Open Access. Powered by Scholars. Published by Universities.®

Artificial Intelligence and Robotics Commons

Open Access. Powered by Scholars. Published by Universities.®

3,581 Full-Text Articles 5,239 Authors 1,290,840 Downloads 206 Institutions

All Articles in Artificial Intelligence and Robotics

Faceted Search

3,581 full-text articles. Page 7 of 162.

Risk-Aware Procurement Optimization In A Global Technology Supply Chain, Jonathan David CHASE, Jingfeng YANG, Hoong Chuin LAU 2022 Singapore Management University

Risk-Aware Procurement Optimization In A Global Technology Supply Chain, Jonathan David Chase, Jingfeng Yang, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Supply chain disruption, from ‘Black Swan’ events like the COVID-19 pandemic or the Russian invasion of Ukraine, to more ordinary issues such as labour disputes and adverse weather conditions, can result in delays, missed orders, and financial loss for companies that deliver products globally. Developing a risk-tolerant procurement strategy that anticipates the logistical problems incurred by disruption involves both accurate quantification of risk and cost-effective decision-making. We develop a supplier-focused risk evaluation metric that constrains a procurement optimization model for a global technology company. Our solution offers practical risk tolerance and cost-effectiveness, accounting for a range of constraints that realistically …


Singapore Public Sector Ai Applications Emphasizing Public Engagement: Six Examples, Steven MILLER 2022 Singapore Management University

Singapore Public Sector Ai Applications Emphasizing Public Engagement: Six Examples, Steven Miller

Research Collection School Of Computing and Information Systems

This article provides an overview of six examples of public sector AI applications in Singapore that illustrate different ways of enhancing engagement with the public. These applications demonstrate ways of enhancing engagement with the public by providing greater accessibility to government services (access anywhere, anytime) and speedier responses to public processes and feedback. Some applications make it substantially easier for members of the public to do things or make choices, while others reduce waiting time, either across an entire public infrastructure, or for an individual transaction. Some provide highly individualized coaching to guide a person through the process of doing …


Deep Learning-Based Text Recognition Of Agricultural Regulatory Document, Hua Leong FWA, Farn Haur CHAN 2022 Singapore Management University

Deep Learning-Based Text Recognition Of Agricultural Regulatory Document, Hua Leong Fwa, Farn Haur Chan

Research Collection School Of Computing and Information Systems

In this study, an OCR system based on deep learning techniques was deployed to digitize scanned agricultural regulatory documents comprising of certificates and labels. Recognition of the certificatesand labels is challenging as they are scanned images of the hard copy form and the layout and size of the text as well as the languages vary between the various countries (due to diverse regulatory requirements). Weevaluated and compared between various state-of-the-art deep learningbased text detection and recognition model as well as a packaged OCR library – Tesseract. We then adopted a two-stage approach comprisingof text detection using Character Region Awareness For …


Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing LING, Arambam James SINGH, Nguyen Duc THIEN, Akshat KUMAR 2022 Singapore Management University

Constrained Multiagent Reinforcement Learning For Large Agent Population, Jiajing Ling, Arambam James Singh, Nguyen Duc Thien, Akshat Kumar

Research Collection School Of Computing and Information Systems

Learning control policies for a large number of agents in a decentralized setting is challenging due to partial observability, uncertainty in the environment, and scalability challenges. While several scalable multiagent RL (MARL) methods have been proposed, relatively few approaches exist for large scale constrained MARL settings. To address this, we first formulate the constrained MARL problem in a collective multiagent setting where interactions among agents are governed by the aggregate count and types of agents, and do not depend on agents’ specific identities. Second, we show that standard Lagrangian relaxation methods, which are popular for single agent RL, do not …


Influence Level Prediction On Social Media Through Multi-Task And Sociolinguistic User Characteristics Modeling, Denys Katerenchuk 2022 The Graduate Center, City University of New York

Influence Level Prediction On Social Media Through Multi-Task And Sociolinguistic User Characteristics Modeling, Denys Katerenchuk

Dissertations, Theses, and Capstone Projects

Prediction of a user’s influence level on social networks has attracted a lot of attention as human interactions move online. Influential users have the ability to influence others’ behavior to achieve their own agenda. As a result, predicting users’ level of influence online can help to understand social networks, forecast trends, prevent misinformation, etc. The research on user influence in social networks has attracted much attention across multiple disciplines, from social sciences to mathematics, yet it is still not well understood. One of the difficulties is that the definition of influence is specific to a particular problem or a domain, …


