Computer Sciences Commons^™

Trend: Temporal Event And Node Dynamics For Graph Representation Learning, Zhihao Wen, Yuan Fang

Research Collection School Of Computing and Information Systems

Temporal graph representation learning has drawn significant attention for the prevalence of temporal graphs in the real world. However, most existing works resort to taking discrete snapshots of the temporal graph, or are not inductive to deal with new nodes, or do not model the exciting effects which is the ability of events to influence the occurrence of another event. In this work, We propose TREND, a novel framework for temporal graph representation learning, driven by TempoRal Event and Node Dynamics and built upon a Hawkes process-based graph neural network (GNN). TREND presents a few major advantages: (1) it is …

Cost: Contrastive Learning Of Disentangled Seasonal-Trend Representations For Time Series Forecasting, Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step – we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for long sequence time …

Internet Privacy Concerns Revisited: Oversight From Surveillance And Right To Be Forgotten As New Dimensions, Gaurav Bansal, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

In the post-Snowden revelation era, concerns related to government surveillance and oversight have come to the forefront. The ability of the Internet to remember “everything” (or forget anything) also raises a privacy concern associated with the right to be forgotten (RTBF). In this paper, we examine the conceptualization of Internet privacy concerns (IPC) by extending Hong and Thong's (2013) model with the addition of two dimensions: oversight (i.e., due to surveillance) and the RTBF. We provide theoretical and empirical evidence for our proposed integrated conceptualization. Data were collected from Amazon's Mechanical Turk and analyzed with structural equation modeling using a …

Metaheuristics For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-C Liang, Vanny Minanda, Aldy Gunawan, Hsiang-L. Chen

Research Collection School Of Computing and Information Systems

Vehicle routing problem (VRP), a combinatorial problem, deals with the vehicle’s capacity visiting a particular set of nodes while its variants attempt to fit real-world scenarios. Our study aims to minimise total travelling time, total distance, and the number of vehicles under time-dependent and time windows constraints (TDVRPTW). The harmony search algorithm (HSA) focuses on the harmony memory and pitch adjustment mechanism for new solution construction. Several local search operators and a roulette wheel for the performance improvement were verified via 56 Solomon’s VRP instances by adding a speed matrix. The performance comparison with a genetic algorithm (GA) was completed …

Victor: An Implicit Approach To Mitigate Misinformation Via Continuous Verification Reading, Kuan-Chieh Lo, Shih-Chieh Dai, Aiping Xiong, Jing Jiang, Lun-Wei Ku

Research Collection School Of Computing and Information Systems

We design and evaluate VICTOR, an easy-to-apply module on top of a recommender system to mitigate misinformation. VICTOR takes an elegant, implicit approach to deliver fake-news verifications, such that readers of fake news can continuously access more verified news articles about fake-news events without explicit correction. We frame fake-news intervention within VICTOR as a graph-based question-answering (QA) task, with Q as a fake-news article and A as the corresponding verified articles. Specifically, VICTOR adopts reinforcement learning: it first considers fake-news readers’ preferences supported by underlying news recommender systems and then directs their reading sequence towards the verified news articles. To …

Chatbot4qr: Interactive Query Refinement For Technical Question Retrieval, Neng Zhang, Qiao Huang, Xin Xia, Ying Zou, David Lo, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Technical Q&A sites (e.g., Stack Overflow(SO)) are important resources for developers to search for knowledge about technical problems. Search engines provided in Q&A sites and information retrieval approaches have limited capabilities to retrieve relevant questions when queries are imprecisely specified, such as missing important technical details (e.g., the user's preferred programming languages). Although many automatic query expansion approaches have been proposed to improve the quality of queries by expanding queries with relevant terms, the information missed is not identified. Moreover, without user involvement, the existing query expansion approaches may introduce unexpected terms and lead to undesired results. In this paper, …

Relationships Among The Community Of Inquiry, Achievement Emotions, And Academic Achievement In Asynchronous Online Learning In Higher Education, David H. Tai

USF Tampa Graduate Theses and Dissertations

This research aimed to investigate how achievement emotions predict, mediate, and affect academic achievement in online learning. Online learning has been proliferating, but little is known about how emotion mediates cognition in the Community of Inquiry framework. Recent progress in cognitive neuroscience provided the theoretical foundation for researchers to investigate emotion's role in online learning, especially academic achievement. The researcher of this study adopted a quantitative non-experimental research design to investigate how achievement emotions mediated, predicted, and affected academic achievement in the Community of Inquiry framework. The Partial Least Square Structure Equation Modeling (PLS- SEM) method was adopted for statistical …

