Databases and Information Systems Commons^™

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Sistem Informasi Geografi (Sig) Pencarian Lokasi Tambal Ban Dengan Pemanfaatan Teknologi Gps, Desy Ika Puspitasari, Zaenuddin Zaenuddin, Fitrah Yuridka

Elinvo (Electronics, Informatics, and Vocational Education)

The occurrence of a tire leak in the middle of a trip can often be caused by many things, such as being hit by a sharp object, the age of the tire is too old, leaking in the former patches, or it can be caused by others. These factors make vehicle users panic and think to immediately find the nearest tire repair location. Almost everyone has a smartphone where there is a Google Map application by utilizing GPS technology so as not to get lost when driving, with GPS technology can be estimated the distance between one location and another. …

Analisis Dan Desain Sistem Informasi Penelitian Dan Pengabdian Masyarakat (Simpenmas) Politeknik Negeri Lhokseumawe, Hari Toha Hidayat

Elinvo (Electronics, Informatics, and Vocational Education)

Information systems of research and community service need to be developed to improve the management of research outputs and community service outcomes by lecturers while making it easier to evaluate tertiary accreditation. This system was developed using the waterfall model. This article aims to describe the stages of system development consisting of: (1) investigating user needs; (2) data and needs analysis in the process of making a system; (3) design; and (4) implementation. The result is the need for a system that specifically can provide facilities for uploading research outputs and community service such as journal publications, textbooks / texts, …

Using The Analytic Hierarchy Process For Decision Making In Smart Traffic Light For High Priority Vehicle, Muhammad Izzuddin Mahali, Eko Marpanaji, Muhammad Adi Febri Setiawan

Elinvo (Electronics, Informatics, and Vocational Education)

Kemacetan sering terjadi di banyak persimpangan jalan kota-kota besar di Indonesia. Sesuatu yang penting seperti kendaraan prioritas sering pula berada pada kemaccetan tersebut. Untuk mengatasi permasalahan tersebut terdapat inovasi baru yaitu Intelligent Traffic Light yang dibekali dengan Aplikasi "Bang Jopin". Namun terdapat permasalahan baru ketika ada kendaraan prioritas melakukan request emergency secara bersamaan pada traffic light yang sama. Penentuan prioritas tidak dapat dilakukan dengan pengurutan saja karena ketika memprioritaskan kendaraan pada traffic light harus mempertimbangkan karakteristik traffic light dan kebiasaan pengendara. Oleh kerena itu, Metode Analitical Hierarchy Process (AHP) merupakan solusi yang tepat dalam menentukan kendaraan prioritas yang didahulukan ketika …

Integrated Mobile And Web-Based Application For Enhancing Delivery Of Hiv/Aids Healthcare Information In Tanzania, Ibrahim Mwammenywa

Tanzania Journal of Engineering and Technology (TJET)

This study aims to develop an integrated e-health platform for enhancing delivery of HIV/AIDS healthcare information in Tanzania, which consists of a mobile application and a web-based system. The study is based on the system's functional and non-functional requirements for an e-health system for delivery of HIV/AIDS healthcare information. The Rapid Application Development (RAD) model was adopted during the system development. The system requirements were modelled into Data Flow Diagram (DFD) in order to obtain the clear flow of the HIV/AIDS healthcare information between the clients and HIV/AIDS healthcare practitioners. With the use of different software development tools and environment …

The Potentials Of Faecal Sludge Treatment Using Local Conditioners In Tanzania: A Review, Doglas Benjamin1

Tanzania Journal of Engineering and Technology (TJET)

Worldwide, every day human beings generate millions of tons of Faecal Sludge (FS), which is rich in water, nutrients, energy, and organic compounds. Yet FS is not being managed in a way that permits us to derive value from its reuse, while at the same time, millions of farmers struggle with depleted soils and lack of water. In most of the developing countries, energy for cooking is mainly derived from cutting of trees, either as wood or charcoal. Resource recovery and reuse from FS can create livelihoods, enhance food security, support green economies, reduce waste and contribute to cost recovery …

Information Extraction From Biomedical Text Using Machine Learning, Deepti Garg

Master's Projects

Inadequate drug experimental data and the use of unlicensed drugs may cause adverse drug reactions, especially in pediatric populations. Every year the U.S. Food and Drug Administration approves human prescription drugs for marketing. The labels associated with these drugs include information about clinical trials and drug response in pediatric population. In order for doctors to make an informed decision about the safety and effectiveness of these drugs for children, there is a need to analyze complex and often unstructured drug labels. In this work, first, an exploratory analysis of drug labels using a Natural Language Processing pipeline is performed. Second, …

