Artificial Intelligence and Robotics Commons^™

Drug Repositioning Based On Bounded Nuclear Norm Regularization, Mengyun Yang, Huimin Lao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Computational drug repositioning is a cost-effective strategy to identify novel indications for existing drugs. Drug repositioning is often modeled as a recommendation system problem. Taking advantage of the known drug–disease associations, the objective of the recommendation system is to identify new treatments by filling out the unknown entries in the drug–disease association matrix, which is known as matrix completion. Underpinned by the fact that common molecular pathways contribute to many different diseases, the recommendation system assumes that the underlying latent factors determining drug–disease associations are highly correlated. In other words, the drug–disease matrix to be completed is low-rank. Accordingly, …

Electroencephalogram (Eeg) For Delineating Objective Measure Of Autism Spectrum Disorder, Sampath Jayarathna, Yasith Jayawardana, Mark Jaime, Sashi Thapaliya

Computer Science Faculty Publications

Autism spectrum disorder (ASD) is a developmental disorder that often impairs a child's normal development of the brain. According to CDC, it is estimated that 1 in 6 children in the US suffer from development disorders, and 1 in 68 children in the US suffer from ASD. This condition has a negative impact on a person's ability to hear, socialize, and communicate. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. EEG measures the …

Living With Artificial Intelligence – Developing A Theory On Trust In Health Chatbots, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

The current world is AI-filled and AI-fueled. Humans need to be able to live in harmony with AI. In this research, we aim to develop a theory on Trust between human and health chatbots. With the development of intelligent personal assistants, chatbots are becoming common and ubiquitous. Chatbots can behave as a conversational partner, complete information acquisition, and provide responses to inquiries. Chatbots have been widely used in the healthcare area, supporting physicians and assisting patients. The potential threats to privacy issues and the unpredictable performances of the chatbots hindered people’s trust and adoption of the new technology. This research-in-progress …

Emotion Recognition Using Deep Convolutional Neural Network With Large Scale Physiological Data, Astha Sharma

USF Tampa Graduate Theses and Dissertations

Classification of emotions plays a very important role in affective computing and has real-world applications in fields as diverse as entertainment, medical, defense, retail, and education. These applications include video games, virtual reality, pain recognition, lie detection, classification of Autistic Spectrum Disorder (ASD), analysis of stress levels, and determining attention levels. This vast range of applications motivated us to study automatic emotion recognition which can be done by using facial expression, speech, and physiological data.

A person’s physiological signals such are heart rate, and blood pressure are deeply linked with their emotional states and can be used to identify a …

Trusting Artificial Intelligence In Healthcare, W. Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) is able to perform at humans and even surpass human’s performances in some tasks. Recent cases about self-driving cars, cashier-free supermarket Amazon Go, and virtual assistants such as Apple’s Siri and Google Assistant have illustrated the current and future potential of AI. AI and its applications have infiltrated human’s work and daily life. It is inevitable that humans need to build a working relationship with AI and its applications. On one hand, humans can benefit from this new technology, for instance, a home robot can release housewife from mundane and monotonous tasks (Siau 2017, Siau 2018). On …

Reserved Optimisation: Handling Incident Priorities In Emergency Response Systems, Muralidhar Konda, Supriyo Ghosh, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Emergency (medical, fire or criminal) Management Systems(EMSs) are crucial for ensuring public safety and security. Typically in many cities, less than 20% of the cases received by EMSs belong to the extremely serious category and require immediate help. Rest of the incidents typically are less serious and thereby allow more flexibility in response time. Therefore, for efficient management of EMS requests, several EMSs now categorise an incoming emergency request into apriority level based on well studied “triaging” methods. Leading research on optimising emergency response has either focussed on data-driven models for settings with homogenous incidents or on generic heuristics (that …

Similarity Based Classification Of Adhd Using Singular Value Decomposition, Taban Eslami, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Attention deficit hyperactivity disorder (ADHD) is one of the most common brain disorders among children. This disorder is considered as a big threat for public health and causes attention, focus and organizing difficulties for children and even adults. Since the cause of ADHD is not known yet, data mining algorithms are being used to help discover patterns which discriminate healthy from ADHD subjects. Numerous efforts are underway with the goal of developing classification tools for ADHD diagnosis based on functional and structural magnetic resonance imaging data of the brain. In this paper, we used Eros, which is a technique for …

Towards Using A Physio-Cognitive Model In Tutoring For Psychomotor Tasks., Jong W. Kim, Chris Dancy, Robert A. Sottilare

