Computer Sciences Commons^™

Learning Relation Prototype From Unlabeled Texts For Long-Tail Relation Extraction, Yixin Cao, Jun Kuang, Ming Gao, Aoying Zhou, Yonggang Wen, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …

Campus Safety Data Gathering, Classification, And Ranking Based On Clery-Act Reports, Walaa F. Abo Elenin

College of Graduate Studies: Theses & Dissertations

Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions.

In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate …

Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: , which describes the position, size, and layout of each view in the display space; and, which indicates the interaction between pairwise views. We build a new dataset containing 854 dashboards crawled online, …

Mitigating Popularity Bias In Recommendation With Unbalanced Interactions: A Gradient Perspective, Weijieying Ren, Lei Wang, Kunpeng Liu, Ruocheng Guo, Ee-Peng Lim, Yanjie Fu

Research Collection School Of Computing and Information Systems

Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-tailed distribution. Such long-tailed data lead to popularity bias to recommend popular but not personalized items to users. We present a gradient perspective to understand two negative impacts of popularity bias in recommendation model optimization: (i) the gradient direction of popular item embeddings is closer to that of positive interactions, and (ii) the magnitude of positive gradient for popular items are much greater than that of unpopular items. To address these issues, we propose a simple yet efficient …

Hybrid Feature Selection Based On Principal Component Analysis And Grey Wolf Optimizer Algorithm For Arabic News Article Classification, Osama Ahmad Alomari, Ashraf Elnagar, Imad Afyouni, Ismail Shahin, Ali Bou Nassif, Ibrahim Abaker Hashem, Mohammad Tubishat

All Works

The rapid growth of electronic documents has resulted from the expansion and development of internet technologies. Text-documents classification is a key task in natural language processing that converts unstructured data into structured form and then extract knowledge from it. This conversion generates a high dimensional data that needs further analusis using data mining techniques like feature extraction, feature selection, and classification to derive meaningful insights from the data. Feature selection is a technique used for reducing dimensionality in order to prune the feature space and, as a result, lowering the computational cost and enhancing classification accuracy. This work presents a …

An Empirical Study Of Blockchain System Vulnerabilities: Modules, Types, And Patterns, Xiao Yi, Daoyuan Wu, Lingxiao Jiang, Yuzhou Fang, Kehuan Zhang, Wei Zhang

Research Collection School Of Computing and Information Systems

Blockchain, as a distributed ledger technology, becomes increasingly popular, especially for enabling valuable cryptocurrencies and smart contracts. However, the blockchain software systems inevitably have many bugs. Although bugs in smart contracts have been extensively investigated, security bugs of the underlying blockchain systems are much less explored. In this paper, we conduct an empirical study on blockchain’s system vulnerabilities from four representative blockchains, Bitcoin, Ethereum, Monero, and Stellar. Specifically, we first design a systematic filtering process to effectively identify 1,037 vulnerabilities and their 2,317 patches from 34,245 issues/PRs (pull requests) and 85,164 commits on GitHub. We thus build the first blockchain …

Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chalé

Theses and Dissertations

Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …

Using Deep Learning To Detect Social Media ‘Trolls’, Áine Macdermott, Michal Motylinski, Farkhund Iqbal, Kellyann Stamp, Mohammed Hussain, Andrew Marrington

All Works

Detecting criminal activity online is not a new concept but how it can occur is changing. Technology and the influx of social media applications and platforms has a vital part to play in this changing landscape. As such, we observe an increasing problem with cyber abuse and ‘trolling’/toxicity amongst social media platforms sharing stories, posts, memes sharing content. In this paper we present our work into the application of deep learning techniques for the detection of ‘trolls’ and toxic content shared on social media platforms. We propose a machine learning solution for the detection of toxic images based on embedded …

Exploiting Reuse For Gpu Subgraph Enumeration, Wentiao Guo, Yuchen Li, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Subgraph enumeration is important for many applications such as network motif discovery, community detection, and frequent subgraph mining. To accelerate the execution, recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration. The performances of these parallel schemes are dominated by the set intersection operations which account for up to $95\%$ of the total processing time. (Un)surprisingly, a significant portion (as high as $99\%$) of these operations is actually redundant, i.e., the same set of vertices is repeatedly encountered and evaluated. Therefore, in this paper, we seek to salvage and recycle the results of such operations to avoid repeated …

