Computer Sciences Commons^™

Zone Path Construction (Zac) Based Approaches For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet

Research Collection School Of Computing and Information Systems

Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the "right" requests to travel together in the "right" available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. This challenge has been addressed in existing work by: (i) generating as many relevant feasible (with respect to the available delay for customers) combinations of requests as possible …

Rethinking Mistake In The Age Of Algorithms: Quoine Pte Ltd V B2c2 Ltd, Vincent Ooi, Kian Peng Soh

Research Collection Yong Pung How School Of Law

Good traders remove emotion from the decision-making process. Automated trading algorithms have enabled this, allowing one to trade round the clock, and without the constant need to monitor one’s investments. But software has gremlins. Given the vast amounts of money involved in such trades, it was only a matter of time before disputes involving automated trading software came before the courts. The decision in Quoine v B2C2 (“Quoine”) represents the first time an apex court in the Commonwealth has ruled on the applicability of contractual principles to situations involving automated trading software.

Coronavirus: Pandemics, Artificial Intelligence And Personal Data: How To Manage Pandemics Using Ai And What That Means For Personal Data Protection, Warren B. Chik

Research Collection Yong Pung How School Of Law

This chapter discusses the hearing of essential and urgent court matters in the Singapore courts during the COVID-19 pandemic. On 27 march 2020, the Singapore judiciary notified courst users that remote hearings were to be implemented for certain types of hearings by means of video and telephone conferencing facilities. Court users were also provided with indicative lists of matters which might be considered essential and urgent.

Coronavirus: Pandemics, Artificial Intelligence And Personal Data: How To Manage Pandemics Using Ai And What That Means For Personal Data Protection, Warren B. Chik

Research Collection Yong Pung How School Of Law

This chapter discusses the hearing of essential and urgent court matters in the Singapore courts during the COVID-19 pandemic. On 27 march 2020, the Singapore judiciary notified courst users that remote hearings were to be implemented for certain types of hearings by means of video and telephone conferencing facilities. Court users were also provided with indicative lists of matters which might be considered essential and urgent.

Solar Power Forecasting Using Wavelet Transform And Machine Learning Approaches, Abdullah Nor Azliana

Student Works (2020-2029)

Generation of photovoltaic (PV) power is intermittent in nature and integration of PV system into the grid system causes an imbalanced power production and power demand. One of the efforts to reduce this problem is to forecast the generation of solar power in the PV system. Solar power forecasting requires the collection of solar power and meteorological data. Hence, this work collected solar power data and various meteorological data (global radiation, tilted radiation, temperature surrounding, humidity surrounding, PV module/ PV panel temperature and wind speed) from Universiti Teknikal Malaysia Melaka (UTeM). A pre-processing process is carried out to ensure that …

Detectif: Unified Detection And Correction Of Iot Faults In Smart Homes, Madhumita Maliick, Archan Misra, Niloy Ganguly, Youngki Lee

Research Collection School Of Computing and Information Systems

This paper tackles the problem of detecting a comprehensive set of sensor faults that can occur in IoT-instrumented smart homes customized to infer Activities of Daily Living (ADL) from the activation of sensor sets. Specifically, sensors can suffer faults that (a) span durations that vary between several seconds to hours, (b) can result in both missing or false-alarm sensor-events. Previous fault detection approaches are geared primarily to identify missing faults (absence of sensor readings) of a permanent (very long-lived) nature, or sporadic false-alarm events. We propose DetectIF, a fault-detection framework that detects faults of varying time duration, and identifies both …

Deepstyle: User Style Embedding For Authorship Attribution Of Short Texts, Zhiqiang Hu, Roy Ka-Wei Lee, Lei Wang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Authorship attribution (AA), which is the task of finding the owner of a given text, is an important and widely studied research topic with many applications. Recent works have shown that deep learning methods could achieve significant accuracy improvement for the AA task. Nevertheless, most of these proposed methods represent user posts using a single type of features (e.g., word bi-grams) and adopt a text classification approach to address the task. Furthermore, these methods offer very limited explainability of the AA results. In this paper, we address these limitations by proposing DeepStyle, a novel embedding-based framework that learns the representations …

Querying Recurrent Convoys Over Trajectory Data, Munkh-Erdene Yadamjav, Zhifeng Bao, Baihua Zheng, Farhana M. Choudhury, Hanan Samet

Research Collection School Of Computing and Information Systems

Moving objects equipped with location-positioning devices continuously generate a large amount of spatio-temporal trajectory data. An interesting finding over a trajectory stream is a group of objects that are travelling together for a certain period of time. Existing studies on mining co-moving objects do not consider an important correlation between co-moving objects, which is the reoccurrence of the movement pattern. In this study, we define a problem of finding recurrent pattern of co-moving objects from streaming trajectories and propose an efficient solution that enables us to discover recent co-moving object patterns repeated within a given time period. Experimental results on …

The Future Of Work Now: The Multi-Faceted Mall Security Guard At A Multi-Faceted Jewel, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.

