Artificial Intelligence and Robotics Commons^™

Securing The Emerging Technologies Of Autonomous And Connected Vehicles, Shahab Tayeb, Matin Pirouz

Mineta Transportation Institute

The Internet of Vehicles (IoV) aims to establish a network of autonomous and connected vehicles that communicate with one another through facilitation led by road-side units (RSUs) and a central trust authority (TA). Messages must be efficiently and securely disseminated to conserve resources and preserve network security. Currently, research in this area lacks consensus about security schemes and methods of disseminating messages. Furthermore, a current deficiency of information regarding resource optimization prevents further efficient development of this network. This paper takes an interdisciplinary approach to these issues by merging both cybersecurity and data science to optimize and secure the network. …

A Feel For The Game: Ai, Computer Games And Perceiving Perception, Marc A. Ouellette, Steven Conway

English Faculty Publications

I walk into the room and the smell of burning wood hits me immediately. The warmth from the fireplace grows as I step nearer to it. The fire needs to heat the little cottage through the night so I add a log to the fire. There are a few sparks and embers. I throw a bigger log onto the fire and it drops with a thud. Again, there are barely any sparks or embers. The heat and the smell stay the same. They don’t change and I do not become habituated to it. Rather, they are just a steady stream, …

A Cue Adaptive Decoder For Controllable Neural Response Generation, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang

Research Collection School Of Computing and Information Systems

In open-domain dialogue systems, dialogue cues such as emotion, persona, and emoji can be incorporated into conversation models for strengthening the semantic relevance of generated responses. Existing neural response generation models either incorporate dialogue cue into decoder’s initial state or embed the cue indiscriminately into the state of every generated word, which may cause the gradients of the embedded cue to vanish or disturb the semantic relevance of generated words during back propagation. In this paper, we propose a Cue Adaptive Decoder (CueAD) that aims to dynamically determine the involvement of a cue at each generation step in the decoding. …

A Semi-Automatic Integrated Framework For Non-English Sentiment Lexicons, Abdullah Kaity Mohammed Salem

Student Works (2020-2029)

There has been significant growth in social media networks in the last few years. Posting opinions and messages on social networking websites has become a popular activity on the Internet. The data sources are necessary for business intelligence and market analytics, as human opinions form a major indicator of human desires and behaviour. This has resulted in the development of a new study field called sentiment analysis. This includes the analysis, evaluation and interpretation of the opinions with the help of text mining and Natural Language Processing (NLP) processes, for identifying the text polarity, as positive, neutral or negative. It …

Multi-Tier Classification Based On Sentiment, Type, Emotion And Purpose For Online Diabetes Community, Ratan Singh Wandeep Kaur

Student Works (2020-2029)

The evolution of social media platforms has created a niche for users to increasingly turn to such sites in order to share and exchange health related information. Facebook being one of the largest social networking sites has only encouraged such exchange thus mounting to a sheer amount of data that is hidden within unstructured text. The aim of this research is to propose a multi-tier classification based on sentiment, type, emotion and purpose (STEP) to classify data collected from diabetes community within Facebook. There are three tiers within the proposed STEP framework namely type, purpose and sentiment (and emotion within …

Applications Of Machine Learning To Threat Intelligence, Intrusion Detection And Malware, Charity Barker

Senior Honors Theses

Artificial Intelligence (AI) and Machine Learning (ML) are emerging technologies with applications to many fields. This paper is a survey of use cases of ML for threat intelligence, intrusion detection, and malware analysis and detection. Threat intelligence, especially attack attribution, can benefit from the use of ML classification. False positives from rule-based intrusion detection systems can be reduced with the use of ML models. Malware analysis and classification can be made easier by developing ML frameworks to distill similarities between the malicious programs. Adversarial machine learning will also be discussed, because while ML can be used to solve problems or …

Recipegpt: Generative Pre-Training Based Cooking Recipe Generation And Evaluation System, Helena Huey Chong Lee, Ke Shu, Palakorn Achananuparp, Philips Kokoh Prasetyo, Yue Liu, Ee-Peng Lim, Lav R. Varshney

Research Collection School Of Computing and Information Systems

Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes. We present RecipeGPT, a novel online recipe generation and evaluation system. The system provides two modes of text generations: (1) instruction generation from given recipe title and ingredients; and (2) ingredient generation from recipe title and cooking instructions. Its back-end text generation module comprises a generative pre-trained language model GPT-2 fine-tuned on a large cooking recipe dataset. Moreover, the recipe evaluation module allows the users to conveniently inspect the quality of the generated recipe …

Keeping Ai Under Observation: Anticipated Impacts On Physicians' Standard Of Care, Iria Giuffrida, Taylor Treece

Faculty Publications

As Artificial Intelligence (AI) tools become increasingly present across industries, concerns have started to emerge as to their impact on professional liability. Specifically, for the medical industry--in many ways an inherently "risky" business--hospitals and physicians have begun evaluating the impact of Al tools on their professional malpractice risk. This Essay seeks to address that question, zooming in on how AI may affect physicians' standard of care for medical malpractice claims.

