Digital Commons Network^™

Why So Negative? Street Literature And Its Negative Effects On The African American Psyche, Lilly Lamia Simone

Theses (2016-Present)

Urban literature, more widely known as street lit is a genre of literature that glorifies and exaggerates drugs, violence, and sex in the lives of its African American characters. Through Street lit readers are introduced to the black man as a figure of power through illegal activity in his community, and black women as either overly aggressive or figures in need of protection. These novels choose loyalty over family, drugs, and athleticism over education, and money and power over morality. These novels and their glorifications cause African American people to see dream lives in goals that are not only unattainable …

A Study Of Students’ And Teachers’ Beliefs About Learning English As A Second Language In Hindi Medium Schools In India, Samir Toppo

Doctoral Dissertations

The present study is an exploration of beliefs about learning English held by English as a second Language (ESL) students and teachers from Jharkhand, India. The basic premise of the study is that the learners’ and the instructors’ beliefs about learning a language affect the learning approach, learning strategy use, personal motivation, the choice of instructional material and the instructional method. Thus, beliefs indirectly affect language learning. The assumption of this study is also that the participants in this study hold debilitative beliefs about English language learning. A mixed method research approach was used in this study. The quantitative data …

Early Childhood Special Education Teachers’ Attitudes Toward Inclusion: A Qualitative Study On A School’S Transition From Segregated Classrooms To More Inclusion Classrooms, Shally Moua

Doctoral Dissertations

Inclusion has been a topic of interest in education and a great deal of research has identified teachers’ attitudes as a barrier to inclusion. Therefore, using the model of attitude (Eagly & Chaiken, 1993) and inclusive pedagogy framework (Florian & Black-Hawkins, 2012), the qualitative study aims to: (1) examine the attitudes of early childhood special education (ECSE) teachers toward inclusion as a school shift from segregated to more inclusive classrooms, (2) explore types of support they received and how that support shaped their inclusive pedagogical practices to teach in inclusive classrooms, and (3) identify barriers that impact their transition to …

Understanding The African American Male Student Experience Of Being Diagnosed With Emotional Disturbance Through The Use Of Counter-Storytelling, Sara Ordaz

Doctoral Dissertations

In the U.S. educational system, it is too common to see African American males overrepresented in special education classrooms, including segregated special education settings. African American males continuously experience disproportionate representation and placement in special education, especially under the label of ED (Harry & Anderson, 1994). Twice as many African American students in the United States are receiving services for Emotional Disturbance than their White counterparts.

Students who are labeled with ED have the lowest educational outcomes as well as lower success rates in life than any other disability classification (Merrrell & Walker, 2004). The consequences of the ED label …

Votes And Voters In Time And Space: The Changing Landscape Of Political Party Support In Kentucky, 1974–2020, Glenn Humphress

Department of Geography: Dissertations, Theses, and Student Research

This research examines the time-series geography of voter registrations, presidential elections, senatorial elections, and gubernatorial elections within Kentucky during the period from 1974 to 2020 to explore the dimensions of a changing geography of political party support. During this time Kentucky realigned from strong election support for the Democratic Party to consistent election support for the Republican Party. Using graphs, Dissimilarity Indices, cartographic analysis, and factor analysis, this study confirms aspects of intra-state sectionalism and periodization in election results identified in previous research but finds different characteristics of section and period in voter registrations. In effect this study finds support …

Identification And Development Of Therapeutics For Covid-19, Halie M. Rando, Nils Wellhausen, Soumita Ghosh, Alexandra J. Lee, Anna Ada Dattoli, Fengling Hu, James Brian Byrd, Diane N. Rafizadeh, Ronan Lordan, Yanjun Qi, Yuchen Sun, Christian Brueffer, Jeffrey M. Field, Marouen Ben Guebila, Nafisa M. Jadavji, Ashwin N. Skelly, Bharath Ramsundar, Jinhui Wang, Rishi Raj Goel, Yoson Park, Simina M. Boca, Anthony Gitter, Casey S. Greene

Computer Science: Faculty Publications

After emerging in China in late 2019, the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread worldwide, and as of mid- 2021, it remains a significant threat globally. Only a few coronaviruses are known to infect humans, and only two cause infections similar in severity to SARS-CoV-2: Severe acute respiratory syndrome-related coronavirus, a species closely related to SARS-CoV-2 that emerged in 2002, and Middle East respiratory syndrome-related coronavirus, which emerged in 2012. Unlike the current pandemic, previous epidemics were controlled rapidly through public health measures, but the body of research investigating severe acute respiratory syndrome and Middle East …

Cattle Industry Funding Scheme Annual Report 2020/2021, Department Of Primary Industries And Regional Development, Western Australia

Biosecurity published reports

In June 2010 the Cattle Industry Funding Scheme (IFS) was introduced to address biosecurity threats relevant to the Western Australian (WA) cattle industry. The Scheme was established under the Biosecurity and Agriculture Management Act 2007 to enable cattle producers to identify the pest and disease priorities at a whole-of-industry level and to raise funds for activities to address these priorities.