Finite Gaussian Neurons: Defending Against Adversarial Attacks By Making Neural Networks Say "I Don’T Know", Felix Grezes 2022 The Graduate Center, City University of New York

Finite Gaussian Neurons: Defending Against Adversarial Attacks By Making Neural Networks Say "I Don’T Know", Felix Grezes

Dissertations, Theses, and Capstone Projects

In this work, I introduce the Finite Gaussian Neuron (FGN), a novel neuron architecture for artificial neural networks aimed at protecting against adversarial attacks.
Since 2014, artificial neural networks have been known to be vulnerable to adversarial attacks, which can fool the network into producing wrong or nonsensical outputs by making humanly imperceptible alterations to inputs. While defenses against adversarial attacks have been proposed, they usually involve retraining a new neural network from scratch, a costly task.

My works aims to:
- easily convert existing models to Finite Gaussian Neuron architecture,
- while preserving the existing model's behavior on real …


Truncated Matrix Power Iteration For Differentiable Dag Learning, Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M. Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi 2022 The University of Adelaide, Australia

Truncated Matrix Power Iteration For Differentiable Dag Learning, Zhen Zhang, Ignavier Ng, Dong Gong, Yuhang Liu, Ehsan M. Abbasnejad, Mingming Gong, Kun Zhang, Javen Qinfeng Shi

Machine Learning Faculty Publications

Recovering underlying Directed Acyclic Graph structures (DAG) from observational data is highly challenging due to the combinatorial nature of the DAG-constrained optimization problem. Recently, DAG learning has been cast as a continuous optimization problem by characterizing the DAG constraint as a smooth equality one, generally based on polynomials over adjacency matrices. Existing methods place very small coefficients on high-order polynomial terms for stabilization, since they argue that large coefficients on the higher-order terms are harmful due to numeric exploding. On the contrary, we discover that large coefficients on higher-order terms are beneficial for DAG learning, when the spectral radiuses of …


Exploiting Higher-Order Derivatives In Convex Optimization Methods, Dmitry Kamzolov, Alexander Gasnikov, Pavel Dvurechensky, Artem Agafonov, Martin Takac 2022 Mohamed bin Zayed University of Artificial Intelligence

Exploiting Higher-Order Derivatives In Convex Optimization Methods, Dmitry Kamzolov, Alexander Gasnikov, Pavel Dvurechensky, Artem Agafonov, Martin Takac

Machine Learning Faculty Publications

Exploiting higher-order derivatives in convex optimization is known at least since 1970’s. In each iteration higher-order (also called tensor) methods minimize a regularized Taylor expansion of the objective function, which leads to faster convergence rates if the corresponding higher-order derivative is Lipschitz-continuous. Recently a series of lower iteration complexity bounds for such methods were proved, and a gap between upper an lower complexity bounds was revealed. Moreover, it was shown that such methods can be implementable since the appropriately regularized Taylor expansion of a convex function is also convex and, thus, can be minimized in polynomial time. Only very recently …


Sr-Dcsk Cooperative Communication System With Code Index Modulation: A New Design For 6g New Radios, Yi Fang, Wang Chen, Pingping Chen, Yiwei Tao, Mohsen Guizani 2022 School of Information Engineering, Guangdong University of Technology, Guangzhou, China

Sr-Dcsk Cooperative Communication System With Code Index Modulation: A New Design For 6g New Radios, Yi Fang, Wang Chen, Pingping Chen, Yiwei Tao, Mohsen Guizani

Machine Learning Faculty Publications

This paper proposes a high-throughput short reference differential chaos shift keying cooperative communication system with the aid of code index modulation, referred to as CIM-SR-DCSK-CC system. In the proposed CIM-SR-DCSK-CC system, the source transmits information bits to both the relay and destination in the first time slot, while the relay not only forwards the source information bits but also sends new information bits to the destination in the second time slot. To be specific, the relay employs an N-order Walsh code to carry additional log2N information bits, which are superimposed onto the SR-DCSK signal carrying the decoded source information bits. …


Towards The Development Of A Cost-Effective Image-Sensing-Smart-Parking Systems (Isensmap), Aakriti Sharma 2022 The University of Western Ontario

Towards The Development Of A Cost-Effective Image-Sensing-Smart-Parking Systems (Isensmap), Aakriti Sharma

