Artificial Intelligence and Robotics Commons^™

A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das

Graduate Theses/Dissertations

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …

Mining Semantic Knowledge Graphs To Add Explainability To Black Box Recommender Systems, Mohammed Alshammari, Olfa Nasraoui, Scott Sanders

Faculty and Staff Scholarship

Recommender systems are being increasingly used to predict the preferences of users on online platforms and recommend relevant options that help them cope with information overload. In particular, modern model-based collaborative filtering algorithms, such as latent factor models, are considered state-of-the-art in recommendation systems. Unfortunately, these black box systems lack transparency, as they provide little information about the reasoning behind their predictions. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of …

Digital Marketing In The Artificial Intelligence And Machine Learning Age, Z. Ruan, Keng Siau

Research Collection School Of Computing and Information Systems

We are living in a period of profound change driven by digitization, information and communication technology, artificial intelligence, machine learning, and robotics (Gupta, Keen, Shah, and Verdier, 2017; Wang and Siau, 2019). Traditional marketing is shifting to digital marketing enabled by AI and machine learning. Customer consumption behavior has changed from traditional in-store shopping to online shopping (Thiraviyam, 2018). The large volume of transaction and demographic data enables business analytics, AI, and machine learning to analyze and predict customer behavior to improve customer satisfaction and enhance sales (Siau and Wang, 2018). For example, predictive analytics uses different algorithms to predict …

How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau

Research Collection School Of Computing and Information Systems

In many organizations, promoting diversity and enhancing inclusion are still major concerns. Unconscious biases and stereotyping cause us to have preconceived ideas about what an ideal employee or leader should look like. Unconscious biases are also a major roadblock to an inclusive environment and business culture. Organizations have been investing heavily in training programs for their employees attempting to changes these patterns. Human habits, especially unconscious ones, are not easy to overcome. This research looks at the use of AI to enhance diversity and inclusion in organizations. Literature has shown that a more diverse and inclusive workforce has a competitive …

Itaa: An Intelligent Trajectory-Driven Outdoor Advertising Deployment Assistant, Yipeng Zhang, Zhifeng Bao, Songsong Mo, Yuchen Li, Yanghao Zhou

Research Collection School Of Computing and Information Systems

In this paper, we demonstrate an Intelligent Trajectory-driven outdoor Advertising deployment Assistant (ITAA), which assists users to find an optimal strategy for outdoor advertising (ad) deployment. The challenge is how to measure the influence to the moving trajectories of ads, and how to optimize the placement of ads among billboards that maximize the influence has been proven NP-hard. Therefore, we develop a framework based on two trajectory-driven influence models. ITAA is built upon this framework with a user-friendly UI. It serves both ad companies and their customers. We enhance the interpretability to improve the user's understanding of the influence of …

Potential Impact Of Artificial Intelligence On Mental Well-Being, Weiyu Wang, Keng Siau

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) will result in job replacement and job elimination. Some AI technologies, such as self-driving vehicles, have the potential to disrupt existing industries. Self-driving trucks may replace the 3.5 million truck drivers in the US. Scholars at Oxford University estimated that no less than 47% of American jobs and 54% of those in Europe are at a high risk of being taken over by machines. Routine, repetitive, and predictable jobs are expected to be automated (Siau, 2018). Although new jobs will be created, unemployment rate may go up in the short term and the emergence of a “useless …

Fintech Empowerment: Data Science, Ai, And Machine Learning, Keng Siau, Michael Hilgers, Langtao Chen, Steve Liu, Fiona Fui-Hoon Nah, Richard Hall, Barry Flachsbart

Research Collection School Of Computing and Information Systems

The article discusses how data science, artificial intelligence and machine learning are affecting the evolution of “fintech,” the technologies used to deliver financial services. After presenting fintech’s competitive advantages in combination with these other advanced technologies, the article posits that financial institutions that don’t move forward with the innovations will be eliminated from the marketplace.

Knowledge Base Question Answering With Topic Units, Yunshi Lan, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning …

Adapting Bert For Target-Oriented Multimodal Sentiment Classification, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

As an important task in Sentiment Analysis, Target-oriented Sentiment Classification (TSC) aims to identify sentiment polarities over each opinion target in a sentence. However, existing approaches to this task primarily rely on the textual content, but ignoring the other increasingly popular multimodal data sources (e.g., images), which can enhance the robustness of these text-based models. Motivated by this observation and inspired by the recently proposed BERT architecture, we study Target-oriented Multimodal Sentiment Classification (TMSC) and propose a multimodal BERT architecture. To model intra-modality dynamics, we first apply BERT to obtain target-sensitive textual representations. We then borrow the idea from self-attention …

Ezlog: Data Visualization For Logistics, Aldy Gunawan, Benjamin Gan, Jin An Tan, Sheena L.S.L Villanueva, Timothy K.J. Wen

Research Collection School Of Computing and Information Systems

With the increasing availability of data in the logistics industry due to the digitalization trend, interest and opportunities for leveraging analytics in supply chain management to make data-driven decisions is growing rapidly. In this paper, we introduce EzLog, an integrated visualization prototype platform for supply chain analytics. This web-based platform built by two undergraduate student teams for their capstone course can be used for data wrangling and rapid analysis of data from different business units of a major logistics company. Other functionalities of the system include standard processes to perform data analysis such as supervised extraction, transformation, loading (ETL), data …

Integrated Assignment And Routing With Mixed Service Mode Cross-Dock, Vincent Yu, Aldy Gunawan, Eric I. Junaidi, Audrey T. Widjaja

