Computer Sciences Commons^™

Situation-Aware Authenticated Video Broadcasting Over Train-Trackside Wifi Networks, Yongdong Wu, Dengpan Ye, Zhuo Wei, Qian Wang, William Tan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Live video programmes can bring in better travel experience for subway passengers and earn abundant advertisement revenue for subway operators. However, because the train-trackside channels for video dissemination are easily accessible to anyone, the video traffic are vulnerable to attacks which may cause deadly tragedies. This paper presents a situation-aware authenticated video broadcasting scheme in the railway network which consists of train, on-board sensor, trackside GSM-R (Global System for Mobile Communications-Railway) device, WiFi AP (Access Point), and train control center. Specifically, the scheme has four modules: (1) a train uses its on-board sensors to obtain its speed, location, and RSSI …

A Driver Guidance System For Taxis In Singapore, Shashi Shekhar Jha, Shih-Fen Cheng, Meghna Lowalekar, Nicholas Wong, Rishikeshan Rajendram, Pradeep Varakantham, Nghia Troung Troung, Firmansyah Bin Abd Rahman

Research Collection School Of Computing and Information Systems

Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab.Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multiagent optimization technology could help taxi drivers compete against more technologically advanced service platforms. Our system has been in field trial …

Taxis Strike Back: A Field Trial Of The Driver Guidance System, Shih-Fen Cheng, Shashi Shekhar Jha, Rishikeshan Rajendram

Research Collection School Of Computing and Information Systems

Traditional taxi fleet operators world-over have been facing intense competitions from various ride-hailing services such as Uber and Grab (specific to the Southeast Asia region). Based on our studies on the taxi industry in Singapore, we see that the emergence of Uber and Grab in the ride-hailing market has greatly impacted the taxi industry: the average daily taxi ridership for the past two years has been falling continuously, by close to 20% in total. In this work, we discuss how efficient real-time data analytics and large-scale multi-agent optimization technology could potentially help taxi drivers compete against more technologically advanced service …

Online Active Learning With Expert Advice, Shuji Hao, Peiying Hu, Peilin Zhao, Steven C. H. Hoi, Chunyan Miao

Research Collection School Of Computing and Information Systems

In literature, learning with expert advice methods usually assume that a learner always obtain the true label of every incoming training instance at the end of each trial. However, in many real-world applications, acquiring the true labels of all instances can be both costly and time consuming, especially for large-scale problems. For example, in the social media, data stream usually comes in a high speed and volume, and it is nearly impossible and highly costly to label all of the instances. In this article, we address this problem with active learning with expert advice, where the ground truth of an …

Adopt: Combining Parameter Tuning And Adaptive Operator Ordering For Solving A Class Of Orienteering Problems, Aldy Gunawan, Hoong Chuin Lau, Kun Lu

Research Collection School Of Computing and Information Systems

Two fundamental challenges in local search based metaheuristics are how to determine parameter configurations and design the underlying Local Search (LS) procedure. In this paper, we propose a framework in order to handle both challenges, called ADaptive OPeraTor Ordering (ADOPT). In this paper, The ADOPT framework is applied to two metaheuristics, namely Iterated Local Search (ILS) and a hybridization of Simulated Annealing and ILS (SAILS) for solving two variants of the Orienteering Problem: the Team Dependent Orienteering Problem (TDOP) and the Team Orienteering Problem with Time Windows (TOPTW). This framework consists of two main processes. The Design of Experiment (DOE) …

Searching For The X-Factor: Exploring Corpus Subjectivity For Word Embeddings, Maksim Tkachenko, Chong Cher Chia, Hady W. Lauw

Research Collection School Of Computing and Information Systems

We explore the notion of subjectivity, and hypothesize that word embeddings learnt from input corpora of varying levels of subjectivity behave differently on natural language processing tasks such as classifying a sentence by sentiment, subjectivity, or topic. Through systematic comparative analyses, we establish this to be the case indeed. Moreover, based on the discovery of the outsized role that sentiment words play on subjectivity-sensitive tasks such as sentiment classification, we develop a novel word embedding SentiVec which is infused with sentiment information from a lexical resource, and is shown to outperform baselines on such tasks.