Graph Neural Network And Phylogenetic Tree Construction, Gaofeng Pan

Theses and Dissertations

Deep Learning had been widely used in computational biology research in past few years. A great amount of deep learning methods were proposed to solve bioinformatics problems, such as gene function prediction, protein interaction classification, drug effects analysis, and so on; most of these methods yield better solutions than traditional computing methods. However, few methods were proposed to solve problems encountered in evolutionary biology research. In this dissertation, two neural network learning methods are proposed to solve the problems of genome location prediction and median genome generation encountered in phylogenetic tree construction; the ability of neural network learning models on …

Deep Learning Based Generative Materials Design, Yong Zhao

Theses and Dissertations

Discovery of novel functional materials is playing an increasingly important role in many key industries such as lithium batteries for electric vehicles and cell phones. However experimental tinkering of existing materials or Density Functional Theory (DFT) based screening of known crystal structures, two of the major current materials design approaches, are both severely constrained by the limited scale (around 250,000 in ICSD database) and diversity of existing materials and the lack of a sufficient number of materials with annotated properties. How to generate a large number of physically feasible, stable, and synthesizable crystal materials and build accurate property prediction models …

Understanding The Mechanism Of Deep Learning Frameworks In Lesion Detection For Pathological Images With Breast Cancer, Wei-Wen Hsu, Chung-Hao Chen, Chang Hao, Yu-Ling Hou, Xiang Gao, Yun Shao, Xueli Zhang, Jingjing Wang, Tao He, Yanhong Tai

Electrical & Computer Engineering Faculty Publications

With the advances of scanning sensors and deep learning algorithms, computational pathology has drawn much attention in recent years and started to play an important role in the clinical workflow. Computer-aided detection (CADe) systems have been developed to assist pathologists in slide assessment, increasing diagnosis efficiency and reducing misdetections. In this study, we conducted four experiments to demonstrate that the features learned by deep learning models are interpretable from a pathological perspective. In addition, classifiers such as the support vector machine (SVM) and random forests (RF) were used in experiments to replace the fully connected layers and decompose the end-to-end …

Afnd: Arabic Fake News Dataset For The Detection And Classification Of Articles Credibility, Ashwaq Khalil, Moath Jarrah, Monther Aldwairi, Manar Jaradat

All Works

The news credibility detection task has started to gain more attention recently due to the rapid increase of news on different social media platforms. This article provides a large, labeled, and diverse Arabic Fake News Dataset (AFND) that is collected from public Arabic news websites. This dataset enables the research community to use supervised and unsupervised machine learning algorithms to classify the credibility of Arabic news articles. AFND consists of 606912 public news articles that were scraped from 134 public news websites of 19 different Arab countries over a 6-month period using Python scripts. The Arabic fact-check platform, Misbar, is …

Deep Convolutional Neural Network-Based System For Fish Classification, Ahmad Al Smadi, Atif Mehmood, Ahed Abugabah, Eiad Almekhlafi, Ahmad Mohammad Al-Smadi

All Works

In computer vision, image classification is one of the potential image processing tasks. Nowadays, fish classification is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective fish classification method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous fish species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 …

How Can Generative Adversarial Networks Impact Computer Generated Art? Insights From Poetry To Melody Conversion, Sakib Shahriar, Noora Al Roken

All Works

Recent advances in deep learning and generative adversarial networks (GANs), in particular, has enabled interesting applications including photorealistic image generation, image translation, and automatic caption generation. This has opened up possibilities for many cross-domain applications in computer generated arts and literature. Although there are existing software-based approaches for generating musical accompaniment of a given poetry, there are no existing implementation using GANs. This work proposes a novel poetry to melody generation conditioned on poem emotion using GANs. A dataset containing pairs of poetry and melody based on three emotion categories is introduced. Furthermore, various GAN architectures including SpecGAN and WaveGAN …

Computableviz: Mathematical Operators As A Formalism For Visualization Processing And Analysis, Aoyu Wu, Wai Tong, Haotian Li, Dominik Moritz, Yong Wang, Huamin. Qu

Research Collection School Of Computing and Information Systems

Data visualizations are created and shared on the web at an unprecedented speed, raising new needs and questions for processing and analyzing visualizations after they have been generated and digitized. However, existing formalisms focus on operating on a single visualization instead of multiple visualizations, making it challenging to perform analysis tasks such as sorting and clustering visualizations. Through a systematic analysis of previous work, we abstract visualization-related tasks into mathematical operators such as union and propose a design space of visualization operations. We realize the design by developing ComputableViz, a library that supports operations on multiple visualization specifications. To demonstrate …