Faculty Conference Papers and Presentations

We report our exploratory research of psychomotor task training in intelligent tutoring systems (ITSs) that are generally limited to tutoring in the desktop learning environment where the learner acquires cognitively oriented knowledge and skills. It is necessary to support computer-guided training in a psychomotor task domain that is beyond the desktop environment. In this study, we seek to extend the current capability of GIFT (Generalized Intelligent Frame-work for Tutoring) to address these psychomotor task training needs. Our ap-proach is to utilize heterogeneous sensor data to identify physical motions through acceleration data from a smartphone and to monitor respiratory activity through …

Towards A Physio-Cognitive Model Of Slow-Breathing, Chris Dancy

Faculty Conference Papers and Presentations

How may controlled breathing be beneficial, or detrimental to behavior? Computational process models are useful to specify the potential mechanisms that lead to behavioral adaptation during different breathing exercises. We present a physio-cognitive model of slow breathing implemented within a hybrid cognitive architecture, ACT-R/Φ. Comparisons to data from an experiment indicate that the physiological mechanisms are operating in a manner that is consistent with actual human function. The presented computational model provides predictions of ways that controlled breathing interacts with mechanisms of arousal to mediate cognitive behavior. The increasing use of breathing techniques to counteract effects of stressors makes it …

Deep Convolutional Neural Networks Enable Discrimination Of Heterogeneous Digital Pathology Images, Pegah Khosravi, Ehsan Kazemi, Marcin Imielinski, Olivier Elemento, Iman Hajirasouliha

Publications and Research

Pathological evaluation of tumor tissue is pivotal for diagnosis in cancer patients and automated image analysis approaches have great potential to increase precision of diagnosis and help reduce human error.

In this study, we utilize several computational methods based on convolutional neural networks (CNN) and build a stand-alone pipeline to effectively classify different histopathology images across different types of cancer.

In particular, we demonstrate the utility of our pipeline to discriminate between two subtypes of lung cancer, four biomarkers of bladder cancer, and five biomarkers of breast cancer. In addition, we apply our pipeline to discriminate among four immunohistochemistry …

Using The Qbest Equation To Evaluate Ellagic Acid Safety Data: Generating A Qnoael With Confidence Levels From Disparate Literature, Cynthia Rose Dickerson

Theses and Dissertations--Pharmacy

QBEST, a novel statistical method, can be applied to the problem of estimating the No Observed Adverse Effect Level (NOAEL or QNOAEL) of a New Molecular Entity (NME) in order to anticipate a safe starting dose for beginning clinical trials. The NOAEL from QBEST (called the QNOAEL) can be calculated using multiple disparate studies in the literature and/or from the lab. The QNOAEL is similar in some ways to the Benchmark Dose Method (BMD) used widely in toxicological research, but is superior to the BMD in some ways. The QNOAEL simulation generates an intuitive curve that is comparable to the …

Scalable Feature Selection And Extraction With Applications In Kinase Polypharmacology, Derek Jones

Theses and Dissertations--Computer Science

In order to reduce the time associated with and the costs of drug discovery, machine learning is being used to automate much of the work in this process. However the size and complex nature of molecular data makes the application of machine learning especially challenging. Much work must go into the process of engineering features that are then used to train machine learning models, costing considerable amounts of time and requiring the knowledge of domain experts to be most effective. The purpose of this work is to demonstrate data driven approaches to perform the feature selection and extraction steps in …

Minimally Actuated Walking: Identifying Core Challenges To Economical Legged Locomotion Reveals Novel Solutions, Ryan T. Schroeder, John Ea Bertram

Research outputs 2014 to 2021

Terrestrial organisms adept at locomotion employ strut-like legs for economical and robust movement across the substrate. Although it is relatively easy to observe and analyze details of the solutions these organic systems have arrived at, it is not as easy to identify the problems these movement strategies have solved. As such, it is useful to investigate fundamental challenges that effective legged locomotion overcomes in order to understand why the mechanisms employed by biological systems provide viable solutions to these challenges. Such insight can inform the design and development of legged robots that may eventually match or exceed animal performance. In …

Leveraging Artificial Intelligence To Improve Provider Documentation In Patient Medical Records, Evangeline C. Ozurigbo

Walden Dissertations and Doctoral Studies

Clinical documentation is at the center of a patient's medical record; this record contains all the information applicable to the care a patient receives in the hospital. The practice problem addressed in this project was the lack of clear, consistent, accurate, and complete patient medical records in a pediatric hospital. Although the occurrence of incomplete medical records has been a known issue for the project hospital, the issue was further intensified following the implementation of the 10th revision of International Classification of Diseases (ICD-10) standard for documentation, which resulted in gaps in provider documentation that needed to be filled. Based …

The Ability Of Different Imputation Methods To Preserve The Significant Genes And Pathways In Cancer, Rosa Aghdam, Taban Baghfalaki, Pegah Khosravi, Elnaz Saberi Ansari