Disease Gene Classification With Metagraph Representations, Sezin Kircali Ata, Yuan Fang, Min Wu, Xiao-Li Li, Xiaokui Xiao

Research Collection School Of Computing and Information Systems

This chapter is based on exploiting the network-based representations of proteins, metagraphs, in protein-protein interaction network to identify candidate disease-causing proteins. Protein-protein interaction (PPI) networks are effective tools in studying the functional roles of proteins in the development of various diseases. However, they are insufficient without the support of additional biological knowledge for proteins such as their molecular functions and biological processes. To enhance PPI networks, we utilize biological properties of individual proteins as well. More specifically, we integrate keywords from UniProt database describing protein properties into the PPI network and construct a novel heterogeneous PPI-Keyword (PPIK) network consisting …

A Bayesian Latent Variable Model Of User Preferences With Item Context, Aghiles Salah, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Personalized recommendation has proven to be very promising in modeling the preference of users over items. However, most existing work in this context focuses primarily on modeling user-item interactions, which tend to be very sparse. We propose to further leverage the item-item relationships that may reflect various aspects of items that guide users’ choices. Intuitively, items that occur within the same “context” (e.g., browsed in the same session, purchased in the same basket) are likely related in some latent aspect. Therefore, accounting for the item’s context would complement the sparse user-item interactions by extending a user’s preference to other items …

Deep Specification Mining, Tien-Duy B. Le, David Lo

Research Collection School Of Computing and Information Systems

Formal specifications are essential but usually unavailable in software systems. Furthermore, writing these specifications is costly and requires skills from developers. Recently, many automated techniques have been proposed to mine specifications in various formats including finite-state automaton (FSA). However, more works in specification mining are needed to further improve the accuracy of the inferred specifications. In this work, we propose Deep Specification Miner (DSM), a new approach that performs deep learning for mining FSA-based specifications. Our proposed approach uses test case generation to generate a richer set of execution traces for training a Recurrent Neural Network Based Language Model (RNNLM). …

Characterizing Common And Domain-Specific Package Bugs: A Case Study On Ubuntu, Xiaoxue Ren, Qiao Huang, Xin Xia, Zhenchang Xing, Lingfeng Bao, David Lo

Research Collection School Of Computing and Information Systems

Ubuntu is an open source software platform that runs everywhere from the smartphone, the tablet and the PC to the server and the cloud. In Ubuntu, there are many self-contained or third-party software packages for different use, and a bug report in Ubuntu could affect one or more packages simultaneously. Identifying the common package bugs in Ubuntu can help both developers and users better understand the packages they are developing or using, and also provide further guidelines to developers of similar packages in the future. In this paper, we perform a large-scale empirical study of common package bugs on Ubuntu …

Rumor Detection On Twitter With Tree-Structured Recursive Neural Networks, Jing Ma, Wei Gao, Kam-Fai Wong

Research Collection School Of Computing and Information Systems

Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …

Stackelberg Security Games: Looking Beyond A Decade Of Success, Arunesh Sinha, Fei Fang, Bo An, Christopher Kiekintveld, Milind Tambe

Research Collection School Of Computing and Information Systems

The Stackelberg Security Game (SSG) model has been immensely influential in security research since it was introduced roughly a decade ago. Furthermore, deployed SSG-based applications are one of most successful examples of game theory applications in the real world. We present a broad survey of recent technical advances in SSG and related literature, and then look to the future by highlighting the new potential applications and open research problems in SSG.

A Unified Approach To Route Planning For Shared Mobility, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Jieping Ye, Ke Xu

Research Collection School Of Computing and Information Systems

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In this …

Analysis Of Public Transportation Patterns In A Densely Populated City With Station-Based Shared Bikes, Di Wang, Evan Wu, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Densely populated cities face great challenges of high transportation demand and limited physical space. Thus, in these cities, the public transportation system is heavily relied on. Conventional public transportation modes such as bus, taxi and subway have been globally deployed over the past century. In the last decade, a new type of public transportation mode, shared bike, emerged in many cities. These shared bikes are deployed by either government-regulated or profit-driven companies and are either station-based or station-less. Nonetheless, all of them are designed to better solve the last-mile problem in densely populated cities as complements to the conventional public …

Detecting Personal Intake Of Medicine From Twitter, Debanjan Mahata, Jasper Friedrichs, Rajiv Ratn Shah, Jing Jiang

Research Collection School Of Computing and Information Systems

Mining social media messages such as tweets, blogs, and Facebook posts for health and drug related information has received significant interest in pharmacovigilance research. Social media sites (e.g., Twitter), have been used for monitoring drug abuse, adverse reactions to drug usage, and analyzing expression of sentiments related to drugs. Most of these studies are based on aggregated results from a large population rather than specific sets of individuals. In order to conduct studies at an individual level or specific groups of people, identifying posts mentioning intake of medicine by the user is necessary. Toward this objective we develop a classifier …

Understanding Generalization And Optimization Performance Of Deep Cnns, Pan Zhou, Jiashi Feng

Research Collection School Of Computing and Information Systems

This work aims to provide understandings on the remarkable success of deep convolutional neural networks (CNNs) by theoretically analyzing their generalization performance and establishing optimization guarantees for gradient descent based training algorithms. Specifically, for a CNN model consisting of l convolutional layers and one fully connected layer, we prove that its generalization error is bounded by O( p θ%/n e ) where θ denotes freedom degree of the network parameters and %e = O(log(Ql i=1 bi(ki − si + 1)/p) + log(bl+1)) encapsulates architecture parameters including the kernel size ki , stride si , pooling size p and parameter magnitude …