Design And Analysis Of Strategic Behavior In Networks, Sixie Yu

McKelvey School of Engineering Graduate Student Theses & Dissertations

Networks permeate every aspect of our social and professional life.A networked system with strategic individuals can represent a variety of real-world scenarios with socioeconomic origins. In such a system, the individuals' utilities are interdependent---one individual's decision influences the decisions of others and vice versa. In order to gain insights into the system, the highly complicated interactions necessitate some level of abstraction. To capture the otherwise complex interactions, I use a game theoretic model called Networked Public Goods (NPG) game. I develop a computational framework based on NPGs to understand strategic individuals' behavior in networked systems. The framework consists of three …

Solving The Challenges Of Concept Drift In Data Stream Classification., Hanqing Hu

Electronic Theses and Dissertations

The rise of network connected devices and applications leads to a significant increase in the volume of data that are continuously generated overtime time, called data streams. In real world applications, storing the entirety of a data stream for analyzing later is often not practical, due to the data stream’s potentially infinite volume. Data stream mining techniques and frameworks are therefore created to analyze streaming data as they arrive. However, compared to traditional data mining techniques, challenges unique to data stream mining also emerge, due to the high arrival rate of data streams and their dynamic nature. In this dissertation, …

Design Demand Trend Acquisition Method Based On Short Text Mining Of User Comments In Shopping Websites, Zhiyong Xiong, Zhaoxiong Yan, Huanan Yao, Shangsong Liang

Machine Learning Faculty Publications

In order to facilitate designers to explore the market demand trend of laptops and to establish a better “network users-market feedback mechanism”, we propose a design and research method of a short text mining tool based on the K-means clustering algorithm and Kano mode. An improved short text clustering algorithm is used to extract the design elements of laptops. Based on the traditional questionnaire, we extract the user’s attention factors, score the emotional tendency, and analyze the user’s needs based on the Kano model. Then, we select 10 laptops, process them by the improved algorithm, cluster the evaluation words and …

Subomiembed: Self-Supervised Representation Learning Of Multi-Omics Data For Cancer Type Classification, Sayed Hashim, Muhammad Ali, Karthik Nandakumar, Mohammad Yaqub

Computer Vision Faculty Publications

For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. Different types of omics data show various aspects of samples. Integration and analysis of multi-omics data give us a broad view of tumours, which can improve clinical decision making. Omics data, mainly DNA methylation and gene expression profiles are usually high dimensional data with a lot of molecular features. In recent years, variational autoencoders (VAE) [13] have been extensively used in embedding image and text data into …

Impact Of Sleep And Training On Game Performance And Injury In Division-1 Women’S Basketball Amidst The Pandemic, Samah Senbel, S. Sharma, S. M. Raval, Christopher B. Taber, Julie K. Nolan, N. S. Artan, Diala Ezzeddine, Kaya Tolga

School of Computer Science & Engineering Faculty Publications

We investigated the impact of sleep and training load of Division - 1 women’s basketball players on their game performance and injury prediction using machine learning algorithms. The data was collected during a pandemic-condensed season with unpredictable interruptions to the games and athletic training schedules. We collected data from sleep monitoring devices, training data from coaches, injury reports from medical staff, and weekly survey data from athletes for 22 weeks.With proper data imputation, interpretable feature set, data balancing, and classifiers, we showed that we could predict game performance and injuries with more than 90% accuracy. More importantly, our F1 and …

Data Science Applied To Discover Ancient Minoan-Indus Valley Trade Routes Implied By Commonweight Measures, Peter Revesz

School of Computing: Conference and Workshop Papers

This paper applies data mining of weight measures to discover possible long-distance trade routes among Bronze Age civilizations from the Mediterranean area to India. As a result, a new northern route via the Black Sea is discovered between the Minoan and the Indus Valley civilizations. This discovery enhances the growing set of evidence for a strong and vibrant connection among Bronze Age civilizations.

Messiness: Automating Iot Data Streaming Spatial Analysis, Christopher White, Atilio Barreda Ii

Publications and Research

The spaces we live in go through many transformations over the course of a year, a month, or a day; My room has seen tremendous clutter and pristine order within the span of a few hours. My goal is to discover patterns within my space and formulate an understanding of the changes that occur. This insight will provide actionable direction for maintaining a cleaner environment, as well as provide some information about the optimal times for productivity and energy preservation.

