite the relative successes of natural language processing in providing some useful interfaces for users, natural language understanding is a much more difficult issue. Natural language processing was one of the main topics of AI for as long as computers were put to the task of generating intelligent behavior, and a number of systems that were created since the inception of AI have also been characterized as being capable of natural language understanding. However, in the existing domain of natural language processing and understanding, a definition and consensus of what it means for a system to “truly” understand language do …

A Review On Swarm Intelligence And Evolutionary Algorithms For Solving Flexible Job Shop Scheduling Problems, Kaizhou Gao, Zhiguang Cao, Le Zhang, Zhenghua Chen, Yuyan Han, Quanke Pan

Research Collection School Of Computing and Information Systems

Flexible job shop scheduling problems (FJSP) have received much attention from academia and industry for many years. Due to their exponential complexity, swarm intelligence (SI) and evolutionary algorithms (EA) are developed, employed and improved for solving them. More than 60% of the publications are related to SI and EA. This paper intents to give a comprehensive literature review of SI and EA for solving FJSP. First, the mathematical model of FJSP is presented and the constraints in applications are summarized. Then, the encoding and decoding strategies for connecting the problem and algorithms are reviewed. The strategies for initializing algorithms? population …

Success Factors Impacting Artificial Intelligence Adoption --- Perspective From The Telecom Industry In China, Hong Chen

Theses and Dissertations in Business Administration

As the core driving force of the new round of informatization development and the industrial revolution, the disruptive achievements of artificial intelligence (AI) are rapidly and comprehensively infiltrating into various fields of human activities. Although technologies and applications of AI have been widely studied, and factors that affect AI adoption are identified in existing literature, the impact of success factors on AI adoption remains unknown. Accordingly, the main study of this paper proposes a framework to explore the effects of success factors on AI adoption by integrating the technology, organization, and environment (TOE) framework and diffusion of innovation (DOI) theory. …

Using Feature Extraction From Deep Convolutional Neural Networks For Pathological Image Analysis And Its Visual Interpretability, Wei-Wen Hsu

Electrical & Computer Engineering Theses & Dissertations

This dissertation presents a computer-aided diagnosis (CAD) system using deep learning approaches for lesion detection and classification on whole-slide images (WSIs) with breast cancer. The deep features being distinguishing in classification from the convolutional neural networks (CNN) are demonstrated in this study to provide comprehensive interpretability for the proposed CAD system using the domain knowledge in pathology. In the experiment, a total of 186 slides of WSIs were collected and classified into three categories: Non-Carcinoma, Ductal Carcinoma in Situ (DCIS), and Invasive Ductal Carcinoma (IDC). Instead of conducting pixel-wise classification (segmentation) into three classes directly, a hierarchical framework with the …

Developing Algorithms To Detect Incidents On Freeways From Loop Detector And Vehicle Re-Identification Data, Biraj Adhikari

Civil & Environmental Engineering Theses & Dissertations

A new approach for testing incident detection algorithms has been developed and is presented in this thesis. Two new algorithms were developed and tested taking California #7, which is the most widely used algorithm to date, and SVM (Support Vector Machine), which is considered one of the best performing classifiers, as the baseline for comparisons. Algorithm #B in this study uses data from Vehicle Re-Identification whereas the other three algorithms (California #7, SVM and Algorithm #A) use data from a double loop detector for detection of an incident. A microscopic traffic simulator is used for modeling three types of incident …

Interpretable Fashion Matching With Rich Attributes, Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Understanding the mix-and-match relationships of fashion items receives increasing attention in fashion industry. Existing methods have primarily utilized the visual content to learn the visual compatibility and performed matching in a latent space. Despite their effectiveness, these methods work like a black box and cannot reveal the reasons that two items match well. The rich attributes associated with fashion items, e.g.,off-shoulder dress and black skinny jean, which describe the semantics of items in a human-interpretable way, have largely been ignored.This work tackles the interpretable fashion matching task, aiming to inject interpretability into the compatibility modeling of items. Specifically, given a …

Could A Robot Be District Attorney?, Stephen Henderson

Other Faculty Publications

No abstract provided.