Knowledge-Aware Attentive Neural Network For Ranking Question Answer Pairs, Ying Shen, Yang Deng, Min Yang, Yaliang Li, Nan Du, Wei Fan, Kai Lei

Research Collection School Of Computing and Information Systems

Ranking question answer pairs has attracted increasing attention recently due to its broad applications such as information retrieval and question answering (QA). Significant progresses have been made by deep neural networks. However, background information and hidden relations beyond the context, which play crucial roles in human text comprehension, have received little attention in recent deep neural networks that achieve the state of the art in ranking QA pairs. In the paper, we propose KABLSTM, a Knowledge-aware Attentive Bidirectional Long Short-Term Memory, which leverages external knowledge from knowledge graphs (KG) to enrich the representational learning of QA sentences. Specifically, we develop …

Effect Of Gamification On Intrinsic Motivation, Edna Chan, Fiona Fui-Hoon Nah, Qizhang Liu, Zhiwei Lu

Research Collection School Of Computing and Information Systems

Gamification has been increasing in popularity in a variety of online context, including online learning. However, its impact on intrinsic motivation is still unclear. In this research, we carried out an experiment to assess the impact of providing two gamification features in an online learning system – point and leaderboard – on intrinsic motivation.

Analysis Of Bulk Power System Resilience Using Vulnerability Graph, Md Ariful Haque

Computational Modeling & Simulation Engineering Theses & Dissertations

Critical infrastructure such as a Bulk Power System (BPS) should have some quantifiable measure of resiliency and definite rule-sets to achieve a certain resilience value. Industrial Control System (ICS) and Supervisory Control and Data Acquisition (SCADA) networks are integral parts of BPS. BPS or ICS are themselves not vulnerable because of their proprietary technology, but when the control network and the corporate network need to have communications for performance measurements and reporting, the ICS or BPS become vulnerable to cyber-attacks. Thus, a systematic way of quantifying resiliency and identifying crucial nodes in the network is critical for addressing the cyber …

Novel Technique For Gait Analysis Using Two Waist Mounted Gyroscopes, Ahmed Nasr

Computer Science Theses & Dissertations

Analysis of the human gait is used in many applications such as medicine, sports, and person identification. Several research studies focused on the use of MEMS inertial sensors for gait analysis and showed promising results. The miniaturization of these sensors and their wearability allowed the analysis of gait on a long term outside of the laboratory environment which can reveal more information about the person and introduced the use of gait analysis in new applications such as indoor localization.

Step detection and step length estimation are two basic and important gait analysis tasks. In fact, step detection is a prerequisite …

Identification And Optimal Linear Tracking Control Of Odu Autonomous Surface Vehicle, Nadeem Khan

Mechanical & Aerospace Engineering Theses & Dissertations

Autonomous surface vehicles (ASVs) are being used for diverse applications of civilian and military importance such as: military reconnaissance, sea patrol, bathymetry, environmental monitoring, and oceanographic research. Currently, these unmanned tasks can accurately be accomplished by ASVs due to recent advancements in computing, sensing, and actuating systems. For this reason, researchers around the world have been taking interest in ASVs for the last decade. Due to the ever-changing surface of water and stochastic disturbances such as wind and tidal currents that greatly affect the path-following ability of ASVs, identification of an accurate model of inherently nonlinear and stochastic ASV system …

Deep Learning For Segmentation Of 3d Cryo-Em Images, Devin Reid Haslam

Computer Science Theses & Dissertations

Cryo-electron microscopy (cryo-EM) is an emerging biophysical technique for structural determination of protein complexes. However, accurate detection of secondary structures is still challenging when cryo-EM density maps are at medium resolutions (5-10 Å). Most existing methods are image processing methods that do not fully utilize available images in the cryo-EM database. In this paper, we present a deep learning approach to segment secondary structure elements as helices and β-sheets from medium- resolution density maps. The proposed 3D convolutional neural network is shown to detect secondary structure locations with an F1 score between 0.79 and 0.88 for six simulated test cases. …

Security Risk Tolerance In Mobile Payment: A Trade-Off Framework, Yong Chen

Information Technology & Decision Sciences Theses & Dissertations

Security is identified as a major barrier for consumers in adopting mobile payment. Although existing literature has incorporated security into the Technology Acceptance Model (TAM), the Unified Theory of Acceptance, and the Use of Technology (UTAUT) and it has investigated the way in which security affects consumers’ acceptance of mobile payment, security is a factor only in diverse research models. Studies of mobile payment that focus on security are not available. Additionally, previous studies of mobile payment are based on Direct Carrier Billing- (DCB)-based mobile payment or Near Field Communication- (NFC)-based mobile payment. The results regarding security might not be …

A Framework For Executable Systems Modeling, Matthew Amissah

Engineering Management & Systems Engineering Theses & Dissertations

Systems Modeling Language (SysML), like its parent language, the Unified Modeling Language (UML), consists of a number of independently derived model languages (i.e. state charts, activity models etc.) which have been co-opted into a single modeling framework. This, together with the lack of an overarching meta-model that supports uniform semantics across the various diagram types, has resulted in a large unwieldy and informal language schema. Additionally, SysML does not offer a built in framework for managing time and the scheduling of time based events in a simulation.