Using a Raspberry Pi, I will set up automated image capture in a room in my home. These images will …

Extracting Clinical Event Sequence By Using Association Rule Mining To Predict Clinical Events From Health Records, Aashara Shrestha

Computer Science and Engineering Dissertations - Archive

Data mining is the process of extracting useful information from large amounts of data. Data mining has been around for a long time, and there are many multiple methods of performing data mining. However, the abundance of data that has become available in the last decade has made it possible to mine through this data to uncover important patterns and sequences. The relationship between variables and the way in which they can lead to a specific outcome is an interesting area of research. Today's healthcare industry faces a number of challenges. Providers must reduce costs, improve transparency, and improve the …

Data-Driven Operational And Safety Analysis Of Emerging Shared Electric Scooter Systems, Qingyu Ma

Computational Modeling & Simulation Engineering Theses & Dissertations

The rapid rise of shared electric scooter (E-Scooter) systems offers many urban areas a new micro-mobility solution. The portable and flexible characteristics have made E-Scooters a competitive mode for short-distance trips. Compared to other modes such as bikes, E-Scooters allow riders to freely ride on different facilities such as streets, sidewalks, and bike lanes. However, sharing lanes with vehicles and other users tends to cause safety issues for riding E-Scooters. Conventional methods are often not applicable for analyzing such safety issues because well-archived historical crash records are not commonly available for emerging E-Scooters.

Perceiving the growth of such a micro-mobility …

Improving Accurate Candidates For Missing Data Using Benefit Performance Of (Ml-Som), Abeer Abdullah Al-Mohdar, Mohamed Abdullah Bamatraf

Hadhramout University Journal of Natural & Applied Sciences

Missing data is one of the major challenges in extracting and analyzing knowledge from datasets. The performance of training quality was affected by the appearance of missing data in a dataset. For this reason, there is a need for a quick and reliable method to find possible solutions in order to provide an accurate system. Therefore, the previous studies provided robust ability of Self Organizing Map (SOM) algorithm to deal with the missing values [6, 20]. However, it has a drawback such as an error rate(ERR) in the missing values that increase huge dataset. This study is mainly based on …

Research On Assocoation Information Mining Of Space Reconnaissance Equipment System Index, Han Chi, Xiong Wei

Journal of System Simulation

Abstract: The system effectiveness and system contribution rate of the Space Reconnaissance Equipment System (SRES) has a large number of mutally associated indicators. How to identify relationships the association, select the key indicators and clarify the assocition between core indicators and system contribution rate are the key of the evaluation of system effectiveness and contribution rate. Through the joint simulation of MATLAB and STK, the underlying index data of SRES is obtained. Based on the Frequent Pattern-Tree (FP-Tree) algorithm, the assocition information is discovered, the redundancy is removed and the type of indicator assocition is determined, and an optimization model …

Tweet-To-Act: Towards Tweet-Mining Framework For Extracting Terrorist Attack-Related Information And Reporting, Farkhund Iqbal, Rabia Batool, Benjamin C. M. Fung, Saiqa Aleem, Ahmed Abbasi, Abdul Rehman Javed

All Works

The widespread popularity of social networking is leading to the adoption of Twitter as an information dissemination tool. Existing research has shown that information dissemination over Twitter has a much broader reach than traditional media and can be used for effective post-incident measures. People use informal language on Twitter, including acronyms, misspelled words, synonyms, transliteration, and ambiguous terms. This makes incident-related information extraction a non-trivial task. However, this information can be valuable for public safety organizations that need to respond in an emergency. This paper proposes an early event-related information extraction and reporting framework that monitors Twitter streams, synthesizes event-specific …

Characterizing Search Activities On Stack Overflow, Jiakun Liu, Sebastian Baltes, Christoph Treude, David Lo, Yun Zhang, Xin Xia

Research Collection School Of Computing and Information Systems

To solve programming issues, developers commonly search on Stack Overflow to seek potential solutions. However, there is a gap between the knowledge developers are interested in and the knowledge they are able to retrieve using search engines. To help developers efficiently retrieve relevant knowledge on Stack Overflow, prior studies proposed several techniques to reformulate queries and generate summarized answers. However, few studies performed a large-scale analysis using real-world search logs. In this paper, we characterize how developers search on Stack Overflow using such logs. By doing so, we identify the challenges developers face when searching on Stack Overflow and seek …

Modeling Of Argon Bombardment And Densification Of Low Temperature Organic Precursors Using Reactive Md Simulations And Machine Learning, Kwabena Asante-Boahen