A Study On Large-Scale Deep Learning In Bioinformatics And Biomedical Applications, Shayan Shams

LSU Doctoral Dissertations

Recent advances in Artificial Intelligence and deep learning have provided researchers in various fields insights into the analysis of multiple datasets. These applications include image analysis, text analysis, and many more. However, the effectiveness of deep learning in some areas, such as biomedical imaging and genomic research, has been overshadowed by the variance in the types and complexity of data. This is in addition to the expensive labeling process and the limited size of datasets in these fields. These challenges require advanced deep learning models capable of learning from a small dataset and also from a small number of labeled …

Field Drilling Data Cleaning And Preparation For Data Analytics Applications, Daniel Cardoso Braga

LSU Master's Theses

Throughout the history of oil well drilling, service providers have been continuously striving to improve performance and reduce total drilling costs to operating companies. Despite constant improvement in tools, products, and processes, data science has not played a large part in oil well drilling. With the implementation of data science in the energy sector, companies have come to see significant value in efficiently processing the massive amounts of data produced by the multitude of internet of thing (IOT) sensors at the rig. The scope of this project is to combine academia and industry experience to analyze data from 13 different …

Forecasting Building Energy Consumption With Deep Learning: A Sequence To Sequence Approach, Ljubisa Sehovac, Cornelius Nesen, Katarina Grolinger

Electrical and Computer Engineering Publications

Energy Consumption has been continuously increasing due to the rapid expansion of high-density cities, and growth in the industrial and commercial sectors. To reduce the negative impact on the environment and improve sustainability, it is crucial to efficiently manage energy consumption. Internet of Things (IoT) devices, including widely used smart meters, have created possibilities for energy monitoring as well as for sensor based energy forecasting. Machine learning algorithms commonly used for energy forecasting such as feedforward neural networks are not well-suited for interpreting the time dimensionality of a signal. Consequently, this paper uses Recurrent Neural Networks (RNN) to capture time …

Identifying Hourly Traffic Patterns With Python Deep Learning, Christopher L. Leavitt

Computer Engineering

This project was designed to explore and analyze the potential abilities and usefulness of applying machine learning models to data collected by parking sensors at a major metro shopping mall. By examining patterns in rates at which customer enter and exit parking garages on the campus of the Bellevue Collection shopping mall in Bellevue, Washington, a recurrent neural network will use data points from the previous hours will be trained to forecast future trends.

Labeling Paths With Convolutional Neural Networks, Sean Wallace, Kyle Wuerch

Computer Engineering

With the increasing development of autonomous vehicles, being able to detect driveable paths in arbitrary environments has become a prevalent problem in multiple industries. This project explores a technique which utilizes a discretized output map that is used to color an image based on the confidence that each block is a driveable path. This was done using a generalized convolutional neural network that was trained on a set of 3000 images taken from the perspective of a robot along with matching masks marking which portion of the image was a driveable path. The techniques used allowed for a labeling accuracy …

Entrans: Leveraging Kinetic Energy Harvesting Signal For Transportation Mode Detection, Guohao Lan, Weitao Xu, Dong Ma, Sara Khalifa, Mahbub Hassan, Wen Hu

Research Collection School Of Computing and Information Systems

Monitoring the daily transportation modes of an individual provides useful information in many application domains, such as urban design, real-time journey recommendation, as well as providing location-based services. In existing systems, accelerometer and GPS are the dominantly used signal sources for transportation context monitoring which drain out the limited battery life of the wearable devices very quickly. To resolve the high energy consumption issue, in this paper, we present EnTrans, which enables transportation mode detection by using only the kinetic energy harvester as an energy-efficient signal source. The proposed idea is based on the intuition that the vibrations experienced by …

Meta-Transfer Learning For Few-Shot Learning, Qianru Sun, Yaoyao Liu, Tat-Seng Chua, Bernt Schiele

Research Collection School Of Computing and Information Systems

Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order to learn how to adapt a base-learner to a new task for which only a few labeled samples are available. As deep neural networks (DNNs) tend to overfit using a few samples only, meta-learning typically uses shallow neural networks (SNNs), thus limiting its effectiveness. In this paper we propose a novel few-shot learning method called meta-transfer learning (MTL) which learns to adapt a deep NN for few shot learning tasks. Specifically, …

Single Image Reflection Removal Beyond Linearity, Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Due to the lack of paired data, the training of image reflection removal relies heavily on synthesizing reflection images. However, existing methods model reflection as a linear combination model, which cannot fully simulate the real-world scenarios. In this paper, we inject non-linearity into reflection removal from two aspects. First, instead of synthesizing reflection with a fixed combination factor or kernel, we propose to synthesize reflection images by predicting a non-linear alpha blending mask. This enables a free combination of different blurry kernels, leading to a controllable and diverse reflection synthesis. Second, we design a cascaded network for reflection removal with …