Research Collection School Of Computing and Information Systems

Amixed service mode cross-dock is a cross-dock facility that considers the useof flexible doors. Instead of having a specific task as an exclusive mode, eachdoor can be used as a flexible door, either an inbound or an outbound doordepending on the requirement. Having a mixed service mode cross-dock in anintegrated assignment and routing problem is a new model in large field ofcross-docking problems. Decisions that need to be made include doors’functionality, suppliers’ assignments, customers’ deliveries, and vehicles’ routeswith the objective of minimizing the total transportation and material handlingcosts. We develop a mathematical programming model and propose a SimulatedAnnealing (SA) algorithm …

Language And Robotics: Complex Sentence Understanding, Seng-Beng Ho, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Existing robotic systems can take actions based on natural language commands but they tend to be only simple commands. On the other hand, in the domain of Natural Language Processing (NLP), complex sentences are processed, but this NLP domain does not make close contact with robotics. The beginning of computer processing of natural language, when traced back to a system such as Winograd’s SHRUDLU, conceived in 1973, actually aimed to address the issues of Natural Language Understanding (NLU) of relatively complex sentences by a robotic system which in turn takes actions accordingly based on the natural language input. NLU, in …

Applications Of The Op, Pieter Vansteenwegen, Aldy Gunawan

Research Collection School Of Computing and Information Systems

In recent years, we observe from literature that the VRP and OP, including their variants, have been used to model many different planning and scheduling problems from practice, such as the routing of technicians, athlete recruitment, or military applications. Recently, other practical applications, such as the tourist trip design problem, the mobile crowdsourcing problem, the smuggler search problem, the wildfire routing problem, and the integration of vehicle routing, inventory management, and customer selection problems, have been studied and use the OP as a basic model. In this chapter, various practical applications will be discussed in more detail. We will describe …

Towards Robust Resnet: A Small Step But A Giant Leap, Jingfeng Zhang, Bo Han, Laura Wynter, Bryan Kian Hsiang Low, Mohan Kankanhalli

Research Collection School Of Computing and Information Systems

This paper presents a simple yet principled approach to boosting the robustness of the residual network (ResNet) that is motivated by a dynamical systems perspective. Namely, a deep neural network can be interpreted using a partial differential equation, which naturally inspires us to characterize ResNet based on an explicit Euler method. This consequently allows us to exploit the step factor h in the Euler method to control the robustness of ResNet in both its training and generalization. In particular, we prove that a small step factor h can benefit its training and generalization robustness during backpropagation and forward propagation, respectively. …

Who Should Pay The Cost: A Game-Theoretic Model For Government Subsidized Investments To Improve National Cybersecurity, Xinrun Wang, Bo An, Hau Chan

Research Collection School Of Computing and Information Systems

Due to the recent cyber attacks, cybersecurity is becoming more critical in modern society. A single attack (e.g., WannaCry ransomware attack) can cause as much as $4 billion in damage. However, the cybersecurity investment by companies is far from satisfactory. Therefore, governments (e.g., in the UK) launch grants and subsidies to help companies to boost their cybersecurity to create a safer national cyber environment. The allocation problem is hard due to limited subsidies and the interdependence between self-interested companies and the presence of a strategic cyber attacker. To tackle the government's allocation problem, we introduce a Stackelberg game-theoretic model where …

Higher Education In The Ai Age, Yizhi Ma, Keng Siau

Research Collection School Of Computing and Information Systems

A perfect storm is hitting higher education. Decrease funding from traditional funding sources such as State Governments and transformative changes caused by artificial intelligence (AI) will revolutionize higher education (Siau 2018). Higher education needs to change and evolve quickly and continuously to prepare students for the upheavals in the job market caused by AI, machine learning, and automation. Further, continuous organizational and curriculum changes will be necessary for a higher education institution to stay relevant and to stay afloat. This qualitative research looks at higher education in the AI age. Stakeholders (i.e., administrators, faculty, students, industry recruiters) in higher education …

Synthetic, Yet Natural: Properties Of Wordnet Random Walk Corpora And The Impact Of Rare Words On Embedding Performance, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher

Conference papers

Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find …

Dimensional Analysis Of Robot Software Without Developer Annotations, John-Paul W. Ore

School of Computing: Dissertations, Theses, and Student Research

Robot software risks the hazard of dimensional inconsistencies. These inconsistencies occur when a program incorrectly manipulates values representing real-world quantities. Incorrect manipulation has real-world consequences that range in severity from benign to catastrophic. Previous approaches detect dimensional inconsistencies in programs but require extra developer effort and technical complications. The extra effort involves developers creating type annotations for every variable representing a real-world quantity that has physical units, and the technical complications include toolchain burdens like specialized compilers or type libraries.

To overcome the limitations of previous approaches, this thesis presents novel methods to detect dimensional inconsistencies without developer annotations. We …

Enhancing Scalability In Genetic Programming With Adaptable Constraints, Type Constraints And Automatically Defined Functions, George Gerules

Dissertations

Genetic Programming is a type of biological inspired machine learning. It is composed of a population of stochastic individuals. Those individuals can exchange portions of themselves with others in the population through the crossover operation that draws its inspiration from biology. Other biologically inspired operations include mutation and reproduction. The form an individual takes can be many things. It, however, is represented most of the time as a computer program. Constructing correct efficient programs can be notoriously difficult. Various grammar, typing, function constraint, or counting mechanisms can guide creation and evolution of those individuals. These mechanisms can reduce search space …

Mathematics And Programming Exercises For Educational Robot Navigation, Ronald I. Greenberg

Computer Science: Faculty Publications and Other Works

This paper points students towards ideas they can use towards developing a convenient library for robot navigation, with examples based on Botball primitives, and points educators towards mathematics and programming exercises they can suggest to students, especially advanced high school students.