Howard Project TikTok #2

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Description

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from…

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from various Fortune 500 companies to provide a measure of workplace inequality as well as encourage these institutions to adopt more equitable policies. By rating companies based on what truly matters to women, ASU’s Difference Engine hopes to help both women in existing career paths as well as women seeking a new career or position in companies. However, in order for the WPI to become a relevant scoring metric of gender equality within the workplace, we must raise awareness about the issue of gender equality and of the index itself. By raising awareness about gender inequality as well as inspiring companies to further equality within their workplaces, the WPI will serve to have an integral role in increasing gender equality in the workplace. Our approach for raising awareness utilizes two different strategies: (1) establishing a new version of the WPI website that is both informative and aesthetically pleasing and (2) generating social media content on TikTok that appeal to a variety of audiences and introduce them to the WPI and our mission.

Date Created
2022-05
Agent

Howard Project TikTok #1

165885-Thumbnail Image.jpg
Description

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from…

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from various Fortune 500 companies to provide a measure of workplace inequality as well as encourage these institutions to adopt more equitable policies. By rating companies based on what truly matters to women, ASU’s Difference Engine hopes to help both women in existing career paths as well as women seeking a new career or position in companies. However, in order for the WPI to become a relevant scoring metric of gender equality within the workplace, we must raise awareness about the issue of gender equality and of the index itself. By raising awareness about gender inequality as well as inspiring companies to further equality within their workplaces, the WPI will serve to have an integral role in increasing gender equality in the workplace. Our approach for raising awareness utilizes two different strategies: (1) establishing a new version of the WPI website that is both informative and aesthetically pleasing and (2) generating social media content on TikTok that appeal to a variety of audiences and introduce them to the WPI and our mission.

Date Created
2022-05
Agent

Website Prototype Preview

165884-Thumbnail Image.jpg
Description

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from…

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from various Fortune 500 companies to provide a measure of workplace inequality as well as encourage these institutions to adopt more equitable policies. By rating companies based on what truly matters to women, ASU’s Difference Engine hopes to help both women in existing career paths as well as women seeking a new career or position in companies. However, in order for the WPI to become a relevant scoring metric of gender equality within the workplace, we must raise awareness about the issue of gender equality and of the index itself. By raising awareness about gender inequality as well as inspiring companies to further equality within their workplaces, the WPI will serve to have an integral role in increasing gender equality in the workplace. Our approach for raising awareness utilizes two different strategies: (1) establishing a new version of the WPI website that is both informative and aesthetically pleasing and (2) generating social media content on TikTok that appeal to a variety of audiences and introduce them to the WPI and our mission.

Date Created
2022-05
Agent

Howard Final Project (Spring 2022)

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Description

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from…

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from various Fortune 500 companies to provide a measure of workplace inequality as well as encourage these institutions to adopt more equitable policies. By rating companies based on what truly matters to women, ASU’s Difference Engine hopes to help both women in existing career paths as well as women seeking a new career or position in companies. However, in order for the WPI to become a relevant scoring metric of gender equality within the workplace, we must raise awareness about the issue of gender equality and of the index itself. By raising awareness about gender inequality as well as inspiring companies to further equality within their workplaces, the WPI will serve to have an integral role in increasing gender equality in the workplace. Our approach for raising awareness utilizes two different strategies: (1) establishing a new version of the WPI website that is both informative and aesthetically pleasing and (2) generating social media content on TikTok that appeal to a variety of audiences and introduce them to the WPI and our mission.