Electronic Thesis and Dissertation Repository

Finding parking in a busy city has been a major daily problem in today’s busy life. Researchers have proposed various parking spot detection systems to overcome the problem of spending a long time searching for a parking spot. These works include a wide variety of sensors to detect the presence of a vehicle in a parking spot. These approaches are expensive to implement and ineffective in extreme weather conditions in an outdoor parking environment. As a result, a cost-effective, dependable, and time-saving parking solution is much more desirable. In this thesis, we proposed and developed an image processing-based real-time parking-spot …


Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti 2022 The University of Western Ontario

Respiratory Pattern Analysis For Covid-19 Digital Screening Using Ai Techniques, Annita Tahsin Priyoti

Electronic Thesis and Dissertation Repository

Corona Virus (COVID-19) is a highly contagious respiratory disease that the World Health Organization (WHO) has declared a worldwide epidemic. This virus has spread worldwide, affecting various countries until now, causing millions of deaths globally. To tackle this public health crisis, medical professionals and researchers are working relentlessly, applying different techniques and methods. In terms of diagnosis, respiratory sound has been recognized as an indicator of one’s health condition. Our work is based on cough sound analysis. This study has included an in-depth analysis of the diagnosis of COVID-19 based on human cough sound. Based on cough audio samples from …


Interpreting Song Lyrics With An Audio-Informed Pre-Trained Language Model, Yixiao Zhang, Junyan Jiang, Gus Xia, Simon Dixon 2022 Centre for Digital Music, Queen Mary University of London, United Kingdom

Interpreting Song Lyrics With An Audio-Informed Pre-Trained Language Model, Yixiao Zhang, Junyan Jiang, Gus Xia, Simon Dixon

Machine Learning Faculty Publications

Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mass of music archives. In this paper we propose BART-fusion, a novel model for generating lyric interpretations from lyrics and music audio that combines a large-scale pre-trained language model with an audio encoder. We employ a cross-modal attention module to incorporate the audio representation into the lyrics representation to help the pre-trained language model understand the song from an audio perspective, while preserving the language model’s original generative performance. We also release the …


Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem 2022 The University of Western Ontario

Exploring Artificial Intelligence (Ai) Techniques For Forecasting Network Traffic: Network Qos And Security Perspectives, Ibrahim Mohammed Sayem

Electronic Thesis and Dissertation Repository

This thesis identifies the research gaps in the field of network intrusion detection and network QoS prediction, and proposes novel solutions to address these challenges. Our first topic presents a novel network intrusion detection system using a stacking ensemble technique using UNSW-15 and CICIDS-2017 datasets. In contrast to earlier research, our proposed novel network intrusion detection techniques not only determine if the network traffic is benign or normal, but also reveal the type of assault in the flow. Our proposed stacking ensemble model provides a more effective detection capability than the existing works. Our proposed stacking ensemble technique can detect …


Fdrl Approach For Association And Resource Allocation In Multi-Uav Air-To-Ground Iomt Network, Abegaz Mohammed, Aiman Erbad, Hayla Nahom, Abdullatif Albaseer, Mohammed Abdallah, Mohsen Guizani 2022 Division of Information and Computing Technology, College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar

Fdrl Approach For Association And Resource Allocation In Multi-Uav Air-To-Ground Iomt Network, Abegaz Mohammed, Aiman Erbad, Hayla Nahom, Abdullatif Albaseer, Mohammed Abdallah, Mohsen Guizani

Machine Learning Faculty Publications

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients’ data with the limited UAV resources is a major challenge. In this paper, …


Reconfigurable Intelligent Surfaces And Capacity Optimization: A Large System Analysis, Aris L. Moustakas, George C. Alexandropoulos, Mérouane Debbah 2022 The Department of Physics, National and Kapodistrian University of Athens, Athens, 15784, Greece

Reconfigurable Intelligent Surfaces And Capacity Optimization: A Large System Analysis, Aris L. Moustakas, George C. Alexandropoulos, Mérouane Debbah

Machine Learning Faculty Publications

Reconfigurable Intelligent Surfaces (RISs), comprising large numbers of low-cost and almost passive metamaterials with tunable reflection properties, have been recently proposed as an enabling technology for programmable wireless propagation environments. In this paper, we present asymptotic closed-form expressions for the mean and variance of the mutual information metric for a multi-antenna transmitter-receiver pair in the presence of multiple RISs, using methods from statistical physics. While nominally valid in the large system limit, we show that the derived Gaussian approximation for the mutual information can be quite accurate, even for modest-sized antenna arrays and metasurfaces. The above results are particularly useful …