Evaluating Data Quality Of Newborn Hearing Screening, Maria C. Sanchez Gomez, Kelly Dundon, Xidong Deng

Journal of Early Hearing Detection and Intervention

Scope

Jurisdictional-based Early Hearing Detection and Intervention Information Systems (EHDI-IS) collect data on the hearing screening and follow-up status of infants across the United States. These systems serve as tools that assist EHDI programs’ staff and partners in their tracking activities and provide a variety of data reports to help ensure that all children who are deaf/hard of hearing (D/HH) are identified early and receive recommended intervention services. The quality and timeliness of the data collected with these systems are crucial to effectively meeting these goals.

Methodology

Forty-eight EHDI programs, funded by the Centers for Disease Control and Prevention (CDC), …

A Hybrid Approach For Multi-Document Text Summarization, Rashmi Varma

Master's Projects

Text summarization has been a long studied topic in the field of natural language processing. There have been various approaches for both extractive text summarization as well as abstractive text summarization. Summarizing texts for a single document is a methodical task. But summarizing multiple documents poses as a greater challenge. This thesis explores the application of Latent Semantic Analysis, Text-Rank, Lex-Rank and Reduction algorithms for single document text summarization and compares it with the proposed approach of creating a hybrid system combining each of the above algorithms, individually, with Restricted Boltzmann Machines for multi-document text summarization and analyzing how all …

Music Retrieval System Using Query-By-Humming, Parth Patel

Master's Projects

Music Information Retrieval (MIR) is a particular research area of great interest because there are various strategies to retrieve music. To retrieve music, it is important to find a similarity between the input query and the matching music. Several solutions have been proposed that are currently being used in the application domain(s) such as Query- by-Example (QBE) which takes a sample of an audio recording playing in the background and retrieves the result. However, there is no efficient approach to solve this problem in a Query-by-Humming (QBH) application. In a Query-by-Humming application, the aim is to retrieve music that is …

Designing Learning Activities For Experiential Learning In A Design Thinking Course, Benjamin Gan, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

One experiential learning design challenge is the duration of learning activities. These learning activities take up time and effort for teachers to design and student to perform. Another design challenge is the minimum instructional guidance of these learning activities which potentially impact the learning effectiveness of novice students. In this paper, we describe our findings of applying experiential learning method in a design thinking course with a list of learning activities performed iteratively. Each of the learning activity varies in their duration required and level of instructional guidance. Our survey seeks to find out which of the learning activities are …

Optimal Management Of Virtual Infrastructures Under Flexible Cloud Service Agreements, Zhiling Guo, Jin Li, Ram Ramesh

Research Collection School Of Computing and Information Systems

A cloud service agreement entails the provisioning of a required set of virtual infrastructure resources at a specified level of availability to a client. The agreement also lays out the price charged to the client and a penalty to the provider when the assured availability is not met. The availability assurance involves backup resource provisioning, and the provider needs to allocate backups cost-effectively by balancing the resource-provisioning costs with the potential penalty costs. We develop stochastic dynamic optimization models of the backup resource-provisioning problem, leading to cost-effective resource-management policies in different practical settings. We present two sets of dynamic provisioning …

Optimal Design And Ownership Structures Of Innovative Retail Payment Systems, Zhiling Guo, Dan Ma

Research Collection School Of Computing and Information Systems

In response to the Fintech trend, an ongoing debate in the banking industry is how to design the new-generation interbank retail payment and settlement system. We propose a two-stage analytical model that takes into account the value-risk tradeoff in the new payment system design, as well as banks’ participation incentives and adoption timing decisions. We find that, as the system base value increases, banks tend to synchronize their investment and adoption decisions. When the system base value is low and banks are heterogeneous, bank association ownership maximizes social welfare. When both the system base value and bank heterogeneity are moderate, …

Self-Organizing Neural Networks For Universal Learning And Multimodal Memory Encoding, Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng

Research Collection School Of Computing and Information Systems

Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models …

A Transformative Concept: From Data Being Passive Objects To Data Being Active Subjects, Hans-Peter Plag, Shelley-Ann Jules-Plag