Date Created
2022-05
Agent

ASU Women’s Power and Influence Index: Creating Awareness for the WPI

Description
The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from…

The Difference Engine at Arizona State University developed the Women’s Power and Influence Index (WPI) in order to combat the systemic inequality faced by women in the workplace. It aims to analyze data, such as Equal Employment Opportunity data, from various Fortune 500 companies to provide a measure of workplace inequality as well as encourage these institutions to adopt more equitable policies. By rating companies based on what truly matters to women, ASU’s Difference Engine hopes to help both women in existing career paths as well as women seeking a new career or position in companies. However, in order for the WPI to become a relevant scoring metric of gender equality within the workplace, we must raise awareness about the issue of gender equality and of the index itself. By raising awareness about gender inequality as well as inspiring companies to further equality within their workplaces, the WPI will serve to have an integral role in increasing gender equality in the workplace. Our approach for raising awareness utilizes two different strategies: (1) establishing a new version of the WPI website that is both informative and aesthetically pleasing and (2) generating social media content on TikTok that appeal to a variety of audiences and introduce them to the WPI and our mission.
Date Created
2022-05
Agent

Making Bayesian Optimization Practical in the Context of High Dimensional, Highly Expensive, Black­Box Functions

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Description
Complex systems appear when interaction among system components creates emergent behavior that is difficult to be predicted from component properties. The growth of Internet of Things (IoT) and embedded technology has increased complexity across several sectors (e.g., automotive, aerospace, agriculture,…

Complex systems appear when interaction among system components creates emergent behavior that is difficult to be predicted from component properties. The growth of Internet of Things (IoT) and embedded technology has increased complexity across several sectors (e.g., automotive, aerospace, agriculture, city infrastructures, home technologies, healthcare) where the paradigm of cyber-physical systems (CPSs) has become a standard. While CPS enables unprecedented capabilities, it raises new challenges in system design, certification, control, and verification. When optimizing system performance computationally expensive simulation tools are often required, and search algorithms that sequentially interrogate a simulator to learn promising solutions are in great demand. This class of algorithms are black-box optimization techniques. However, the generality that makes black-box optimization desirable also causes computational efficiency difficulties when applied real problems. This thesis focuses on Bayesian optimization, a prominent black-box optimization family, and proposes new principles, translated in implementable algorithms, to scale Bayesian optimization to highly expensive, large scale problems. Four problem contexts are studied and approaches are proposed for practically applying Bayesian optimization concepts, namely: (1) increasing sample efficiency of a highly expensive simulator in the presence of other sources of information, where multi-fidelity optimization is used to leverage complementary information sources; (2) accelerating global optimization in the presence of local searches by avoiding over-exploitation with adaptive restart behavior; (3) scaling optimization to high dimensional input spaces by integrating Game theoretic mechanisms with traditional techniques; (4) accelerating optimization by embedding function structure when the reward function is a minimum of several functions. In the first context this thesis produces two multi-fidelity algorithms, a sample driven and model driven approach, and is implemented to optimize a serial production line; in the second context the Stochastic Optimization with Adaptive Restart (SOAR) framework is produced and analyzed with multiple applications to CPS falsification problems; in the third context the Bayesian optimization with sample fictitious play (BOFiP) algorithm is developed with an implementation in high-dimensional neural network training; in the last problem context the minimum surrogate optimization (MSO) framework is produced and combined with both Bayesian optimization and the SOAR framework with applications in simultaneous falsification of multiple CPS requirements.
Date Created
2021
Agent

Developing a Machine Learning Framework for Student Persistence Prediction

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Description
Student retention is a critical metric for many universities whose intention is to support student success. The goal of this thesis is to create retention models utilizing machine learning (ML) techniques. The factors explored in this research include only those…

Student retention is a critical metric for many universities whose intention is to support student success. The goal of this thesis is to create retention models utilizing machine learning (ML) techniques. The factors explored in this research include only those known during the admissions process. These models have two goals: first, to correctly predict as many non-returning students as possible, while minimizing the number of students who are falsely predicted as non-returning. Next, to identify important features in student retention and provide a practical explanation for a student's decision to no longer persist. The models are then used to provide outreach to students that need more support. The findings of this research indicate that the current top performing model is Adaboost which is able to successfully predict non-returning students with an accuracy of 54 percent.
Date Created
2021
Agent

Cognitive Computing for Decision Support

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Description
The Cognitive Decision Support (CDS) model is proposed. The model is widely applicable and scales to realistic, complex decision problems based on adaptive learning. The utility of a decision is discussed and four types of decisions associated with CDS model…

The Cognitive Decision Support (CDS) model is proposed. The model is widely applicable and scales to realistic, complex decision problems based on adaptive learning. The utility of a decision is discussed and four types of decisions associated with CDS model are identified. The CDS model is designed to learn decision utilities. Data enrichment is introduced to promote the effectiveness of learning. Grouping is introduced for large-scale decision learning. Introspection and adjustment are presented for adaptive learning. Triage recommendation is incorporated to indicate the trustworthiness of suggested decisions.

