Computer Sciences Commons^™

Document Images And Machine Learning: A Collaboratory Between The Library Of Congress And The Image Analysis For Archival Discovery (Aida) Lab At The University Of Nebraska, Lincoln, Ne, Yi Liu, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Conference and Workshop Papers

This presentation summarized and presented preliminary results from the first weeks of work conducted by the Aida research team in response to Library of Congress funding notice ID 030ADV19Q0274, “The Library of Congress – Pre-processing Pilot.” It includes overviews of projects on historic document segmentation, document classification, document quality assessment, figure and graph extraction from historic documents, text-line extraction from figures, subject and objective quality assesments, and digitization type differentiation.

A Two-Step Classification Platform To Classify Construction Sounds To Enhance Real-Time Construction Zone Monitoring, Jayati Halder Jui

LSU Master's Theses

In recent years, sounds generated from equipment used at a construction site have been found to be useful in identifying construction activities and associated equipment usage providing insightful information regarding project status and hazard issues. This study proposes a Two-Step Neural Classifier (TSNC) for accurate classification of real world construction sounds. Experimental results from real world construction site audio data showed an average classification accuracy of 98% for TSNC compared to 91% average accuracy for an ordinary (one-step) Artificial Neural Network (ANN). The TSNC model is a hierarchical model that exploits the idea of creating disjoint subgroups of construction sounds …

Pristine Sentence Translation: A New Approach To A Timeless Problem, Meenu Ahluwalia, Brian Coari, Ben Brock

SMU Data Science Review

Abstract.

Pristine Sentence Translation (PST) is a new approach to language translation based upon sentence-level granularity. Traditional translation approaches, including those utilizing advanced machine learning or neural network-based approaches, translate on a word-by-word or phrase-by-phrase basis; thereby, potentially missing the context or meaning of the complete sentence. Instead of these piecewise translations, PST utilizes deep learning and predictive modeling techniques to translate complete sentences from their source language into their target language. With these approaches we were able to translate sentences that closely conveyed the meaning of the original sentences. Our results demonstrated that PST’s method of translating an entire …

Texture-Based Deep Neural Network For Histopathology Cancer Whole Slide Image (Wsi) Classification, Nelson Zange Tsaku

Master of Science in Computer Science Theses

Automatic histopathological Whole Slide Image (WSI) analysis for cancer classification has been highlighted along with the advancements in microscopic imaging techniques. However, manual examination and diagnosis with WSIs is time-consuming and tiresome. Recently, deep convolutional neural networks have succeeded in histopathological image analysis. In this paper, we propose a novel cancer texture-based deep neural network (CAT-Net) that learns scalable texture features from histopathological WSIs. The innovation of CAT-Net is twofold: (1) capturing invariant spatial patterns by dilated convolutional layers and (2) Reducing model complexity while improving performance. Moreover, CAT-Net can provide discriminative texture patterns formed on cancerous regions of histopathological …

Emergent Behavior Development And Control In Multi-Agent Systems, David W. King

Theses and Dissertations

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, …

Foodai: Food Image Recognition Via Deep Learning For Smart Food Logging, Doyen Sahoo, Hao Wang, Ke Shu, Xiongwei Wu, Hung Le, Palakorn Achananuparp, Ee-Peng Lim, Hoi, Steven C. H.

Research Collection School Of Computing and Information Systems

An important aspect of health monitoring is effective logging of food consumption. This can help management of diet-related diseases like obesity, diabetes, and even cardiovascular diseases. Moreover, food logging can help fitness enthusiasts, and people who wanting to achieve a target weight. However, food-logging is cumbersome, and requires not only taking additional effort to note down the food item consumed regularly, but also sufficient knowledge of the food item consumed (which is difficult due to the availability of a wide variety of cuisines). With increasing reliance on smart devices, we exploit the convenience offered through the use of smart phones …

Machine Learning-Based Network Vulnerability Analysis Of Industrial Internet Of Things, Maede Zolanvari, Marcio Teixeira, Lav Gupta, Khaled Khan, Raj Jain

Computer Science Faculty Works

No abstract provided.

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 …

Pose Based Human Activity Recognition, Wenbo Li

Legacy Theses & Dissertations (2009 - 2024)

Pose based human activity recognition is an important step towards video understanding. The last decade has witnessed the great progress in this field which is driven by multiple technical innovations, i.e., kinect, pose estimation techniques, deep learning, etc.

Energy-Based Modelling For Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including …

Impact Of Robotic Challenges On Fifth Grade Problem Solving, Julie Rankin

Department of Teaching, Learning, and Teacher Education: Theses and Other Student Research

This action research project was designed to investigate the impact of educational robotics in a fifth grade rural classroom. The integration of science, technology, engineering, and math in education (STEM) has sparked an increase of robotics in the classroom. The purpose of the study was to determine if problem-solving skills can be impacted through continuing involvement with challenges using various educational robotics and programming tools. The study sought to answer two research questions: (1) How does the introduction of robotics challenges in a fifth-grade classroom impact students’ problem solving skills? (2) How do robotics in the classroom impact student interest …

Law Library Blog (August 2019): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

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 …

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 …

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 …

How Does Machine Learning Change Software Development Practices?, Zhiyuan Wan, Xin Xia, David Lo, Gail C. Murphy

Research Collection School Of Computing and Information Systems

Adding an ability for a system to learn inherently adds uncertainty into the system. Given the rising popularity of incorporating machine learning into systems, we wondered how the addition alters software development practices. We performed a mixture of qualitative and quantitative studies with 14 interviewees and 342 survey respondents from 26 countries across four continents to elicit significant differences between the development of machine learning systems and the development of non-machine-learning systems. Our study uncovers significant differences in various aspects of software engineering (e.g., requirements, design, testing, and process) and work characteristics (e.g., skill variety, problem solving and task identity). …

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 …

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 …

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 …

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 …

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 …

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. …

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 …

Ai-Fashion: Collaborative Ai In The Fashion Industry, Y. Luo, Keng Siau

Research Collection School Of Computing and Information Systems

Abstract The word vintage is generally accepted to mean clothing produced in the period between 1920s and 1980s (Cervellon et al., 2012). According to Fischer (2015), fashion usually means rapid changes and up-to-date trendiness. Vintage dressing, however, has been a fashionable trend for over 40 years. Can AI be used to predict the next fashion trend? Fashion industry is currently exploring the use of AI to analyze customer behavior and predict next year’s fashion trends. Predicting the correct next trend is vital to the competitiveness and survivability of fashion brands. Research in this area is not new. For example, research …

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 …