OES Faculty Publications

The exploitation of potential societal benefits of Earth observations is hampered by users having to engage in often tedious processes to discover data and extract information and knowledge. A concept is introduced for a transition from the current perception of data as passive objects (DPO) to a new perception of data as active subjects (DAS). This transition would greatly increase data usage and exploitation, and support the extraction of knowledge from data products. Enabling the data subjects to actively reach out to potential users would revolutionize data dissemination and sharing and facilitate collaboration in user communities. The three core elements …

Sentiment Analysis, Quantification, And Shift Detection, Kevin Labille

Graduate Theses and Dissertations

This dissertation focuses on event detection within streams of Tweets based on sentiment quantification. Sentiment quantification extends sentiment analysis, the analysis of the sentiment of individual documents, to analyze the sentiment of an aggregated collection of documents. Although the former has been widely researched, the latter has drawn less attention but offers greater potential to enhance current business intelligence systems. Indeed, knowing the proportion of positive and negative Tweets is much more valuable than knowing which individual Tweets are positive or negative. We also extend our sentiment quantification research to analyze the evolution of sentiment over time to automatically detect …

Iomt Malware Detection Approaches: Analysis And Research Challenges, Mohammad Wazid, Ashok Kumar Das, Joel J.P.C. Rodrigues, Sachin Shetty, Youngho Park

VMASC Publications

The advancement in Information and Communications Technology (ICT) has changed the entire paradigm of computing. Because of such advancement, we have new types of computing and communication environments, for example, Internet of Things (IoT) that is a collection of smart IoT devices. The Internet of Medical Things (IoMT) is a specific type of IoT communication environment which deals with communication through the smart healthcare (medical) devices. Though IoT communication environment facilitates and supports our day-to-day activities, but at the same time it has also certain drawbacks as it suffers from several security and privacy issues, such as replay, man-in-the-middle, impersonation, …

Hybrid Recommender Systems Via Spectral Learning And A Random Forest, Alyssa Williams

Electronic Theses and Dissertations

We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first …

Objective Sleep Quality As A Predictor Of Mild Cognitive Impairment In Seniors Living Alone, Brian Chen, Hwee-Pink Tan, Irus Rawtaer, Hwee Xian Tan

Research Collection School Of Computing and Information Systems

Singapore has the fastest ageing population in the Asia Pacific region, with an estimated 82,000 seniors living with dementia. These figures are projected to increase to more than 130,000 by 2030. The challenge is to identify more community dwelling seniors with Mild Cognitive Impairment (MCI), a prodromal state, as it provides an opportunity for evidence-based early intervention to delay the onset of dementia. In this paper, we explore the use of Internet of Things (IoT) systems in detecting MCI symptoms in seniors who are living alone, and accurately grouping them into MCI positive and negative subjects. We present feature extraction …

Study Group Travel Behaviour Patterns From Large-Scale Smart Card Data, Xiancai Tian, Baihua Zheng

Research Collection School Of Computing and Information Systems

In this paper, we aim at studying the group travel behaviour (GTB) patterns from large-scale auto fare collection (AFC) data. GTB is defined as two or more commuters intentionally and regularly traveling together from an origin to a destination. We propose a method to identify GTB accurately and efficiently and apply our method to the Singapore AFC dataset to reveal the GTB patterns of Singapore commuters. The case study proves that our method is able to identify GTB patterns more accurately and efficiently than the state-of-the-art.

Salience-Aware Adaptive Resonance Theory For Large-Scale Sparse Data Clustering, Lei Meng, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Sparse data is known to pose challenges to cluster analysis, as the similarity between data tends to be ill-posed in the high-dimensional Hilbert space. Solutions in the literature typically extend either k-means or spectral clustering with additional steps on representation learning and/or feature weighting. However, adding these usually introduces new parameters and increases computational cost, thus inevitably lowering the robustness of these algorithms when handling massive ill-represented data. To alleviate these issues, this paper presents a class of self-organizing neural networks, called the salience-aware adaptive resonance theory (SA-ART) model. SA-ART extends Fuzzy ART with measures for cluster-wise salient feature modeling. …

Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell

Research Collection School Of Computing and Information Systems

Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …

Gms: Grid-Based Motion Statistics For Fast, Ultra-Robust Feature Correspondence, Jia-Wang Bian, Wen-Yan Lin, Yun Liu, Le Zhang, Sai-Kit Yeung, Ming-Ming Cheng, Ian Reid

