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- Genre: Masters Thesis
- Creators: Yang, Yezhou
- Creators: Bansal, Ajay
Description
ABSTRACT In an attempt to advocate body-conscious design and healing work environments, this research study of holistic health in the workplace explores cognitive, social and physical well-being in four small US offices that are between 1000 and 4000 square feet and employ three to twelve employees. Holistic health, as pursued in this research, includes social health, emotional health and physical health. These three factors of holistic health have been identified and investigated in this study: biophilia: peoples' love and affiliation with other species and the natural environment; ergonomics: the relationship between the human body, movement, the immediate environment and productivity; and exercise: exertion of the body to obtain physical fitness. This research study proposes that employees and employers of these four participating workplaces desire mobility and resources in the workplace that support holistic health practices involving biophilia, ergonomics, and exercise. Literature review of holistic health and the holistic health factors of this research topic support the idea that interaction with other species can be healing, ergonomic body-conscious furniture and equipment increase productivity, limit body aches, pains and health costs; and exercise stimulates the mind and body, increasing productivity. This study has been conducted primarily with qualitative and flexible research approaches using observation, survey, interview and pedometer readings as methods for data collection. Two small corporate franchise financial institutions and two small private healthcare providers from both Arizona and Georgia participated in this study. Each office volunteered one employer and two employee participants. Of the holistic health factors considered in these four case studies, this study found that a majority of participants equally valued emotional health, social health and physical health. A majority of participants declared a preference for workplace environments with serene natural environments with outdoor spaces and interaction with other species, work environments with body-conscious furniture, equipment and workstations, as well as exercise space and equipment. As these particular workplace environments affirmed value for elements of the factors biophilia, ergonomics and exercise, all three factors are considered valueable within the workplaces of these case studies. Furthermore, factors that were said to contribute to personal productivity in participating workplaces were found as well as sacrifices that participants stated they would be willing to make in order to implement their preferred work environment(s). In addition, this study recorded and calculated average miles walked by participants in each workplace as well as existing incentives and descriptions of ideal work environments. Implications of this research study involve interior design, industrial design and fashion design that can accommodate the desires of the four participating workplaces. Major design implications involve accommodating these particular workplaces to provide personnel with opportunities for holistic health in working environments. More specific implications of office related design involve providing access to natural environments, body-conscious equipment and spaces, as well as opportunities for exercise and social interaction. These elements of the factors biophilia, ergonomics and exercise were found to be said to contribute to cognitive, social and physical health.
ContributorsMcEwan, April (Author) / White, Philip (Thesis advisor) / Shraiky, James (Committee member) / Barry, Rebecca (Committee member) / Arizona State University (Publisher)
Created2011
Description
Smart home system (SHS) is a kind of information system aiming at realizing home automation. The SHS can connect with almost any kind of electronic/electric device used in a home so that they can be controlled and monitored centrally. Today's technology also allows the home owners to control and monitor the SHS installed in their homes remotely. This is typically realized by giving the SHS network access ability. Although the SHS's network access ability brings a lot of conveniences to the home owners, it also makes the SHS facing more security threats than ever before. As a result, when designing a SHS, the security threats it might face should be given careful considerations. System security threats can be solved properly by understanding them and knowing the parts in the system that should be protected against them first. This leads to the idea of solving the security threats a SHS might face from the requirements engineering level. Following this idea, this paper proposes a systematic approach to generate the security requirements specifications for the SHS. It can be viewed as the first step toward the complete SHS security requirements engineering process.
ContributorsXu, Rongcao (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Lindquist, Timothy (Committee member) / Arizona State University (Publisher)
Created2013
Description
"Too often, people in pain are stuck in limbo. With no diagnosis there is no prognosis. They feel that without knowing what is wrong, there is no way to make it right" (Lewandowski, 2006, p. ix). Research has shown that environmental factors, such as views of nature, positive distractions and natural light can reduce anxiety and pain (Ulrich, 1984). Patients with chronic, painful diseases are often worried, anxious and tired. Doctor's appointments for those with a chronic pain diagnosis can be devastating (Gilron, Peter, Watson, Cahill, & Moulin, 2006). The research question explored in this study is: Does the layout, seating and elements of positive distraction in the pain center waiting room relate to the patients experience of pain and distress? This study utilized a mixed-method approach. A purposive sample of 39 individuals participated in the study. The study employed the Positive and Negative Affect Schedule (PANAS), the Lewandowski Pain Scale (LPS) and a researcher developed Spatial Perception Instrument (SPI) rating the appearance and comfort of a pain center waiting room in a large metropolitan area. Results indicated that there were no significant correlations between pain, distress and the waiting room environment. It is intended that this study will provide a framework for future research in the area of chronic pain and distress in order to advance the understanding of research in the waiting area environment and the effect it may have on the patient.