Self-Supervised Spatio-Temporal Representation Learning For Videos By Predicting Motion And Appearance Statistics, Jiangliu Wang, Jianbo Jiao, Linchao Bao, Shengfeng He, Yunhui Liu, Wei Liu

Research Collection School Of Computing and Information Systems

We address the problem of video representation learning without human-annotated labels. While previous efforts address the problem by designing novel self-supervised tasks using video data, the learned features are merely on a frame-by-frame basis, which are not applicable to many video analytic tasks where spatio-temporal features are prevailing. In this paper we propose a novel self-supervised approach to learn spatio-temporal features for video representation. Inspired by the success of two-stream approaches in video classification, we propose to learn visual features by regressing both motion and appearance statistics along spatial and temporal dimensions, given only the input video data. Specifically, we …

An Agent-Based Model Of Financial Benchmark Manipulation, Gabriel Virgil Rauterberg, Megan Shearer, Michael Wellman

Articles

Financial benchmarks estimate market values or reference rates used in a wide variety of contexts, but are often calculated from data generated by parties who have incentives to manipulate these benchmarks. Since the the London Interbank Offered Rate (LIBOR) scandal in 2011, market participants, scholars, and regulators have scrutinized financial benchmarks and the ability of traders to manipulate them. We study the impact on market quality and microstructure of manipulating transaction-based benchmarks in a simulated market environment. Our market consists of a single benchmark manipulator with external holdings dependent on the benchmark, and numerous background traders unaffected by the benchmark. …

Brain Tumor Classification Using Hit-Or-Miss Capsule Layers, Spencer J. Chang

Master's Theses

The job of classifying or annotating brain tumors from MRI images can be time-consuming and difficult, even for radiologists. To increase the survival chances of a patient, medical practitioners desire a means for quick and accurate diagnosis. While datasets like CIFAR, ImageNet, and SVHN have tens of thousands, hundreds of thousands, or millions of samples, an MRI dataset may not have the same luxury of receiving accurate labels for each image containing a tumor. This work covers three models that classify brain tumors using a combination of convolutional neural networks and of the concept of capsule layers. Each network utilizes …

Can Algorithms Help Us Decide Who To Trust?, David De Cremer, Jack Mcguire, Yorck Hesselbarth, Ke M Mai

Research Collection Lee Kong Chian School Of Business

The use of artificial intelligence (AI) and algorithms is increasing within organizations to manage business processes, hire employees, and automate routine organizational decision making. This comes as no surprise, since the application of simple linear algorithms have been shown to outperform human judgment in the accuracy of many administrative tasks. A 2017 Accenture survey also revealed that 85% of executives want to invest more extensively in AI-related technologies over the next three years.

Probabilistic Spiking Neural Networks : Supervised, Unsupervised And Adversarial Trainings, Alireza Bagheri

Dissertations

Spiking Neural Networks (SNNs), or third-generation neural networks, are networks of computation units, called neurons, in which each neuron with internal analogue dynamics receives as input and produces as output spiking, that is, binary sparse, signals. In contrast, second-generation neural networks, termed as Artificial Neural Networks (ANNs), rely on simple static non-linear neurons that are known to be energy-intensive, hindering their implementations on energy-limited processors such as mobile devices. The sparse event-based characteristics of SNNs for information transmission and encoding have made them more feasible for highly energy-efficient neuromorphic computing architectures. The most existing training algorithms for SNNs are based …

Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan

Dissertations

Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.

First, dependencies among discrete, continuous and repeated observations are studied …

Deep Morphological Neural Networks, Yucong Shen

Theses

Mathematical morphology is a theory and technique applied to collect features like geometric and topological structures in digital images. Determining suitable morphological operations and structuring elements for a give purpose is a cumbersome and time-consuming task. In this paper, morphological neural networks are proposed to address this problem. Serving as a non-linear feature extracting layers in deep learning frameworks, the efficiency of the proposed morphological layer is confirmed analytically and empirically. With a known target, a single-filter morphological layer learns the structuring element correctly, and an adaptive layer can automatically select appropriate morphological operations. For high level applications, the proposed …