Securead: A Secure Video Anomaly Detection Framework On Convolutional Neural Network In Edge Computing Environment, Hang Cheng, Ximeng Liu, Huaxiong Wang, Yan Fang, Meiqing Wang, Xiaopeng Zhao

Research Collection School Of Computing and Information Systems

Anomaly detection offers a powerful approach to identifying unusual activities and uncommon behaviors in real-world video scenes. At present, convolutional neural networks (CNN) have been widely used to tackle anomalous events detection, which mainly rely on its stronger ability of feature representation than traditional hand-crafted features. However, massive video data and high cost of CNN model training are a challenge to achieve satisfactory detection results for resource-limited users. In this paper, we propose a secure video anomaly detection framework (SecureAD) based on CNN. Specifically, we introduce additive secret sharing to design several calculation protocols for achieving safe CNN training and …

A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin

Research Collection School Of Computing and Information Systems

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its …

Rescuecastr: Exploring Photos And Live Streaming To Support Contextual Awareness In The Wilderness Search And Rescue Command Post, Brennon Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) is a command-and-control activity where a Command team manages field teams scattered across a large area looking for a lost person. The challenge is that it can be difficult for Command to maintain awareness of field teams and the conditions of the field. We designed RescueCASTR, an interface that explores the idea of deploying field teams with wearable cameras that stream live video or sequential photos periodically to Command that aid contextual awareness. We ran a remote user study with WSAR managers to understand the opportunities and challenges of such a system. We found that …

Asteroids: Exploring Swarms Of Mini-Telepresence Robots For Physical Skill Demonstration, Jiannan Li, Maurício Sousa, Chu Li, Jessie Liu, Yan Chen, Ravin Balakrishnan, Tovi Grossman

Research Collection School Of Computing and Information Systems

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ …

Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to learn scenario representation for a class of SIP instances. Specifically, we design a graph convolutional network based encoder to embed each scenario with the deterministic part of its instance (i.e. context) into a low-dimensional latent space, from which a decoder reconstructs the scenario from its latent representation conditioned on the context. Such a design effectively captures the dependencies of …

Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li

Research Collection School Of Computing and Information Systems

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been designed for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g., sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks. Results: In …

Comai: Enabling Lightweight, Collaborative Intelligence By Retrofitting Vision Dnns, Kasthuri Jayarajah, Dhanuja Wanniarachchige, Tarek Abdelzaher, Archan Misra

Research Collection School Of Computing and Information Systems

While Deep Neural Network (DNN) models have transformed machine vision capabilities, their extremely high computational complexity and model sizes present a formidable deployment roadblock for AIoT applications. We show that the complexity-vs-accuracy-vs-communication tradeoffs for such DNN models can be significantly addressed via a novel, lightweight form of “collaborative machine intelligence” that requires only runtime changes to the inference process. In our proposed approach, called ComAI, the DNN pipelines of different vision sensors share intermediate processing state with one another, effectively providing hints about objects located within their mutually-overlapping Field-of-Views (FoVs). CoMAI uses two novel techniques: (a) a secondary shallow ML …

Verifiable Searchable Encryption Framework Against Insider Keyword-Guessing Attack In Cloud Storage, Yinbin Miao, Robert H. Deng, Kim-Kwang Raymond Choo, Ximeng Liu, Hongwei Li

Research Collection School Of Computing and Information Systems

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand …

Chosen-Instruction Attack Against Commercial Code Virtualization Obfuscators, Shijia Li, Chunfu Jia, Pengda Qiu, Qiyuan Chen, Jiang Ming, Debin Gao

Research Collection School Of Computing and Information Systems

—Code virtualization is a well-known sophisticated obfuscation technique that uses custom virtual machines (VM) to emulate the semantics of original native instructions. Commercial VM-based obfuscators (e.g., Themida and VMProtect) are often abused by malware developers to conceal malicious behaviors. Since the internal mechanism of commercial obfuscators is a black box, it is a daunting challenge for the analyst to understand the behavior of virtualized programs. To figure out the code virtualization mechanism and design deobfuscation techniques, the analyst has to perform reverse-engineering on large-scale highly obfuscated programs. This knowledge learning process suffers from painful cost and imprecision. In this project, …

Gesturelens: Visual Analysis Of Gestures In Presentation Videos, Haipeng Zeng, Xingbo Wang, Yong Wang, Aoyu Wu, Ting Chuen Pong, Huamin Qu

