LookBook: pioneering Inclusive beauty with artificial intelligence and machine learning algorithms

Technology's imperfections and biases inherited from historical norms are crucial to acknowledge. Rapid perpetuation and amplification of these biases necessitate transparency and proactive measures to mitigate their impact. The online visual culture reinforces Eurocentric beauty ideals through prioritized algorithms and augmented reality filters, distorting reality and perpetuating unrealistic standards of beauty. Narrow beauty standards in technology pose a significant challenge to overcome. Algorithms personalize content, creating "filter bubbles" that reinforce these ideals and limit exposure to diverse representations of beauty. This cycle compels individuals to conform, hindering the embrace of their unique features and alternative definitions of beauty. LookBook counters prevalent narrow beauty standards in technology. It promotes inclusivity and representation through self-expression, community engagement, and diverse visibility. LookBook comprises three core sections: Dash, Books, and Community. In Dash, users curate their experience through personalization algorithms. Books allow users to collect curated content for inspiration and creativity, while Community fosters connections with like-minded individuals. Through LookBook, users create a reality aligned with their unique vision. They control consumed content, nurturing individualism through preferences and creativity. This personalization empowers individuals to break free from narrow beauty standards and embrace their distinctiveness. LookBook stands out with its algorithmic training and data representation. It offers transparency on how personalization algorithms operate and ensures a balanced and diverse representation of physicalities and ethnicities. By addressing biases and embracing a wide range of identities, LookBook sparks a conversation for a technology landscape that amplifies all voices, fostering an environment celebrating diversity and prioritizing inclusivity

Modeling Crowd Feedback in the Mobile App Market

Mobile application (app) stores, such as Google Play and the Apple App Store, have recently emerged as a new model of online distribution platform. These stores have expanded in size in the past five years to host millions of apps, offering end-users of mobile software virtually unlimited options to choose from. In such a competitive market, no app is too big to fail. In fact, recent evidence has shown that most apps lose their users within the first 90 days after initial release. Therefore, app developers have to remain up-to-date with their end-users’ needs in order to survive. Staying close to the user not only minimizes the risk of failure, but also serves as a key factor in achieving market competitiveness as well as managing and sustaining innovation. However, establishing effective communication channels with app users can be a very challenging and demanding process. Specifically, users\u27 needs are often tacit, embedded in the complex interplay between the user, system, and market components of the mobile app ecosystem. Furthermore, such needs are scattered over multiple channels of feedback, such as app store reviews and social media platforms. To address these challenges, in this dissertation, we incorporate methods of requirements modeling, data mining, domain engineering, and market analysis to develop a novel set of algorithms and tools for automatically classifying, synthesizing, and modeling the crowd\u27s feedback in the mobile app market. Our analysis includes a set of empirical investigations and case studies, utilizing multiple large-scale datasets of mobile user data, in order to devise, calibrate, and validate our algorithms and tools. The main objective is to introduce a new form of crowd-driven software models that can be used by app developers to effectively identify and prioritize their end-users\u27 concerns, develop apps to meet these concerns, and uncover optimized pathways of survival in the mobile app ecosystem

The role of supplier relationship platforms in supply chain management- the case of Ecratum

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementThe term supply chain can be defined as a process in which suppliers, manufacturers, distributors, and retailers are working together during the whole process of manufacturing the product and delivering it to the end-user. More specifically, all parties are involved in various phases from getting the raw material, transforming this material into a product that will satisfy users' needs and make sure this product reaches the end customer (La Londe & Masters, 1994). However, even though supply chains are created with the main aim to reduce costs, find the right partners to deliver the products and stay competitive on the market, proper management is crucial for the successful operation. Supply chain management (hereinafter: SCM), is all about the right optimization and strategic planning to identify, acquire, gain, allocate and manage all the needed resources that are involved in the workflow of achieving strategic objectives (Flynn, Harding, Lallatin, Pohlig & Sturzl, 2006)

APPLICATIONS OF GRAPH THEORY FOR REUSE OF MODEL BASED SYSTEMS ENGINEERING DESIGN DATA

This dissertation contributes to systems engineering (SE) by introducing and demonstrating a novel graph-based design repository (GBDR) tool. GBDR enables engineers to leverage system design information from a heterogenous set of system models created using multiple model based systems engineering (MBSE) software tools as an integrated body of knowledge. Specifically, the research provides a set of approaches that allow the use of system models described in Systems Modeling Language and Lifecycle Modeling Language as an integrated body of design information. The coalesced body of system design information serves to support concept ideation and analysis within SE. The research accomplishes this by using a graph database to store system model information imported from digital artifacts created by MBSE tools and applying principles from graph theory and semantic web technologies to identify likely connections and equivalent concepts across system models, modeling languages, and metamodels. The research demonstrates that the presented tool can import, store, synthesize, search, display, distribute, and export information from multiple MBSE tools. As a practical demonstration, feasible subsystem design alternatives for a small unmanned aircraft system government reference architecture are identified from within a set of existing system models.OSD CAPECivilian, Office of the Secretary of DefenseApproved for public release. Distribution is unlimited