ContributorsDraper, Heather (Author) / Bender, Diane (Thesis advisor) / Shraiky, James (Committee member) / Lamb, Gerri (Committee member) / Arizona State University (Publisher)
Created2012
Description
Healthcare is one of the most personal and complex services provided, and as such, designing healthcare environments is particularly challenging. In the last couple of decades, researchers have concentrated their efforts on exploring the elements of the hospital environment that affect patients' health and in finding ways to apply that knowledge in contemporary healthcare design. But despite the growing body of research, there is an element of utmost importance to healing environments that has not been studied very extensively: the patient experience. The interaction of patients with their environment shapes their personal experience, and inversely, focusing on designing experiences rather than services can inform the design of successful healing environments. This shift from designing services to designing experiences has deep implications in healthcare settings because of the stressful situations that patients have to go through; memorable experiences have a positive influence on a patient's emotional health because they help minimize stress and in healthcare environments this translates into improved outcomes. The concept of assembling experiences is not new, especially in the entertainment industry; it was, in fact, the underlying principle behind the creation of the first theme park more than fifty years ago: Disneyland. Today, Disney is an entertainment industry leader and their design concepts and practices have been perfected to achieve the Company's main purpose: to immerse Guests in a happy, unforgettable experience. This research study focuses on examining the principles used by Disney designers, or Imagineers, as they are called within the organization, to generate memorable experiences, and how those theories can be adopted and adapted by healthcare designers to create better healing environments. However, Disney's Imagineering is not the only approach considered in this research. A thorough analysis would not be complete without delving into the concept of experiential design as a design process and from an economical perspective, as well as without analyzing recent notions about the importance of authenticity in businesses and its implications on design. This study, therefore, suggests a new healing environment design model based on a comprehensive review of the literature related to three main design approaches: Disney Imagineering, experiential design and authenticity.
ContributorsDuenas Parra, Betsabe (Author) / Bernardi, Jose (Thesis advisor) / Stein, Morris (Committee member) / Shraiky, James (Committee member) / Arizona State University (Publisher)
Created2012
Description
Gathering and managing software requirements, known as Requirement Engineering (RE), is a significant and basic step during the Software Development Life Cycle (SDLC). Any error or defect during the RE step will propagate to further steps of SDLC and resolving it will be more costly than any defect in other steps. In order to produce better quality software, the requirements have to be free of any defects. Verification and Validation (V&V;) of requirements are performed to improve their quality, by performing the V&V; process on the Software Requirement Specification (SRS) document. V&V; of the software requirements focused to a specific domain helps in improving quality. A large database of software requirements from software projects of different domains is created. Software requirements from commercial applications are focus of this project; other domains embedded, mobile, E-commerce, etc. can be the focus of future efforts. The V&V; is done to inspect the requirements and improve the quality. Inspections are done to detect defects in the requirements and three approaches for inspection of software requirements are discussed; ad-hoc techniques, checklists, and scenario-based techniques. A more systematic domain-specific technique is presented for performing V&V; of requirements.
ContributorsChughtai, Rehman (Author) / Ghazarian, Arbi (Thesis advisor) / Bansal, Ajay (Committee member) / Millard, Bruce (Committee member) / Arizona State University (Publisher)
Created2012
Description
Feature embeddings differ from raw features in the sense that the former obey certain properties like notion of similarity/dissimilarity in it's embedding space. word2vec is a preeminent example in this direction, where the similarity in the embedding space is measured in terms of the cosine similarity. Such language embedding models have seen numerous applications in both language and vision community as they capture the information in the modality (English language) efficiently. Inspired by these language models, this work focuses on learning embedding spaces for two visual computing tasks, 1. Image Hashing 2. Zero Shot Learning. The training set was used to learn embedding spaces over which similarity/dissimilarity is measured using several distance metrics like hamming / euclidean / cosine distances. While the above-mentioned language models learn generic word embeddings, in this work task specific embeddings were learnt which can be used for Image Retrieval and Classification separately.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
Image Hashing is the task of mapping images to binary codes such that some notion of user-defined similarity is preserved. The first part of this work focuses on designing a new framework that uses the hash-tags associated with web images to learn the binary codes. Such codes can be used in several applications like Image Retrieval and Image Classification. Further, this framework requires no labelled data, leaving it very inexpensive. Results show that the proposed approach surpasses the state-of-art approaches by a significant margin.
Zero-shot classification is the task of classifying the test sample into a new class which was not seen during training. This is possible by establishing a relationship between the training and the testing classes using auxiliary information. In the second part of this thesis, a framework is designed that trains using the handcrafted attribute vectors and word vectors but doesn’t require the expensive attribute vectors during test time. More specifically, an intermediate space is learnt between the word vector space and the image feature space using the hand-crafted attribute vectors. Preliminary results on two zero-shot classification datasets show that this is a promising direction to explore.
ContributorsGattupalli, Jaya Vijetha (Author) / Li, Baoxin (Thesis advisor) / Yang, Yezhou (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2019
Description
Calculus as a math course is important subject students need to succeed in, in order to venture into STEM majors. This thesis focuses on the early detection of at-risk students in a calculus course which can provide the proper intervention that might help them succeed in the course. Calculus has high failure rates which corroborates with the data collected from Arizona State University that shows that 40% of the 3266 students whose data were used failed in their calculus course.This thesis proposes to utilize educational big data to detect students at high risk of failure and their eventual early detection and subsequent intervention can be useful. Some existing studies similar to this thesis make use of open-scale data that are lower in data count and perform predictions on low-impact Massive Open Online Courses(MOOC) based courses. In this thesis, an automatic detection method of academically at-risk students by using learning management systems(LMS) activity data along with the student information system(SIS) data from Arizona State University(ASU) for the course calculus for engineers I (MAT 265) is developed. The method will detect students at risk by employing machine learning to identify key features that contribute to the success of a student.