There are currently three Industry Funding Schemes in operation. In addition to the Cattle IFS, there is a Sheep and Goat IFS and a Grains, Seeds and Hay IFS. The three IFSs operate in a similar manner.

The purpose of …

Parallel Approach Of Adaptive Image Thresholding Algorithm On Gpu, Adhi Prahara, Andri Pranolo, Nuril Anwar, Yingchi Mao

Knowledge Engineering and Data Science

Image thresholding is used to segment an image into background and foreground using a given threshold. The threshold can be generated using a specific algorithm instead of a pre-defined value obtained from observation or experiment. However, the algorithm involves per pixel operation, histogram calculation, and iterative procedure to search the optimum threshold that is costly for high-resolution images. In this research, parallel implementations on GPU for three adaptive image thresholding methods, namely Otsu, ISODATA, and minimum cross-entropy, were proposed to optimize their computational times to deal with high-resolution images. The approach involves parallel reduction and parallel prefix sum (scan) techniques …

A Comprehensive Analysis Of Reward Function For Adaptive Traffic Signal Control, Abu Rafe Md Jamil, Naushin Nower

Knowledge Engineering and Data Science

Adaptive traffic control systems (ATCS) can play an essential role in reducing traffic congestion in urban areas. The main challenge for ATSC is to determine the proper signal timing. Recently, Deep Reinforcement Learning (DRL) has been used to determine proper signal timing. However, the success of the DRL algorithm depends on the appropriate reward function design. There exist various reward functions for ATSC in the existing research. This research presents a comprehensive analysis of the widely used reward function. The pros and cons of various reward algorithms were discussed, and experimental analysis shows that the multi-objective reward function enhances the …

Recognition Of Handwritten Javanese Script Using Backpropagation With Zoning Feature Extraction, Anik Nur Handayani, Heru Wahyu Herwanto, Katya Lindi Chandrika, Kohei Arai

Knowledge Engineering and Data Science

Backpropagation is part of supervised learning, in which the training process requires a target. The resulting error is transmitted back to the units below in its training process. Backpropagation can solve complicated problems because it consumes less memory than other algorithms. In addition, it also can produce solutions with a low error rate while executing less time. In image pattern recognition, backpropagation can be utilized for cultural preservation in many places worldwide, including Indonesia. It is used to recognize picture patterns in Javanese script writings. This study concluded that feature extraction approaches, zoning, and backpropagation could be utilized to distinguish …

A Comparative Study Of Machine Learning-Based Approach For Network Traffic Classification, Kien Trang, An Hoang Nguyen

Knowledge Engineering and Data Science

Internet usage has increased rapidly and become an essential part of human life, corresponding to the rapid development of network infrastructure in recent years. Thus, protecting users’ confidential information when joining the global network becomes one of the most significant considerations. Even though multiple encryption algorithms and techniques have been applied in different parties, including internet providers, and web hosting, this situation also allows the hacker to attack the network system anonymously. Therefore, the significance of classifying network data streams to improve network system quality and security is attracting increasing study interests. This work introduces a machine learning-based approach to …

Learning Large Neighborhood Search Policy For Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We propose a deep reinforcement learning (RL) method to learn large neighborhood search (LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the combinatorial number of variable subsets prevents direct application of typical RL algorithms. To tackle this challenge, we represent all subsets by factorizing them into binary decisions on each variable. We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm. We …

Baseline Studies On Sex-Disaggregated Statistics Of Members And Gender Roles Of Officers Among Recognized Uplb Student Organizations, Jickerson P. Lado, Marcelina C. Miranda, Katrina Joy M. Abriol-Santos, Charlotte H. Hagosojos, Jimby E. Casquete, Marites E. Mojica, Nina M. Cadiz

Journal Article

The sex disaggregated statistics of members of recognized UPLB students organizations for five academic years were determined. Furthermore, the prevailing gender roles in elected key positions were identified. This is the first attempt to create and profile reorganized UPLB student organizations in terms of sex-disaggregated data (SDD) that could translate to effective policies and campaign strategies to enable a harmonized and gender-sensitive culture in UPLB. It sought to identify the assess the SDD of members among recognized UPLB student organization using both qualitative and quantitative methods of analysis. In a preliminary study, the data submitted by an average of 170 …

Building Action Sets In A Deep Reinforcement Learner, Yongzhao Wang, Arunesh Sinha, Sky C.H. Wang, Michael P. Wellman

Research Collection School Of Computing and Information Systems

In many policy-learning applications, the agent may execute a set of actions at each decision stage. Choosing among an exponential number of alternatives poses a computational challenge, and even representing actions naturally expressed as sets can be a tricky design problem. Building upon prior approaches that employ deep neural networks and iterative construction of action sets, we introduce a reward-shaping approach to apportion reward to each atomic action based on its marginal contribution within an action set, thereby providing useful feedback for learning to build these sets. We demonstrate our method in two environments where action spaces are combinatorial. Experiments …

Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

Online hate speech has disrupted the social connectedness in online communities and raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many machine learning and deep learning methods to detect hate speech in social media automatically. However, most of the existing automated solutions have focused on detecting hate speech in a single post, neglecting the network and information propagation effects of social media platforms. Ideally, the content moderators would want to identify the hateful posts and monitor posts and threads that are likely to induce hate. This paper aims to address this research gap …

Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter

Research Collection School Of Computing and Information Systems

One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this …

Resource Gaps For Minority Entrepreneurs: A Study Of 3 Cities, Merissa Piazza, Iryna Lendel, Shneur Kushner, Jack Yochum

All Maxine Goodman Levin School of Urban Affairs Publications

This study aims to assess minority resource gaps in entrepreneurial ecosystems (EEs) in three cities across Ohio: Cleveland, Toledo, and Dayton. This report seeks to answer two main research questions: 1) What are the local factors of EEs that drive growth for minority entrepreneurs? And 2) Across three sites in Ohio, what are the gaps in access to different resources for minority entrepreneurs in the ecosystem? This report covers the first year of this project’s findings as the project team engaged with all three region’s ecosystems in 2021.

Writing Myths: Interpreting And Translating Meaning, Naomi Robinson

Postgraduate

Myths and stories are integral communication devices that cross both lived and shared experience while manifesting through internal and external meaning-making processes. To determine the nature of myths in and as popular culture, this project sought to use practise-led methods. In addition to storytelling and thematic analysis, this project involved the interpretation and adaptation of an ancient myth into a modern television series. By considering television shows as conduits of socio-political agendas, the creative process culminating in this exegesis highlighted how the perpetual influence of myths through knowledge, reference, and experience can be manipulated to drive cultural change.

As an …

Healing Rites : A Performance Autoethnography, Auryn Rotten

Postgraduate

The following is an exegesis that accompanies a performance in film. Together they reveal an autoethnographic journey of a researcher-filmmaker that contributes to the open-ended examination of the human experience through the language of art. The study explores a disruptive, trauma-informed healing ritual of “wounding while healing”. The artistic and scholarly practice is guided by the theoretical principles of art-based research throughout the process. Centring around the lived experience of the researcher as a survivor of sexual assault; the knowledge generated through the work is motivated by a political and personal desire to provoke change. Correspondingly, the film performance interrupts …

Moxie : We Just Wanna Play Games, Chris Stojkos

Postgraduate

Hegemonic geek masculinity dominates video games. It has contributed to the formation of a toxic culture that is now widely perceived to consistently and aggressively prevent women and non-binary gamers from participating as equals. Gaming has historically resisted progressive change, and the industry continues to produce content for the assumed heterosexual male target audience. In more recent years, steps have been taken towards recognising these entrenched issues and implementing change, such as improving gender representation in games, however attempts are inconsistent, and other aspects of the culture are slow to adapt. The gap between the number of male, female, and …

Sonic Submersion: Artistic Research Into 360 Audio, Christopher Hancock

Postgraduate

What are the possibilities of 360 audio to expand compositional practice?

This graduate project employed a mixed methodology to research the field of 360 audio and its potential to expand DAW-based compositional practice. The outcomes of the project include a body of new 360 audio compositions, a range of demonstrative 360 audio samples, and an accompanying exegesis.

Specifically, my research looks at 360 composition using ambisonic plugins, and the binaurally decoded output for headphone delivery. A literature review of human spatial hearing and contemporary 360 audio practice has been conducted, and research-led practice has been used to explore concepts and …

Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of “good” representation from a group-theoretic view using Higgins’ definition of disentangled representation [38], and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. …

Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ …

Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.

Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a data-driven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun

Research Collection School Of Computing and Information Systems

Class-Incremental Learning (CIL) [38] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the …

Spurring Digital Transformation In Singapore's Legal Industry, Xin Juan Chua, Steven M. Miller

Research Collection School Of Computing and Information Systems

COVID-19 has transformed the way we live and work. It has caused the processes and operations of businesses and organisations to be restructured, as well as transformed business models. A 2020 McKinsey Global survey reported that companies all over the world claim they have accelerated the digitalisation of their customer and supply-chain interactions, as well as their internal operations, by three to four years. They also said they thought the share of digital or digitally enabled products in their portfolios has advanced by seven years. While technology transformation is not new to the legal profession, COVID-19 has cemented the importance …

Etherlearn: Decentralizing Learning Via Blockchain, Nguyen Binh Duong Ta, Tian Jun Joel Yang

Research Collection School Of Computing and Information Systems

In institutes of higher learning, most of the time course material development and delivery follow a centralized model which is fully lecturer-controlled. In this model, engaging students as partners in learning is a challenging problem as: 1) students are usually hesitant to contribute due to the fear of getting it wrong, 2) not much incentive for them to put in the extra effort, and 3) current online learning systems lack adequate facilities to support seamless and anonymous interactions between students. In this work, we propose EtherLearn, a blockchain based peer-learning system to distribute the control of how course material and …

Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network’s prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior’s support. …