Research Collection School Of Computing and Information Systems

Feature matching aims at generating correspondences across images, which is widely used in many computer vision tasks. Although considerable progress has been made on feature descriptors and fast matching for initial correspondence hypotheses, selecting good ones from them is still challenging and critical to the overall performance. More importantly, existing methods often take a long computational time, limiting their use in real-time applications. This paper attempts to separate true correspondences from false ones at high speed. We term the proposed method (GMS) grid-based motion Statistics, which incorporates the smoothness constraint into a statistic framework for separation and uses a grid-based …

Influence, Information And Team Outcomes In Large Scale Software Development, Subhajit Datta

Research Collection School Of Computing and Information Systems

It is widely perceived that the egalitarian ecosystems of large scale open source software development foster effective team outcomes. In this study, we question this conventional wisdom by examining whether and how the centralization of information and influence in a software development team relate to the quality of the team's work products. Analyzing data from more than a hundred real world projects that include development activities over close to a decade, involving 2000+ developers, who collectively resolve more than two hundred thousand defects through discussions covering more than six hundred thousand comments, we arrive at statistically significant evidence indicating that …

Ssrgd: Simple Stochastic Recursive Gradient Descent For Escaping Saddle Points, Zhize Li

Research Collection School Of Computing and Information Systems

We analyze stochastic gradient algorithms for optimizing nonconvex problems. In particular, our goal is to find local minima (second-order stationary points) instead of just finding first-order stationary points which may be some bad unstable saddle points. We show that a simple perturbed version of stochastic recursive gradient descent algorithm (called SSRGD) can find an $(\epsilon,\delta)$-second-order stationary point with $\widetilde{O}(\sqrt{n}/\epsilon^2 + \sqrt{n}/\delta^4 + n/\delta^3)$ stochastic gradient complexity for nonconvex finite-sum problems. As a by-product, SSRGD finds an $\epsilon$-first-order stationary point with $O(n+\sqrt{n}/\epsilon^2)$ stochastic gradients. These results are almost optimal since Fang et al. [2018] provided a lower bound $\Omega(\sqrt{n}/\epsilon^2)$ for finding …

Happy Toilet: A Social Analytics Approach To The Study Of Public Toilet Cleanliness, Eugene W. J. Choy, Winston M. K. Ho, Xiaohang Li, Ragini Verma, Li Jin Sim, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

This study presents a social analytics approach to the study of public toilet cleanliness in Singapore. From popular social media platforms, our system automatically gathers and analyzes relevant public posts that mention about toilet cleanliness in highly frequented locations across the Singapore island - from busy shopping malls to food 'hawker' centers.

Extracting Social Network From Literary Prose, Tarana Tasmin Bipasha

Graduate Theses and Dissertations

This thesis develops an approach to extract social networks from literary prose, namely, Jane Austen’s published novels from eighteenth- and nineteenth- century. Dialogue interaction plays a key role while we derive the networks, thus our technique relies upon our ability to determine when two characters are in conversation. Our process involves encoding plain literary text into the Text Encoding Initiative’s (TEI) XML format, character name identification, conversation and co-occurrence detection, and social network construction. Previous work in social network construction for literature have focused on drama, specifically manually TEI-encoded Shakespearean plays in which character interactions are much easier to track …

Quantum Consensus, Jorden Seet, Paul Griffin

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel consensus mechanism utilizing the quantum properties of qubits. This move from classical computing to quantum computing is shown to theoretically enhance the scalability and speed of distributed consensus as well as improve security and be a potential solution for the problem of blockchain interoperability. Using this method may circumvent the common problem known as the Blockchain Trilemma, enhancing scalability and speed without sacrificing de-centralization or byzantine fault tolerance. Consensus speed and scalability is shown by removing the need for multicast responses and exploiting quantum properties to ensure that only a single multicast is …

Twenty Years Of Open Source Software: From Skepticism To Mainstream, Gregorio Robles, Igor Steinmacher, Paul Adams, Christoph Treude

Research Collection School Of Computing and Information Systems

Open source software (OSS) has conquered the software world. You can see it nearly everywhere, from Internet infrastructure to mobile phones to the desktop. In addition to that, although many OSS practices were viewed with skepticism 20 years ago, several have become mainstream in software engineering today: from development tools such as Git to practices such as modern code reviews.

Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within …