This thesis also proposes a new technique to convert this button click data into a button click sequence which can be used as inputs to classifiers. In addition, the advancements in Natural Language Processing field can be used by adopting methods such as part-of-speech (POS) tagging and tools such as Facebook Fasttext word embeddings to convert these button click sequences into numeric vectors before feeding them into the classifiers. The thesis proposes two preprocessing techniques and evaluates them on 3 different machine learning ensembles to determine their performance across the two modalities of the class.
ContributorsDileep, Akshay Kumar (Author) / Bansal, Ajay (Thesis advisor) / Cunningham, James (Committee member) / Acuna, Ruben (Committee member) / Arizona State University (Publisher)
Created2021
Description
One persisting problem in Massive Open Online Courses (MOOCs) is the issue of student dropout from these courses. The prediction of student dropout from MOOC courses can identify the factors responsible for such an event and it can further initiate intervention before such an event to increase student success in MOOC. There are different approaches and various features available for the prediction of student’s dropout in MOOC courses.In this research, the data derived from the self-paced math course ‘College Algebra and Problem Solving’ offered on the MOOC platform Open edX offered by Arizona State University (ASU) from 2016 to 2020 was considered. This research aims to predict the dropout of students from a MOOC course given a set of features engineered from the learning of students in a day. Machine Learning (ML) model used is Random Forest (RF) and this model is evaluated using the validation metrics like accuracy, precision, recall, F1-score, Area Under the Curve (AUC), Receiver Operating Characteristic (ROC) curve. The average rate of student learning progress was found to have more impact than other features. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5% respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model. The features engineered in this research are predictive of student dropout and could be used for similar courses to predict student dropout from the course. This model can also help in making interventions at a critical time to help students succeed in this MOOC course.
ContributorsDominic Ravichandran, Sheran Dass (Author) / Gary, Kevin (Thesis advisor) / Bansal, Ajay (Committee member) / Cunningham, James (Committee member) / Sannier, Adrian (Committee member) / Arizona State University (Publisher)
Created2021
Description
Traditional Reinforcement Learning (RL) assumes to learn policies with respect to reward available from the environment but sometimes learning in a complex domain requires wisdom which comes from a wide range of experience. In behavior based robotics, it is observed that a complex behavior can be described by a combination of simpler behaviors. It is tempting to apply similar idea such that simpler behaviors can be combined in a meaningful way to tailor the complex combination. Such an approach would enable faster learning and modular design of behaviors. Complex behaviors can be combined with other behaviors to create even more advanced behaviors resulting in a rich set of possibilities. Similar to RL, combined behavior can keep evolving by interacting with the environment. The requirement of this method is to specify a reasonable set of simple behaviors. In this research, I present an algorithm that aims at combining behavior such that the resulting behavior has characteristics of each individual behavior. This approach has been inspired by behavior based robotics, such as the subsumption architecture and motor schema-based design. The combination algorithm outputs n weights to combine behaviors linearly. The weights are state dependent and change dynamically at every step in an episode. This idea is tested on discrete and continuous environments like OpenAI’s “Lunar Lander” and “Biped Walker”. Results are compared with related domains like Multi-objective RL, Hierarchical RL, Transfer learning, and basic RL. It is observed that the combination of behaviors is a novel way of learning which helps the agent achieve required characteristics. A combination is learned for a given state and so the agent is able to learn faster in an efficient manner compared to other similar approaches. Agent beautifully demonstrates characteristics of multiple behaviors which helps the agent to learn and adapt to the environment. Future directions are also suggested as possible extensions to this research.
ContributorsVora, Kevin Jatin (Author) / Zhang, Yu (Thesis advisor) / Yang, Yezhou (Committee member) / Praharaj, Sarbeswar (Committee member) / Arizona State University (Publisher)
Created2021
Description
Machine learning models can pick up biases and spurious correlations from training data and projects and amplify these biases during inference, thus posing significant challenges in real-world settings. One approach to mitigating this is a class of methods that can identify filter out bias-inducing samples from the training datasets to force models to avoid being exposed to biases. However, the filtering leads to a considerable wastage of resources as most of the dataset created is discarded as biased. This work deals with avoiding the wastage of resources by identifying and quantifying the biases. I further elaborate on the implications of dataset filtering on robustness (to adversarial attacks) and generalization (to out-of-distribution samples). The findings suggest that while dataset filtering does help to improve OOD(Out-Of-Distribution) generalization, it has a significant negative impact on robustness to adversarial attacks. It also shows that transforming bias-inducing samples into adversarial samples (instead of eliminating them from the dataset) can significantly boost robustness without sacrificing generalization.
ContributorsSachdeva, Bhavdeep Singh (Author) / Baral, Chitta (Thesis advisor) / Liu, Huan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021