Reporting Standards For Machine Learning Research In Type 2 Diabetes, Grace Kang 2022 Western University

Reporting Standards For Machine Learning Research In Type 2 Diabetes, Grace Kang

Undergraduate Student Research Internships Conference

In this project, three people scored 90 papers on machine learning predictive models for type 2 diabetes to assess their adherence to TRIPOD, MI-CLAIM, and DOME reporting guidelines.


Damage Assessment In Aging Structures Using Augmented Reality, Omar Zuhair Awadallah, Ayan Sadhu 2022 Western University

Damage Assessment In Aging Structures Using Augmented Reality, Omar Zuhair Awadallah, Ayan Sadhu

Undergraduate Student Research Internships Conference

Structural Health Monitoring (SHM) is the assessment of bridges and observation of data regarding these bridges over time to monitor their evolution and detect the presence of any possible damages. However, existing methods to perform structural inspections in bridges are high in cost, time-consuming and risky. Inspectors use expensive equipment to reach a certain area of the bridge to inspect it, and at different heights, this can pose a risk to the inspector’s safety. This study aims to find cheaper, faster, and safer ways to perform structural inspections using augmented reality and artificial intelligence. The developed system uses a machine …


Augmented Creativity: Leveraging Natural Language Processing For Creative Writing, Daniel Plate, James Hutson 2022 Lindenwood University

Augmented Creativity: Leveraging Natural Language Processing For Creative Writing, Daniel Plate, James Hutson

Faculty Scholarship

Recent advances have moved natural language processing (NLP) capabilities with artificial intelligence beyond mere grammar and spell-checking functionality. One such new use that has arisen is the ability to suggest new content to writers to inspire new ideas by using “machine-in-the-loop” strategies in creative writing. In order to explore the possibilities of such a strategy, this study provides a model to be adopted in creative writing courses in higher education. An NLP application was created using Python and spaCy and deployed via Streamlit. The AI allowed students to see if their grammar aligned with those principles and techniques taught in …


Avist: A Benchmark For Visual Object Tracking In Adverse Visibility, Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer, Hisham Cholakkal, Salman Khan, Luc Van Gool, Fahad Shahbaz Khan 2022 Mohamed bin Zayed University of Artificial Intelligence

Avist: A Benchmark For Visual Object Tracking In Adverse Visibility, Mubashir Noman, Wafa Al Ghallabi, Daniya Najiha, Christoph Mayer, Hisham Cholakkal, Salman Khan, Luc Van Gool, Fahad Shahbaz Khan

Computer Vision Faculty Publications

One of the key factors behind the recent success in visual tracking is the availability of dedicated benchmarks. While being greatly benefiting to the tracking research, existing benchmarks do not pose the same difficulty as before with recent trackers achieving higher performance mainly due to (i) the introduction of more sophisticated transformers-based methods and (ii) the lack of diverse scenarios with adverse visibility such as, severe weather conditions, camouflage and imaging effects. We introduce AVisT, a dedicated benchmark for visual tracking in diverse scenarios with adverse visibility. AVisT comprises 120 challenging sequences with 80k annotated frames, spanning 18 diverse scenarios …


Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn 2022 Mississippi State University

Classification Models For 2,4-D Formulations In Damaged Enlist Crops Through The Application Of Ftir Spectroscopy And Machine Learning Algorithms, Benjamin Blackburn

Theses and Dissertations

With new 2,4-Dichlorophenoxyacetic acid (2,4-D) tolerant crops, increases in off-target movement events are expected. New formulations may mitigate these events, but standard lab techniques are ineffective in identifying these 2,4-D formulations. Using Fourier-transform infrared spectroscopy and machine learning algorithms, research was conducted to classify 2,4-D formulations in treated herbicide-tolerant soybeans and cotton and observe the influence of leaf treatment status and collection timing on classification accuracy. Pooled Classification models using k-nearest neighbor classified 2,4-D formulations with over 65% accuracy in cotton and soybean. Tissue collected 14 DAT and 21 DAT for cotton and soybean respectively produced higher accuracies than the …


Digital Commons powered by bepress