Artificial Stupidity, Clark D. Asay

William & Mary Law Review

Artificial intelligence is everywhere. And yet, the experts tell us, it is not yet actually anywhere. This is because we are yet to achieve artificial general intelligence, or artificially intelligent systems that are capable of thinking for themselves and adapting to their circumstances. Instead, all the AI hype—and it is constant—concerns narrower, weaker forms of artificial intelligence, which are confined to performing specific, narrow tasks. The promise of true artificial general intelligence thus remains elusive. Artificial stupidity reigns supreme.

What is the best set of policies to achieve more general, stronger forms of artificial intelligence? Surprisingly, scholars have paid little …

Artificial Intelligence-Enhanced Predictive Insights For Advancing Financial Inclusion: A Human-Centric Ai-Thinking Approach, Meng Leong How, Sin Mei Cheah, Aik Cheow Khor, Yong Jiet Chan

Research Collection Lee Kong Chian School Of Business

According to the World Bank, a key factor to poverty reduction and improving prosperity is financial inclusion. Financial service providers (FSPs) offering financially-inclusive solutions need to understand how to approach the underserved successfully. The application of artificial intelligence (AI) on legacy data can help FSPs to anticipate how prospective customers may respond when they are approached. However, it remains challenging for FSPs who are not well-versed in computer programming to implement AI projects. This paper proffers a no-coding human-centric AI-based approach to simulate the possible dynamics between the financial profiles of prospective customers collected from 45,211 contact encounters and predict …

Artificial Intelligence (Ai) Ethics: Ethics Of Ai And Ethical Ai, Keng Siau, Weiyu Wang

Research Collection School Of Computing and Information Systems

Artificial intelligence (AI)-based technology has achieved many great things, such as facial recognition, medical diagnosis, and self-driving cars. AI promises enormous benefits for economic growth, social development, as well as human well-being and safety improvement. However, the low-level of explainability, data biases, data security, data privacy, and ethical problems of AI-based technology pose significant risks for users, developers, humanity, and societies. As AI advances, one critical issue is how to address the ethical and moral challenges associated with AI. Even though the concept of “machine ethics” was proposed around 2006, AI ethics is still in the infancy stage. AI ethics …

Neural Network Pruning For Ecg Arrhythmia Classification, Isaac E. Labarge

Master's Theses

Convolutional Neural Networks (CNNs) are a widely accepted means of solving complex classification and detection problems in imaging and speech. However, problem complexity often leads to considerable increases in computation and parameter storage costs. Many successful attempts have been made in effectively reducing these overheads by pruning and compressing large CNNs with only a slight decline in model accuracy. In this study, two pruning methods are implemented and compared on the CIFAR-10 database and an ECG arrhythmia classification task. Each pruning method employs a pruning phase interleaved with a finetuning phase. It is shown that when performing the scale-factor pruning …

Truck Trailer Classification Using Side-Fire Light Detection And Ranging (Lidar) Data, Olcay Sahin

Civil & Environmental Engineering Theses & Dissertations

Classification of vehicles into distinct groups is critical for many applications, including freight and commodity flow modeling, pavement management and design, tolling, air quality monitoring, and intelligent transportation systems. The Federal Highway Administration (FHWA) developed a standardized 13-category vehicle classification ruleset, which meets the needs of many traffic data user applications. However, some applications need high-resolution data for modeling and analysis. For example, the type of commodity being carried must be known in the freight modeling framework. Unfortunately, this information is not available at the state or metropolitan level, or it is expensive to obtain from current resources.

