Matching Items (28)
Filtering by
- All Subjects: deep learning
- All Subjects: Technology
- Creators: Computer Science and Engineering Program
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
Description
Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved.
ContributorsKuhler, Madison Frances (Co-author) / Capuano, Bailey (Co-author) / Preston, Michael (Co-author) / Chen, Yinong (Thesis director) / Hunt, Neil (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
"Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved."
ContributorsCapuano, Bailey Kellen (Co-author) / Preston, Michael (Co-author) / Kuhler, Madison (Co-author) / Chen, Yinong (Thesis director) / Hunt, Neil (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Generating an astounding $110.7 billion annually in domestic revenue alone [1], the world of accounting is one deceptively lacking automation of its most business-critical processes. While accounting tools do exist for the common person, especially when it is time to pay their taxes, such innovations scarcely exist for many larger industrial tasks. Exceedingly common business events, such as Business Combinations, are surprisingly manual tasks despite their $1.1 trillion valuation in 2020 [2]. This work presents the twin accounting solutions TurboGAAP and TurboIFRS: an unprecedented leap into these murky waters in an attempt to automate and streamline these gigantic accounting tasks once entrusted only to teams of experienced accountants.
A first-to-market approach to a trillion-dollar problem, TurboGAAP and TurboIFRS are the answers for years of demands from the accounting sector that established corporations have never solved.
ContributorsPreston, Michael Ernest (Co-author) / Capuano, Bailey (Co-author) / Kuhler, Madison (Co-author) / Chen, Yinong (Thesis director) / Hunt, Neil (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key issue for Company X is how to commercialize RealSense's depth recognition capabilities. This thesis addresses the problem by examining which markets to address and how to monetize this technology. The first part of the analysis identified potential markets for RealSense. This was achieved by evaluating current markets that could benefit from the camera's gesture recognition, 3D scanning, and depth sensing abilities. After identifying seven industries where RealSense could add value, a model of the available, addressable, and obtainable market sizes was developed for each segment. Key competitors and market dynamics were used to estimate the portion of the market that Company X could capture. These models provided a forecast of the discounted gross profits that could be earned over the next five years. These forecasted gross profits, combined with an examination of the competitive landscape and synergistic opportunities, resulted in the selection of the three segments thought to be most profitable to Company X. These segments are smart home, consumer drones, and automotive. The final part of the analysis investigated entrance strategies. Company X's competitive advantages in each space were found by examining the competition, both for the RealSense camera in general and other technologies specific to each industry. Finally, ideas about ways to monetize RealSense were developed by exploring various revenue models and channels.
ContributorsDunn, Nicole (Co-author) / Boudreau, Thomas (Co-author) / Kinzy, Chris (Co-author) / Radigan, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / WPC Graduate Programs (Contributor) / Department of Psychology (Contributor) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Science (Contributor) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
This paper presents work that was done to create a system capable of facial expression recognition (FER) using deep convolutional neural networks (CNNs) and test multiple configurations and methods. CNNs are able to extract powerful information about an image using multiple layers of generic feature detectors. The extracted information can be used to understand the image better through recognizing different features present within the image. Deep CNNs, however, require training sets that can be larger than a million pictures in order to fine tune their feature detectors. For the case of facial expression datasets, none of these large datasets are available. Due to this limited availability of data required to train a new CNN, the idea of using naïve domain adaptation is explored. Instead of creating and using a new CNN trained specifically to extract features related to FER, a previously trained CNN originally trained for another computer vision task is used. Work for this research involved creating a system that can run a CNN, can extract feature vectors from the CNN, and can classify these extracted features. Once this system was built, different aspects of the system were tested and tuned. These aspects include the pre-trained CNN that was used, the layer from which features were extracted, normalization used on input images, and training data for the classifier. Once properly tuned, the created system returned results more accurate than previous attempts on facial expression recognition. Based on these positive results, naïve domain adaptation is shown to successfully leverage advantages of deep CNNs for facial expression recognition.
ContributorsEusebio, Jose Miguel Ang (Author) / Panchanathan, Sethuraman (Thesis director) / McDaniel, Troy (Committee member) / Venkateswara, Hemanth (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Instead of providing the illusion of agency to a reader via a tree or network of prewritten, branching paths, an interactive story should treat the reader as a player who has meaningful influence on the story. An interactive story can accomplish this task by giving the player a large toolset for expression in the plot. LudoNarrare, an engine for interactive storytelling, puts "verbs" in this toolset. Verbs are contextual choices of action given to agents in a story that result in narrative events. This paper begins with an analysis and statement of the problem of creating interactive stories. From here, various attempts to solve this problem, ranging from commercial video games to academic research, are given a brief overview to give context to what paths have already been forged. With the background set, the model of interactive storytelling that the research behind LudoNarrare led to is exposed in detail. The section exploring this model contains explanations on what storyworlds are and how they are structured. It then discusses the way these storyworlds can be brought to life. The exposition on the LudoNarrare model finally wraps up by considering the way storyworlds created around this model can be designed. After the concepts of LudoNarrare are explored in the abstract, the story of the engine's research and development and the specifics of its software implementation are given. With LudoNarrare fully explained, the focus then turns to plans for evaluation of its quality in terms of entertainment value, robustness, and performance. To conclude, possible further paths of investigation for LudoNarrare and its model of interactive storytelling are proposed to inspire those who wish to continue in the spirit of the project.
