Distributed RDF Storage and Querying Using In-Memory Processing Engine

161510-Thumbnail Image.png
Description
The proliferation of semantic data in the form of RDF (Resource Description Framework) triples demands an efficient, scalable, and distributed storage along with a highly available and fault-tolerant parallel processing strategy. There are three open issues with distributed RDF data…

The proliferation of semantic data in the form of RDF (Resource Description Framework) triples demands an efficient, scalable, and distributed storage along with a highly available and fault-tolerant parallel processing strategy. There are three open issues with distributed RDF data management systems that are not well addressed altogether in existing work. First is the querying efficiency, second is that solutions are optimized for certain types of query patterns and don’t necessarily work well for all types, and third is concerned with reducing pre-processing cost. Therefore, the rapid growth of RDF data raises the need for an efficient partitioning strategy over distributed data management systems to improve SPARQL (SPARQL Protocol and RDF Query Language) query performance regardless of its pattern shape with minimized pre-processing overhead. In this context, the first contribution of this work is a distributed RDF data partitioning schema called 3CStore that extends the existing VP (Vertical Partitioning) approach by using a subset of triples from the VP tables based on different join correlations. This approach speeds up queries at the cost of additional pre-processing overhead. To solve this, a relational partitioning schema called VPExp was developed by splitting predicates based on explicit type information of objects. This approach gains a significant query performance only for the specific type of query where the object is bound to a value for a particular predicate. To get efficient query performance on a wide range of query patterns, an improved solution is proposed by extending the existing Property Table approach to Subset-Property Table and combined with the VP approach. Further investigation on distributed RDF processing and querying systems based on typical use cases led to a novel relational partitioning schema called PTP (Property Table Partitioning) that further partitions the whole Property Table into the number of unique properties to minimize query input size and join operations during query evaluation. Finally, an RDF data management system based on the SPARQL-over-SQL approach called S3QLRDF is developed that generates the optimal query execution plan using statistics of PTP tables to provide efficient SPARQL query processing on a distributed system.
Date Created
2021
Agent

Content Agnostic Game Based Stealth Assessment

161463-Thumbnail Image.png
Description
Serious or educational games have been a subject of research for a long time. They usually have game mechanics, game content, and content assessment all tied together to make a specialized game intended to impart learning of the associated content…

Serious or educational games have been a subject of research for a long time. They usually have game mechanics, game content, and content assessment all tied together to make a specialized game intended to impart learning of the associated content to its players. While this approach is good for developing games for teaching highly specific topics, it consumes a lot of time and money. Being able to re-use the same mechanics and assessment for creating games that teach different contents would lead to a lot of savings in terms of time and money. The Content Agnostic Game Engineering (CAGE) Architecture mitigates the problem by disengaging the content from game mechanics. Moreover, the content assessment in games is often quite explicit in the way that it disturbs the flow of the players and thus hampers the learning process, as it is not integrated into the game flow. Stealth assessment helps to alleviate this problem by keeping the player engagement intact while assessing them at the same time. Integrating stealth assessment into the CAGE framework in a content-agnostic way will increase its usability and further decrease in game and assessment development time and cost. This research presents an evaluation of the learning outcomes in content-agnostic game-based assessment developed using the CAGE framework.
Date Created
2021
Agent

SwiftUI vs UIKit: A Case Study on How a Declarative Framework Can Improve Learnability of UI Programming

161223-Thumbnail Image.jpg
Description

User interface development on iOS is in a major transitionary state as Apple introduces a declarative and interactive framework called SwiftUI. SwiftUI’s success depends on how well it integrates its new tooling for novice developers. This paper will demonstrate and…