Nevertheless, using …

Functional Object-Oriented Network: A Knowledge Representation For Service Robotics, David Andrés Paulius Ramos

USF Tampa Graduate Theses and Dissertations

In this dissertation, we discuss our work behind the development of the functional object-oriented network (abbreviated as FOON), a graphical knowledge representation for robotic manipulation and understanding of its own actions and (potentially) the intentions of humans in the household. Based on the theory of affordance, this representation captures manipulations and their effects on actions through the coupling of object and motion nodes as fundamental learning units known as functional units. The activities currently represented in FOON are cooking related, but this representation can be extended to other activities that involve manipulation of objects which result in observable changes of …

Automatic Flood Detection In Sentinei-2 Images Using Deep Convolutional Neural Networks, Pallavi Jain, Bianca Schoen-Phelan, Robert J. Ross

Conference papers

The early and accurate detection of floods from satellite imagery can aid rescue planning and assessment of geophysical damage. Automatic identification of water from satellite images has historically relied on hand-crafted functions, but these often do not provide the accuracy and robustness needed for accurate and early flood detection. To try to overcome these limitations we investigate a tiered methodology combining water index like features with a deep convolutional neural network based solution to flood identification against the MediaEval 2019 flood dataset. Our method builds on existing deep neural network methods, and in particular the VGG16 network. Specifically, we explored …

Extracting Range Data From Images Using Focus Error, Erik M. Madden

Theses and Dissertations

Air-to-air refueling (AAR) has become a staple when performing long missions with aircraft. With modern technology, however, people have begun to research how to perform this task autonomously. Automated air-to-air refueling (A3R) is this exact concept. Combining many different systems, the idea is to allow computers on the aircraft to link up via the refueling boom, refuel, and detach before resuming pilot control. This document lays out one of the systems that is needed to perform A3R, namely, the system that extracts range data. While stereo cameras perform such tasks, there is interest in finding other ways of accomplishing the …

Object Detection With Deep Learning To Accelerate Pose Estimation For Automated Aerial Refueling, Andrew T. Lee

Theses and Dissertations

Remotely piloted aircraft (RPAs) cannot currently refuel during flight because the latency between the pilot and the aircraft is too great to safely perform aerial refueling maneuvers. However, an AAR system removes this limitation by allowing the tanker to directly control the RP A. The tanker quickly finding the relative position and orientation (pose) of the approaching aircraft is the first step to create an AAR system. Previous work at AFIT demonstrates that stereo camera systems provide robust pose estimation capability. This thesis first extends that work by examining the effects of the cameras' resolution on the quality of pose …

System Methodology Of Digital Transformation In Military Manufacturing Industry, Daolin Yu, Wenhai Zhu, Qing Xiao, Guoqiang Shi

Journal of System Simulation

Abstract: Focusing on the lack of the understanding and the solutions to the digital transformation of the military manufacturing enterprises and service providers, the background of the digital transformation in the military manufacturing industry is analyzed. By studying the advanced manufacturing models, the core competitiveness, the value chains and the production (productivity) factors, the (generalization) goals of the digital transformation in the military manufacturing industry are identified, and some urgent problems and the suggestions to solve the complex system problems are provided. The digital transformation in the military manufacturing industry is a long-term strategic action and should be continuously optimized …

Wind Power Generation Hardware-In-Loop Simulation System Based On Rapid Control Prototype Technology, Aihua Wu, Buhui Zhao, Jingfeng Mao, Haiqun Shen, Xudong Zhang

Journal of System Simulation

Abstract: In order to improve the efficiency and effectiveness in the design and testing process of the wind power generation MPPT controller, a rapid control prototype (RCP) system based on the LabVIEW FPGA platform is proposed. The real-time simulation model of wind speed, wind turbine and PMSG, as well as MPPT rapid control prototyping are designed by using PXI-FPGA architecture on the LabVIEW RT real-time operation platform. The power converter, the real-time simulation model and the MPPT rapid control prototyping are connected together to construct a hardware-in-loop (HIL) test system. The simulation results on the gradient wind speed condition and …

Emergency Evacuation Simulation For Dense Passenger Flow In A Rail Transit Transfer Station, Xu Hui, Tian Cheng, Wang Yong

Journal of System Simulation

Abstract: The rail transit is one of the important modes of the public transportation, and its safety operation is crucial. Based on the passenger volume and structure features that have been obtained through the field investigation in Chongqing Lianglukou railway station in China, a multi-level rail transit transfer station simulation model is built to analyze the evacuation process of pedestrians in the consideration of explosion and other emergencies by the software AnyLogic. The evacuation routes and evacuation time of passengers during emergency evacuation in a rail transit transfer station are studied. The research aims at verifying the emergency evacuation capacity …