Publications and Research

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase …

Home Health Care Delivery Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

We address the Home Health Care Delivery Problem (HHCDP), which is concerned with staff scheduling in the home health care industry. The goal is to schedule health care providers to serve patients at their homes that maximizes the total collected preference scores from visited patients subject to several constraints, such as workload of the health care providers, time budget for each provider and so on. The complexity lies in the possibility of cancellation of patient bookings dynamically, and the generated schedule should attempt to patients’ preferred time windows. To cater to these requirements, we model the preference score as a …

Context-Based Human Activity Recognition Using Multimodal Wearable Sensors, Pratool Bharti

USF Tampa Graduate Theses and Dissertations

In the past decade, Human Activity Recognition (HAR) has been an important part of the regular day to day life of many people. Activity recognition has wide applications in the field of health care, remote monitoring of elders, sports, biometric authentication, e-commerce and more. Each HAR application needs a unique approach to provide solutions driven by the context of the problem. In this dissertation, we are primarily discussing two application of HAR in different contexts. First, we design a novel approach for in-home, fine-grained activity recognition using multimodal wearable sensors on multiple body positions, along with very small Bluetooth beacons …

Granting Personhood For Sentient Non-Human Animals And Sentient Artificial Intelligences: A Demonstrative Argument, Jeremiah Meadows

Virginias Collegiate Honors Council Conference

While the subject of personhood has been exhaustively debated regarding the unborn, personhood for sentient animals and artificial intelligences is a concept that is rarely deliberated. Humanity has learned that there are multiple animal species which are very similar to humans in their self-awareness, emotional capacity, and free will. These traits have been partially developed for artificial intelligences as well, and those characteristics will evolve alongside human and technological development. As stratified societies emerged, there have been multiple occurrences where individuals were deemed lesser but then later acquired equal standing. Dr. Daniel Wilson, roboticist, wrote in his novel Robopocalypse, “It …

Toward A Collaborative Ai Framework For Assistive Dementia Care, Tze-Yun Leong

Research Collection School Of Computing and Information Systems

We envision an integrated framework for supporting the development and deployment of human-aware, general artificial intelligence (AI) that needs to collaborate in uncertain, changing environments. We examine the technology and system requirements of building assistive care agents for dementia or cognitive impaired patients through the continuum of care. We summarize the new AI capabilities and show examples of how an evolving, adaptive development approach would be able to support the basic functionalities and applications in a sound, practical, and scalable manner. We highlight the challenges and the opportunities involved in realizing the proposed framework, and call for future research and …

A System To Monitor Cognitive Workload In Naturalistic High-Motion Environments, Bethany K. Bracken, Seth Elkin-Frankston, Noa Palmon, Michael Farry, Blaise De B Frederick

H-Workload 2017: Models and Applications (Works in Progress)

Across many careers, individuals face alternating periods of high and low attention and cognitive workload can impair cognitive function and undermine job performance. We have designed and are developing an unobtrusive system to Monitor, Extract, and Decode Indicators of Cognitive Workload (MEDIC) in naturalistic, high-motion environments. MEDIC is designed to warn individuals, teammates, or supervisors when steps should be taken to augment cognitive readiness. We first designed and manufactured a forehead sensor device that includes a custom fNIRS sensor and a three-axis accelerometer designed to be mounted on the inside of a baseball cap or headband, or standard issue gear …

Towards A Not Obtrusive Low Cost Biosystem To Assess Risk Perception In Workplace Through Stress Detection, Emanuele Bellini, Serena Benevenuti, Chiara Batistini

H-Workload 2017: Models and Applications (Works in Progress)

The main aim of the article is to build a method to assess risk perception in real time in order to early detect and prevent risk behaviors and possible human errors. To this end, the relation between mental workload and stress as critical factors affecting risk perception has been investigated. In particular the mental-physical activation generated by an increment of the workload has the effect of reducing the resources needed to perceive risk increasing the worker vulnerability. The complexity of the stress phenomenon suggested the adoption of an integrated view. The Functional Model has been adopted to for its holistic …

Reducing Peak Workload In The Cockpit: A Human In The Loop Simulation Evaluating New Runway Selection Tool, Tanja Bos, Rolf Zon, Wilfred Rouwhorst

H-Workload 2017: Models and Applications (Works in Progress)

In efforts to increase safety and reduce peak workload situations in the cockpit, a tool with a different interaction style was developed for use in case of a runway change instructed by Air Traffic Control during approach. In an experiment a workload comparison was made between the new tool and the conventional cockpit. Workload was measured by means of a self-rating after each experiment run, as well as eye blink frequency during each run. Results show that the self-rated workload decreases with the new tool for one of the two crew members and the blink frequency suggests a workload decrease …

System Identification Of Motion Artifact: Noise In Eeg Headsets From Locomotion, Kaela Shea, James Tung