ContributorsStark, Joshua Matthew (Author) / VanLehn, Kurt (Thesis director) / Wetzel, Jon (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12
Description
The constant evolution of technology has greatly shifted the way in which we gain knowledge information. This, in turn, has an affect on how we learn. Long gone are the days where students sit in libraries for hours flipping through numerous books to find one specific piece of information. With the advent of Google, modern day students are able to arrive at the same information within 15 seconds. This technology, the internet, is reshaping the way we learn. As a result, the academic integrity policies that are set forth at the college level seem to be outdated, often prohibiting the use of technology as a resource for learning. The purpose of this paper is to explore why exactly these resources are prohibited. By contrasting a subject such as Computer Science with the Humanities, the paper explores the need for the internet as a resource in some fields as opposed to others. Taking a look at the knowledge presented in Computer Science, the course structure, and the role that professors play in teaching this knowledge, this thesis evaluates the epistemology of Engineering subjects. By juxtaposing Computer Science with the less technology reliant humanities subjects, it is clear that one common policy outlining academic integrity does not suffice for an entire university. Instead, there should be amendments made to the policy specific to each subject, in order to best foster an environment of learning at the university level. In conclusion of this thesis, Arizona State University's Academic Integrity Policy is analyzed and suggestions are made to remove ambiguity in the language of the document, in order to promote learning at the university.
ContributorsMohan, Sishir Basavapatna (Author) / Brake, Elizabeth (Thesis director) / Martin, William (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Emotion recognition in conversation has applications within numerous domains such as affective computing and medicine. Recent methods for emotion recognition jointly utilize conversational data over several modalities including audio, video, and text. However, state-of-the-art frameworks for this task do not focus on the feature extraction and feature fusion steps of this process. This thesis aims to improve the state-of-the-art method by incorporating two components to better accomplish these steps. By doing so, we are able to produce improved representations for the text modality and better model the relationships between all modalities. This paper proposes two methods which focus on these concepts and provide improved accuracy over the state-of-the-art framework for multimodal emotion recognition in dialogue.
ContributorsRawal, Siddharth (Author) / Baral, Chitta (Thesis director) / Shah, Shrikant (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Accurate pose initialization and pose estimation are crucial requirements in on-orbit space assembly and various other autonomous on-orbit tasks. However, pose initialization and pose estimation are much more difficult to do accurately and consistently in space. This is primarily due to not only the variable lighting conditions present in space, but also the power requirements mandated by space-flyable hardware. This thesis investigates leveraging a deep learning approach for monocular one-shot pose initialization and pose estimation. A convolutional neural network was used to estimate the 6D pose of an assembly truss object. This network was trained by utilizing synthetic imagery generated from a simulation testbed. Furthermore, techniques to quantify model uncertainty of the deep learning model were investigated and applied in the task of in-space pose estimation and pose initialization. The feasibility of this approach on low-power computational platforms was also tested. The results demonstrate that accurate pose initialization and pose estimation can be conducted using a convolutional neural network. In addition, the results show that the model uncertainty can be obtained from the network. Lastly, the use of deep learning for pose initialization and pose estimation in addition with uncertainty quantification was demonstrated to be feasible on low-power compute platforms.
ContributorsKailas, Siva Maneparambil (Author) / Ben Amor, Heni (Thesis director) / Detry, Renaud (Committee member) / Economics Program in CLAS (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Spotify, one of the most popular music streaming services, has many
algorithms for recommending new music to users. However, at the
core of their recommendations is the collaborative filtering algorithm,
which recommends music based on what other people with similar
tastes have listened to [1]. While this can produce highly relevant
content recommendations, it tends to promote only popular content
[2]. The popularity bias inherent in collaborative-filtering based
systems can overlook music that fits a user’s taste, simply because
nobody else is listening to it. One possible solution to this problem is
to recommend music based on features of the music itself, and
recommend songs which have similar features. Here, a method for
extracting high-level features representing the mood of a song is
presented, with the aim of tailoring music recommendations to an
individual's mood, and providing music recommendations with
diversity in popularity.
algorithms for recommending new music to users. However, at the
core of their recommendations is the collaborative filtering algorithm,
which recommends music based on what other people with similar
tastes have listened to [1]. While this can produce highly relevant
content recommendations, it tends to promote only popular content
[2]. The popularity bias inherent in collaborative-filtering based
systems can overlook music that fits a user’s taste, simply because
nobody else is listening to it. One possible solution to this problem is
to recommend music based on features of the music itself, and
recommend songs which have similar features. Here, a method for
extracting high-level features representing the mood of a song is
presented, with the aim of tailoring music recommendations to an
individual's mood, and providing music recommendations with
diversity in popularity.
ContributorsGomez, Luis Angel (Author) / Kevin, Burger (Thesis director) / Alberto, Hernández (Committee member) / Arts, Media and Engineering Sch T (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05