Confucian Robot Ethics, Qin Zhu, Tom Williams, Ruchen Wen

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

In the literature of artificial moral agents (AMAs), most work is influenced by either deontological or utilitarian frameworks. It has also been widely acknowledged that these Western “rule-based” ethical theories have encountered both philosophical and computing challenges. To tackle these challenges, this paper explores a non-Western, role-based, Confucian approach to robot ethics. In this paper, we start by providing a short introduction to some theoretical fundamentals of Confucian ethics. Then, we discuss some very preliminary ideas for constructing a Confucian approach to robot ethics. Lastly, we briefly share a couple of empirical studies our research group has recently conducted that …

Human Supremacy As Posthuman Risk, Daniel Estrada

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

Human supremacy is the widely held view that human interests ought to be privileged over other interests as a matter of public policy. Posthumanism is an historical and cultural situation characterized by a critical reevaluation of anthropocentrist theory and practice. This paper draws on Rosi Braidotti’s critical posthumanism and the critique of ideal theory in Charles Mills and Serene Khader to address the use of human supremacist rhetoric in AI ethics and policy discussions, particularly in the work of Joanna Bryson. This analysis leads to identifying a set of risks posed by human supremacist policy in a posthuman context, specifically …

Autonomous Vehicles And The Ethical Tension Between Occupant And Non-Occupant Safety, Jason Borenstein, Joseph Herkert, Keith W. Miller

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

Autonomous vehicle manufacturers, people inside an autonomous vehicle (occupants), and people outside the vehicle (non-occupants) are among the distinct stakeholders when addressing ethical issues inherent in systems that include autonomous vehicles. As responses to recent tragic cases illustrate, advocates for autonomous vehicles tend to focus on occupant safety, sometimes to the exclusion of non-occupant safety. Thus, we aim to examine ethical issues associated with non-occupant safety, including pedestrians, bicyclists, motorcyclists, and riders of motorized scooters. We also explore the ethical implications of technical and policy ideas that some might propose to improve non-occupant safety. In addition, if safety (writ large) …

Keeping Anonymity At The Consumer Behavior On The Internet: Proof Of Sacrifice, Sachio Horie

Computer Ethics - Philosophical Enquiry (CEPE) Proceedings

The evolution of the Internet and AI technology has made it possible for the government and the businesses to keep track of their personal lives. GAFA continues to collect information unintended by the individuals. It is a threat that our privacy is violated in this way. In order to solute such problems, it is important to consider a mechanism that enables us to be peaceful lives while protecting privacy in the Internet society.

This paper focuses on the consumption behavior on the Internet and addresses anonymity. We consider some network protocols that enable sustainable consensus by combining anonymity methods such …

Designing Single Guide Rnas For Crispr/Cas9, Neha Atul Bhagwat

Master's Projects

Researchers have been working towards development of tools to facilitate regular use genome engineering techniques. In recent years, the focus of these efforts has been the Clustered Regularly Interspaced Short Palindromic Repeats(CRISPR)/CRISPR associated(Cas) systems. These systems, while found naturally in bacteria and archaea as an immunity mechanism, can be used for genome engineering in eukaryotes.

There are three major computational challenges associated with the use of CRISPR/Cas9 in genome engineering for mammals - identification of CRISPR arrays, single guide RNA design and minimizing off-target effects. This project attempts to solve the problem of single guide RNA design using a novel …

Context-Based Multi-Stage Offline Handwritten Mathematical Symbol Recognition Using Deep Learning, Sui Kun Guan

Master's Projects

We propose a multi-stage machine learning (ML) architecture to improve the accuracy of offline handwritten mathematical symbol recognition. In the first stage, we train and assemble multiple deep convolutional neural networks to classify isolated mathematical symbols. However, certain ambiguous symbols are hard to classify without the context information of the mathematical expressions where the symbols belong. In the second stage, we train a deep convolutional neural network that further classifies the ambiguous symbols based on the context information of the symbols. To further improve the classification accuracy, in the third stage, we develop a set of rules to classify the …

Machine Learning In Crop Classification Of Temporal Multispectral Satellite Image, Ravali Koppaka

Master's Projects

Recently, there has been a remarkable growth in Artificial Intelligence (AI) with

the development of efficient AI models and high-power computational resources for processing complex datasets. There has been a growing number of applications of machine learning in satellite remote sensing image data processing. In this work, machine learning methods were applied for crop classification of temporal multi- spectral satellite image to achieve better prediction of crop-wise area statistics. In India, agriculture has a huge impact on the national economy and most of the critical decisions are dependent on agricultural statistics. Sentinel-2 satellite image data for the Guntur district region …