In response to these challenges, a number of auxiliary standards have been offered …

Cybersecurity Architectural Analysis For Complex Cyber-Physical Systems, Martin Trae Span Iii, Logan O. Mailloux, Michael R. Grimaila

Faculty Publications

In the modern military’s highly interconnected and technology-reliant operational environment, cybersecurity is rapidly growing in importance. Moreover, as a number of highly publicized attacks have occurred against complex cyber-physical systems such as automobiles and airplanes, cybersecurity is no longer limited to traditional computer systems and IT networks. While architectural analysis approaches are critical to improving cybersecurity, these approaches are often poorly understood and applied in ad hoc fashion. This work addresses these gaps by answering the questions: 1. “What is cybersecurity architectural analysis?” and 2. “How can architectural analysis be used to more effectively support cybersecurity decision making for complex …

The Silencing Power Of Algorithms: How The Facebook News Feed Algorithm Manipulates Users' Perceptions Of Opinion Climates, Callie Jessica Morgan

University Honors Theses

This extended literature review investigates how the architecture and features of the Facebook Newsfeed algorithm, EdgeRank, can inhibit and facilitate the expression of political opinions. This paper will investigate how Elisabeth Noelle-Neumann's theory on public opinion, Spiral of Silence, can be used to assess the Facebook news feed as a political opinion source that actively shapes users' perceptions of minority and majority opinion climates. The feedback loops created by the algorithm's criteria influences users' decisions to self-censor or express their political opinions with interpersonal connections and unfamiliar connections on the site.

Machine Learning For Inspired, Structured, Lyrical Music Composition, Paul Mark Bodily

Theses and Dissertations

Computational creativity has been called the "final frontier" of artificial intelligence due to the difficulty inherent in defining and implementing creativity in computational systems. Despite this difficulty computer creativity is becoming a more significant part of our everyday lives, in particular music. This is observed in the prevalence of music recommendation systems, co-creational music software packages, smart playlists, and procedurally-generated video games. Significant progress can be seen in the advances in industrial applications such as Spotify, Pandora, Apple Music, etc., but several problems persist. Of more general interest, however, is the question of whether or not computers can exhibit autonomous …

Using Eeg-Validated Music Emotion Recognition Techniques To Classify Multi-Genre Popular Music For Therapeutic Purposes, Dejoy Shastikk Kumaran

The International Student Science Fair 2018

Music is observed to possess significant beneficial effects to human mental health, especially for patients undergoing therapy and older adults. Prior research focusing on machine recognition of the emotion music induces by classifying low-level music features has utilized subjective annotation to label data for classification. We validate this approach by using an electroencephalography-based approach to cross-check the predictions of music emotion made with the predictions from low-level music feature data as well as collected subjective annotation data. Collecting 8-channel EEG data from 10 participants listening to segments of 40 songs from 5 different genres, we obtain a subject-independent classification accuracy …

An Effectual Approach For The Development Of Novel Applications On Digital Platforms, Onkar Shamrao Malgonde

USF Tampa Graduate Theses and Dissertations

The development of novel software applications on digital platforms differs from traditional software development and provides unique challenges to the software development manager and team. Application producers must achieve application-platform match, application-market match, value propositions exceeding platform’s core value propositions, and novelty. These desired properties support a new vision of the software development team as entrepreneurs with a goal of developing novel applications on digital platforms. Digital platforms are characterized by an uncertain, risky, and resource-constrained environment, where existing approaches—plan-driven, ad-hoc, and controlled-flexible—have limited applicability. Building on the theoretical basis of the theory of effectuation from the entrepreneurship domain, this …

Offensive And Defensive Security For Everyday Computer Systems, Ian Markwood

USF Tampa Graduate Theses and Dissertations

This dissertation treats a variety of topics in the computer security domain which have direct impact on everyday life. The first extends false data injection attacks against state estimation in electric power grids and then provides a novel power flow model camouflage method to hamper these attacks. The second deals with automotive theft response, detailing a method for a car to intelligently identify when it has been stolen, based on collected behavioral traits of its driver. The third demonstrates a new attack against the content integrity of the PDF file format, caus- ing humans and computers to see different information …