H-Workload 2017: Models and Applications (Works in Progress)

Fall prevention for geriatric populations is a growing concern among clinicians and researchers due to severe risk of morbidity and loss of independence. Emerging evidence has demonstrated that mental workload while walking influences gait stability and the risk for falling. Electroencephalography (EEG) presents a potential method to provide objective measures of mental workload, particularly during daily activities. Noise introduced to the EEG signal during motion, however, is restrictive. The study presented in the following paper isolates EEG signal noise attained from gait for a commercially accessible EEG system, the "Emotiv" Time and spectral system identification techniques were applied to model …

Online Measuring Of Available Resources, Enrique Munoz-De-Escalona, José Juan Canas

H-Workload 2017: Models and Applications (Works in Progress)

This paper present a proposal for measuring available mental resources during the accomplishment of a task. Our proposal consists in measuring emotions provoked by perceived self-efficacy in the execution of the task. Self-efficacy is one of the most important factors that affect the resources that a person puts at the disposal of the execution of the task. When a person perceives that he/she is not being effective he/she will activate more resources to improve his performance. This self-efficacy will be reflected in the emotions that the person experiences. A good efficacy will provoke positive emotions and a bad efficacy negative …

A Workload-Centered Perspective On Reduced Crew Operations In Commercial Aviation, Daniela Schmid

H-Workload 2017: Models and Applications (Works in Progress)

Mental workload of a pilot, in short workload, depends on various characteristics of different accumulated tasks on the flight deck. Exogenous task demands and endogenous supply of attentional or information processing resources determine workload [1]. Performance is expect to drop if the demand exceeds the available resources of the pilot. Expertise and experience modulate the endogenous sup- ply of resources like perceiving, updating memory, planing, making a decision, and executing and processing a response. Subsequently, workload manifests in performance variables, subjective experience, and physiological parameters [2]. This is how we can summarize workload very brie y to introduce a model …

A Systems Approach To Predicting And Measuring Workload In Rail Traffic Management Systems, Joanna Evans

H-Workload 2017: Models and Applications (Works in Progress)

The introduction of systems such as Traffic Management (TM) will result in a number of changes in how the railway is managed for operations and maintenance staff such as, an increase in collaborative working styles and shared responsibilities. In order to react to these changing operational demands and user needs, TM workstation designs need to have greater flexibility and be configurable to support the information requirements for each specific role as well as support each role during different scenarios. Although this flexibility in system design has the potential to enhance performance, it increases the complexity of measuring operator workload. The …

Intelligent Profiling Of Blood Donors In Ireland, Joanna Kossakowska

Theses

The demand for blood products in Ireland is constantly rising due to population growth and population ageing. It is believed that within the next decade these two factors will present challenges to blood donor recruitment and the availability of blood supplies. Improving the retention of blood donors will have a positive impact on the availability of blood products. Identification of suitable donors with the potential for long-term donating can potentially enhance the predictability of blood supply levels.

This research proposes that the patterns of blood donation behaviours of donors can be isolated from blood donor databases held by blood collecting …

Towards A Continuous Assessment Of Cognitive Workload For Smartphone Multitasking Users, Angel Jimenez-Molina, Hernan Lira

H-Workload 2017: Models and Applications (Works in Progress)

The intermeshing of Smartphone interactions and daily activities depletes the availability of cognitive resources. This excessive demand may lead to several undesirable cognitive states, which can be avoided by continuously assessing the user cognitive workload. Recently, many attempts have emerged to assess this workload by using psycho physiological signals. This paper provides evidence that it is possible to train models that accurately identify in short time windows such cognitive workload by processing heart rate and blood oxygen saturation signals. This assessment could be applied in Smartphone notification delivery, interface adaptations or cognitive capabilities evaluation.

Human Performance Modelling In Manufacturing: Mental Workload And Task Complexity, Maria Chiara Leva, Lorenzo Comberti, Micaela Demichela, Rebecca Duane

H-Workload 2017: Models and Applications (Works in Progress)

No abstract provided.

Distress And Worry As Mediators In The Relationship Between Psychosocial Risks And Upper Body Musculosketal Complaints In Highly Automated Manufacturing, Fiona Wixted, Leonard O'Sullivan

H-Workload 2017: Models and Applications (Works in Progress)

As a result of an upward trend in automation, the requirement for supervisory monitoring and consequently, cognitive demand has increased in automated manufacturing. The incidence of musculoskeletal disorders has also increased in the manufacturing sector. A model was developed based on survey data to test if distress and worry mediate the relationship between psychosocial factors (job control, cognitive demand, social isolation and skill discretion), stress states and upper body musculoskeletal complaints in highly automated manufacturing companies (n=235). Cognitive demand was shown to be related to higher distress in employees. The data raise the question about the link between job control …