The CDS model and methodologies are integrated into an architecture using concepts from cognitive computing. The proposed architecture is implemented with an example use case to inventory management.

Reinforcement learning (RL) is discussed as an alternative, generalized adaptive learning engine for the CDS system to handle the complexity of many problems with unknown environments. An adaptive state dimension with context that can increase with newly available information is discussed. Several enhanced components for RL which are critical for complex use cases are integrated. Deep Q networks are embedded with the adaptive learning methodologies and applied to an example supply chain management problem on capacity planning.

A new approach using Ito stochastic processes is proposed as a more generalized method to generate non-stationary demands in various patterns that can be used in decision problems. The proposed method generates demands with varying non-stationary patterns, including trend, cyclical, seasonal, and irregular patterns. Conventional approaches are identified as special cases of the proposed method. Demands are illustrated in realistic settings for various decision models. Various statistical criteria are applied to filter the generated demands. The method is applied to a real-world example.
Date Created
2020
Agent

Capacity Planning, Production and Distribution Scheduling for a Multi-Facility and Multi-Product Supply Chain Network

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Description
In today’s rapidly changing world and competitive business environment, firms are challenged to build their production and distribution systems to provide the desired customer service at the lowest possible cost. Designing an optimal supply chain by optimizing supply chain…

In today’s rapidly changing world and competitive business environment, firms are challenged to build their production and distribution systems to provide the desired customer service at the lowest possible cost. Designing an optimal supply chain by optimizing supply chain operations and decisions is key to achieving these goals.

In this research, a capacity planning and production scheduling mathematical model for a multi-facility and multiple product supply chain network with significant capital and labor costs is first proposed. This model considers the key levers of capacity configuration at production plants namely, shifts, run rate, down periods, finished goods inventory management and overtime. It suggests a minimum cost plan for meeting medium range demand forecasts that indicates production and inventory levels at plants by time period, the associated manpower plan and outbound shipments over the planning horizon. This dissertation then investigates two model extensions: production flexibility and pricing. In the first extension, the cost and benefits of investing in production flexibility is studied. In the second extension, product pricing decisions are added to the model for demand shaping taking into account price elasticity of demand.





The research develops methodologies to optimize supply chain operations by determining the optimal capacity plan and optimal flows of products among facilities based on a nonlinear mixed integer programming formulation. For large size real life cases the problem is intractable. An alternate formulation and an iterative heuristic algorithm are proposed and tested. The performance and bounds for the heuristic are evaluated. A real life case study in the automotive industry is considered for the implementation of the proposed models. The implementation results illustrate that the proposed method provides valuable insights for assisting the decision making process in the supply chain and provides significant improvement over current practice.
Date Created
2020
Agent

Applying Knowledge Management Systems to ASU Capstone Courses: Implementing Knowledge Sharing Practices to Better Capture Data and Lessons Learned from Year-Long Capstone Projects

Description
In the past, Industrial Engineering/Engineering Management Capstone groups have not provided adequate documentation of their project data, results, and conclusions to both the course instructor and their project sponsors. The goal of this project is to mitigate these issues by…

In the past, Industrial Engineering/Engineering Management Capstone groups have not provided adequate documentation of their project data, results, and conclusions to both the course instructor and their project sponsors. The goal of this project is to mitigate these issues by instituting a knowledge management system with one of ASU’s cloud storage tools, OSF, and by updating course rubrics to reflect knowledge sharing best practices. This project used existing research to employ tactics that promote the long-term use of this system. In addition, data specialists from ASU Library’s Research and Data Management department were involved.
Date Created
2019-12
Agent
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