Research Collection School Of Computing and Information Systems

Appropriate gestures can enhance message delivery and audience engagement in both daily communication and public presentations. In this paper, we contribute a visual analytic approach that assists professional public speaking coaches in improving their practice of gesture training through analyzing presentation videos. Manually checking and exploring gesture usage in the presentation videos is often tedious and time-consuming. There lacks an efficient method to help users conduct gesture exploration, which is challenging due to the intrinsically temporal evolution of gestures and their complex correlation to speech content. In this paper, we propose GestureLens, a visual analytics system to facilitate gesture-based and …

Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen

Research Collection School Of Computing and Information Systems

Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient. Our work explores beyond the common practice of transfer learning with a single pre-trained model. We focus on the task of Vietnamese sentiment classification and propose LIFA, a framework to learn a unified embedding from several pre-trained models. We further propose two more LIFA variants that encourage the pre-trained models to either cooperate or compete with one another. Studying these variants sheds light on the success of LIFA by showing that sharing knowledge among the …

Sibnet: Food Instance Counting And Segmentation, Huu-Thanh. Nguyen, Chong-Wah Ngo, Wing-Kwong Chan

Research Collection School Of Computing and Information Systems

Food computing has recently attracted considerable research attention due to its significance for health risk analysis. In the literature, the majority of research efforts are dedicated to food recognition. Relatively few works are conducted for food counting and segmentation, which are essential for portion size estimation. This paper presents a deep neural network, named SibNet, for simultaneous counting and extraction of food instances from an image. The problem is challenging due to varying size and shape of food as well as arbitrary viewing angle of camera, not to mention that food instances often occlude each other. SibNet is novel for …

User Satisfaction Estimation With Sequential Dialogue Act Modeling In Goal-Oriented Conversational Systems, Yang Deng, Wenxuan Zhang, Wai Lam, Hong Cheng, Helen Meng

Research Collection School Of Computing and Information Systems

User Satisfaction Estimation (USE) is an important yet challenging task in goal-oriented conversational systems. Whether the user is satisfied with the system largely depends on the fulfillment of the user’s needs, which can be implicitly reflected by users’ dialogue acts. However, existing studies often neglect the sequential transitions of dialogue act or rely heavily on annotated dialogue act labels when utilizing dialogue acts to facilitate USE. In this paper, we propose a novel framework, namely USDA, to incorporate the sequential dynamics of dialogue acts for predicting user satisfaction, by jointly learning User Satisfaction Estimation and Dialogue Act Recognition tasks. In …

Ai Insurance: How Liability Insurance Can Drive The Responsible Adoption Of Artificial Intelligence In Health Care, Ariel Dora Stern, Avi Goldfarb, Timo Minssen, W. Nicholson Price Ii

Articles

Despite enthusiasm about the potential to apply artificial intelligence (AI) to medicine and health care delivery, adoption remains tepid, even for the most compelling technologies. In this article, the authors focus on one set of challenges to AI adoption: those related to liability. Well-designed AI liability insurance can mitigate predictable liability risks and uncertainties in a way that is aligned with the interests of health care’s main stakeholders, including patients, physicians, and health care organization leadership. A market for AI insurance will encourage the use of high-quality AI, because insurers will be most keen to underwrite those products that are …

Unsupervised Learning With Word Embeddings Captures Quiescent Knowledge From Covid-19 And Materials Science Literature, Tasnim H. Gharaibeh

Dissertations

Millions of scientific papers are produced each year and the scientific literature is continuing to grow at a head-spinning speed. Thus, massive scientific knowledge exists in solid text, but all these publications make it difficult, if not impossible, for researchers to keep in up to date with discoveries, even within a narrow scientific area. This massive amount of information also makes it difficult to find implicit and hidden connections, relationships, and dependencies within the information that may guide the direction of future research or lead to valuable new insights. So, there is a need for algorithms or models that can …

Management Of Data Brokers In Support Of Smart Community Applications, Shadha Tabatabai

Dissertations

The widespread use of smart devices has led to the Internet of Things (IoT) revolution. Big data generated by billions of devices must be analyzed to make better decisions. However, this introduces security, communication, and processing problems. To solve these problems, we develop algorithms to enhance the work of brokers. We focus our efforts on three problems.

In the first problem, brokers are used in the cloud along with Software Defined Network (SDN) switches. We formulate minimizing brokers’ load difference within a reconfiguration budget with the constraint of indivisible topics as an Integer Linear Programming (ILP) problem. We show that …