User interface development on iOS is in a major transitionary state as Apple introduces a declarative and interactive framework called SwiftUI. SwiftUI’s success depends on how well it integrates its new tooling for novice developers. This paper will demonstrate and discuss where SwiftUI succeeds and fails at carving a new path for user interface development for new developers. This is done by comparisons against its existing imperative UI framework UIKit as well as elaborating on the background of SwiftUI and examples of how SwiftUI works to help developers. The paper will also discuss what exactly led to SwiftUI and how it is currently faring on Apple's latest operating systems. SwiftUI is a framework growing and evolving to serve the needs of 5 very different platforms with code that claims to be simpler to write and easier to deploy. The world of UI programming in iOS has been dominated by a Storyboard canvas for years, but SwiftUI claims to link this graphic-first development process with the code programmers are used to by keeping them side by side in constant sync. This bold move requires interactive programming capable of recompilation on the fly. As this paper will discuss, SwiftUI has garnered a community of developers giving it the main property it needs to succeed: a component library.

Date Created
2021-12
Agent

Agora: Introducing the Internet’s Opinion to Traditional Stock Analysis and Prediction

161079-Thumbnail Image.png
Description

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations…

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

Date Created
2021-12
Agent

Agora: Introducing the Internet's Opinion to Traditional Stock Analysis and Prediction.

161012-Thumbnail Image.png
Description

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations…

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

Date Created
2021-12
Agent

Generating Trusted Coordination of Collaborative Software Development Using Blockchain

158591-Thumbnail Image.png
Description
The coordination of developing various complex and large-scale projects using computers has been well established and is the so-called computer-supported cooperative work (CSCW). Collaborative software development consists of a group of teams working together to achieve a common goal for…

The coordination of developing various complex and large-scale projects using computers has been well established and is the so-called computer-supported cooperative work (CSCW). Collaborative software development consists of a group of teams working together to achieve a common goal for developing a high-quality, complex, and large-scale software system efficiently, and it requires common processes and communication channels among these teams. The common processes for coordination among software development teams can be handled by similar principles in CSCW. The development of complex and large-scale software becomes complicated due to the involvement of many software development teams. The development of such a software system can be largely improved by effective collaboration among the participating software development teams at both software components and system levels. The efficiency of developing software components depends on trusted coordination among the participating teams for sharing, processing, and managing information on various participating teams, which are often operating in a distributed environment. Participating teams may belong to the same organization or different organizations. Existing approaches to coordination in collaborative software development are based on using a centralized repository to store, process, and retrieve information on participating software development teams during the development. These approaches use a centralized authority, have a single point of failure, and restricted rights to own data and software. In this thesis, the generation of trusted coordination in collaborative software development using blockchain is studied, and an approach to achieving trusted cooperation for collaborative software development using blockchain is presented. The smart contracts are created in the blockchain to encode software specifications and acceptance criteria for the software results generated by participating teams. The blockchain used in the approach is a private blockchain because a private blockchain has the characteristics of providing non-repudiation, privacy, and integrity, which are required in trusted coordination of collaborative software development. This approach is implemented using Hyperledger, an open-source private blockchain. An example to illustrate the approach is also given.
Date Created
2020
Agent

Kitsune: Structurally-Aware and Adaptable Plagiarism Detection

158416-Thumbnail Image.png
Description
Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a…

Plagiarism is a huge problem in a learning environment. In programming classes especially, plagiarism can be hard to detect as source codes' appearance can be easily modified without changing the intent through simple formatting changes or refactoring. There are a number of plagiarism detection tools that attempt to encode knowledge about the programming languages they support in order to better detect obscured duplicates. Many such tools do not support a large number of languages because doing so requires too much code and therefore too much maintenance. It is also difficult to add support for new languages because each language is vastly different syntactically. Tools that are more extensible often do so by reducing the features of a language that are encoded and end up closer to text comparison tools than structurally-aware program analysis tools.