Graduate Theses/Dissertations

In this study, an important aspect of the synthesis process for a-BxC:Hy was systematically modeled by utilizing the Reactive Molecular Dynamics (MD) in modeling the argon bombardment from the orthocarborane molecules as the precursor. The MD simulations are used to assess the dynamics associated with the free radicals that result from the ion bombardment. By applying the Data Mining/Machine Learning analysis into the datasets generated from the large reactive MD simulations, I was able to identify and quality the kinetics of these radicals. Overall, this approach allows for a better understanding of the overall mechanism at the atomistic level of …

Exploring The Use Of Social Media To Infer Relationships Between Demographics, Psychographics And Vaccine Hesitancy, Abhimanyu Kapur

Computer Science Senior Theses

The growing popularity of social media as a platform to obtain information and share one's opinions on various topics makes it a rich source of information for research. In this study, we aimed to develop a framework to infer relationships between demographic and psychographic characteristics of a user and their opinion on a specific narrative - in this case, their stance on taking the COVID-19 vaccine. Twitter was the chosen platform due to the large USA user base and easily available data. Demographic traits included Race, Age, Gender, and Human-vs-Organization Status. Psychographic traits included the Big Five personality traits (Conscientiousness, …

Mining Subgroups From Temporal Data : From The Parts To The Whole, Alexander Gorovits

Legacy Theses & Dissertations (2009 - 2024)

A variety of dynamic systems can be broken down into potentially overlapping subcomponents with varying temporal behavior, ranging from communities in networks, to clusters of trajectories in spatiotemporal data, to co-evolving subsets within multivariate time series. Using explicit regularization on various temporal behaviors within a tensor factorizationframework, I demonstrate means to mine these subgroups along with their temporal activities, as well as how that yields information about the overall systems. Additionally, I adapt this notion of temporal communities to the spatiotemporal setting to develop a reinforcement learning approach for optimizing co-ordinated communication between independent agents.

Robust Inference Of Kinase Activity Using Functional Networks, Serhan Yılmaz, Marzieh Ayati, Daniela Schlatzer, A. Ercüment Çiçek, Mark A. Chance, Mehmet Koyutürk

Computer Science Faculty Publications

Mass spectrometry enables high-throughput screening of phosphoproteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimer’s disease and Parkinson’s disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently …

A Case Study On Player Selection And Team Formation In Football With Machinelearning, Di̇dem Abi̇di̇n

Turkish Journal of Electrical Engineering and Computer Sciences

Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for …

Binary Black Widow Optimization Algorithm For Feature Selection Problems, Ahmed Al-Saedi

Theses and Dissertations (Comprehensive)

This thesis addresses feature selection (FS) problems, which is a primary stage in data mining. FS is a significant pre-processing stage to enhance the performance of the process with regards to computation cost and accuracy to offer a better comprehension of stored data by removing the unnecessary and irrelevant features from the basic dataset. However, because of the size of the problem, FS is known to be very challenging and has been classified as an NP-hard problem. Traditional methods can only be used to solve small problems. Therefore, metaheuristic algorithms (MAs) are becoming powerful methods for addressing the FS problems. …

Applying Web Technologies For Data Mining Of Models Of Advancement Over Time In Space Exploration Technology, Peng-Hung Tsai

Theses and Dissertations

The development of web technologies has progressed rapidly in the past few decades. As a result, these technologies have increasingly gained popularity among researchers as an instrument for data analysis and visualization to aid their work. The purpose of this project is to pursue a new method for mining time-based data to perform curve fitting, using web client technology. This requires comparing the advantages and disadvantages of different JavaScript software designs to determine which is best. Based on the results, we emphasize developing a client-side web application and demonstrate its use through fitting a curve for a new metric to …

Item-Based Collaborative Filtering And Association Rules For A Baseline Recommender In E-Commerce, Jessica Lourenco, Aparna S. Varde

Department of Computer Science Faculty Scholarship and Creative Works

In the ever-growing data-driven world today, data increases in many forms, e.g. e-commerce sites uploading new products, streaming services adding TV shows and movies, and music platforms uploading new songs. It would be highly infeasible for end users to quickly browse all this data. Hence recommender systems can benefit end users (individuals as well as companies) in efficiently finding suitable products. Rather than making end users search through a vast array of items, recommender systems can suggest suitable items to users based on popularity of the items and the respective users' buying behavior. Accordingly, in this paper we explore two …