Kitsune attempts to remedy these issues by tying itself to Antlr, a pre-existing language recognition tool with over 200 currently supported languages. In addition, it provides an interface through which generic manipulations can be applied to the parse tree generated by Antlr. As Kitsune relies on language-agnostic structure modifications, it can be adapted with minimal effort to provide plagiarism detection for new languages. Kitsune has been evaluated for 10 of the languages in the Antlr grammar repository with success and could easily be extended to support all of the grammars currently developed by Antlr or future grammars which are developed as new languages are written.
Date Created
2020
Agent

Generating Vocabulary Sets for Implicit Language Learning using Masked Language Modeling

158310-Thumbnail Image.png
Description
Globalization is driving a rapid increase in motivation for learning new languages, with online and mobile language learning applications being an extremely popular method of doing so. Many language learning applications focus almost exclusively on aiding students in acquiring vocabulary,…

Globalization is driving a rapid increase in motivation for learning new languages, with online and mobile language learning applications being an extremely popular method of doing so. Many language learning applications focus almost exclusively on aiding students in acquiring vocabulary, one of the most important elements in achieving fluency in a language. A well-balanced language curriculum must include both explicit vocabulary instruction and implicit vocabulary learning through interaction with authentic language materials. However, most language learning applications focus only on explicit instruction, providing little support for implicit learning. Students require support with implicit vocabulary learning because they need enough context to guess and acquire new words. Traditional techniques aim to teach students enough vocabulary to comprehend the text, thus enabling them to acquire new words. Despite the wide variety of support for vocabulary learning offered by learning applications today, few offer guidance on how to select an optimal vocabulary study set.

This thesis proposes a novel method of student modeling which uses pre-trained masked language models to model a student's reading comprehension abilities and detect words which are required for comprehension of a text. It explores the efficacy of using pre-trained masked language models to model human reading comprehension and presents a vocabulary study set generation pipeline using this method. This pipeline creates vocabulary study sets for explicit language learning that enable comprehension while still leaving some words to be acquired implicitly. Promising results show that masked language modeling can be used to model human comprehension and that the pipeline produces reasonably sized vocabulary study sets.
Date Created
2020
Agent

Domain-Agnostic Context-Aware Assistant Framework for Task-Based Environment

158297-Thumbnail Image.png
Description
Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home…

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only.

In this thesis, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by making the mapping between the language constructs and the system primitives customizable. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation purposes and machine learning models for language processing tasks. It has been evaluated within a simulation environment consisting of objects and a robot. This environment has been deployed as a web application, providing anonymous user testing for evaluation, and generating training data for machine learning components. Performance evaluation has been done on metrics such as time taken for a task or the number of instructions given by the user to the robot to accomplish a task. Additionally, the framework has been used to create a natural language interface for a database system to demonstrate its domain independence.
Date Created
2020
Agent

Diversifying Relevant Search Results from Social Media Using Community Contributed Images

158206-Thumbnail Image.png
Description
Availability of affordable image and video capturing devices as well as rapid development of social networking and content sharing websites has led to the creation of new type of content, Social Media. Any system serving the end user’s query search…

Availability of affordable image and video capturing devices as well as rapid development of social networking and content sharing websites has led to the creation of new type of content, Social Media. Any system serving the end user’s query search request should not only take the relevant images into consideration but they also need to be divergent for a well-rounded description of a query. As a result, the automated optimization of image retrieval results that are also divergent becomes exceedingly important.



The main focus of this thesis is to use visual description of a landmark by choosing the most diverse pictures that best describe all the details of the queried location from community-contributed datasets. For this, an end-to-end framework has been built, to retrieve relevant results that are also diverse. Different retrieval re-ranking and diversification strategies are evaluated to find a balance between relevance and diversification. Clustering techniques are employed to improve divergence. A unique fusion approach has been adopted to overcome the dilemma of selecting an appropriate clustering technique and the corresponding parameters, given a set of data to be investigated. Extensive experiments have been conducted on the Flickr Div150Cred dataset that has 30 different landmark locations. The results obtained are promising when evaluated on metrics for relevance and diversification.
Date Created
2020
Agent
Subscribe to Bansal, Ajay