On Modeling Labor Markets For Fine-Grained Insights, Hendrik Santoso Sugiarto, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

The labor market consists of job seekers looking for jobs, and job openings waiting for applications. Classical labor market models assume that salary is the primary factor explaining why job-seekers select certain jobs. In practice, job seeker behavior is much more complex and there are other factors that should be considered. In this paper, we therefore propose the Probabilistic Labor Model (PLM) which considers salary satisfaction, topic preference matching, and accessibility as important criteria for job seekers to decide when they apply for jobs. We also determine the user and job latent variables for each criterion and define a graphical …

Urban Scale Trade Area Characterization For Commercial Districts With Cellular Footprints, Yi Zhao, Zimu Zhou, Xu Wang, Tongtong Liu, Zheng Yang

Research Collection School Of Computing and Information Systems

Understanding customer mobility patterns to commercial districts is crucial for urban planning, facility management, and business strategies. Trade areas are a widely applied measure to quantify where the visitors are from. Traditional trade area analysis is limited to small-scale or store-level studies, because information such as visits to competitor commercial entities and place of residence is collected by labour-intensive questionnaires or heavily biased location-based social media data. In this article, we propose CellTradeMap, a novel district-level trade area analysis framework using mobile flow records (MFRs), a type of fine-grained cellular network data. We show that compared to traditional cellular data …

Persona Perception Scale: Development And Exploratory Validation Of An Instrument For Evaluating Individuals' Perceptions Of Personas, Joni Salminen, Joao M. Santos, Haewoon Kwak, Jisun An, Soon-Gyo Jung

Research Collection School Of Computing and Information Systems

Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both …

Attribute-Based Encryption For Cloud Computing Access Control: A Survey, Yinghui Zhang, Robert H. Deng, Shengmin Xu, Jianfei Sun, Qi Li, Dong Zheng

Research Collection School Of Computing and Information Systems

Attribute-based encryption (ABE) for cloud computing access control is reviewed in this article. A taxonomy and comprehensive assessment criteria of ABE are first proposed. In the taxonomy, ABE schemes are assorted into key-policy ABE (KP-ABE) schemes, ciphertext-policy ABE (CP-ABE) schemes, anti-quantum ABE schemes, and generic constructions. In accordance with cryptographically functional features, CP-ABE is further divided into nine subcategories with regard to basic functionality, revocation, accountability, policy hiding, policy updating, multi-authority, hierarchy, offline computation, and outsourced computation. In addition, a systematical methodology for discussing and comparing existing ABE schemes is proposed. For KP-ABE and each type of CP-ABE, the corresponding …

Pricing And Equilibrium In On-Demand Ride-Pooling Markets, Jintao Ke, Hai Yang, Xinwei Li, Hai Wang, Jieping Ye

Research Collection School Of Computing and Information Systems

With the recent rapid growth of technology-enabled mobility services, ride-sourcing platforms, such as Uber and DiDi, have launched commercial on-demand ride-pooling programs that allow drivers to serve more than one passenger request in each ride. Without requiring the prearrangement of trip schedules, these programs match on-demand passenger requests with vehicles that have vacant seats. Ride-pooling programs are expected to offer benefits for both individual passengers in the form of cost savings and for society in the form of traffic alleviation and emission reduction. In addition to some exogenous variables and environments for ride-sourcing market, such as city size and population …

Hybrid Deep Neural Networks For Mining Heterogeneous Data, Xiurui Hou

Dissertations

In the era of big data, the rapidly growing flood of data represents an immense opportunity. New computational methods are desired to fully leverage the potential that exists within massive structured and unstructured data. However, decision-makers are often confronted with multiple diverse heterogeneous data sources. The heterogeneity includes different data types, different granularities, and different dimensions, posing a fundamental challenge in many applications. This dissertation focuses on designing hybrid deep neural networks for modeling various kinds of data heterogeneity.

The first part of this dissertation concerns modeling diverse data types, the first kind of data heterogeneity. Specifically, image data and …

Enrichment Of Ontologies Using Machine Learning And Summarization, Hao Liu

Dissertations

Biomedical ontologies are structured knowledge systems in biomedicine. They play a major role in enabling precise communications in support of healthcare applications, e.g., Electronic Healthcare Records (EHR) systems. Biomedical ontologies are used in many different contexts to facilitate information and knowledge management. The most widely used clinical ontology is the SNOMED CT. Placing a new concept into its proper position in an ontology is a fundamental task in its lifecycle of curation and enrichment.

A large biomedical ontology, which typically consists of many tens of thousands of concepts and relationships, can be viewed as a complex network with concepts as …

Crowdsourcing Atop Blockchains, Yuan Lu

Dissertations

Traditional crowdsourcing systems, such as Amazon's Mechanical Turk (MTurk), though once acquiring great economic successes, have to fully rely on third-party platforms to serve between the requesters and the workers for basic utilities. These third-parties have to be fully trusted to assist payments, resolve disputes, protect data privacy, manage user authentications, maintain service online, etc. Nevertheless, tremendous real-world incidents indicate how elusive it is to completely trust these platforms in reality, and the reduction of such over-reliance becomes desirable.

In contrast to the arguably vulnerable centralized approaches, a public blockchain is a distributed and transparent global "consensus computer" that is …

Machine Learning For Scientific Data Mining And Solar Eruption Prediction, Hao Liu

Dissertations

This dissertation explores new machine learning techniques and adapts them to mine scientific data, specifically data from solar physics and space weather studies. The dissertation tackles three important problems in heliophysics: solar flare prediction, coronal mass ejection (CME) prediction and Stokes inversion.

First, the dissertation presents a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a certain class of solar flare within the next 24 hours. The essence of this approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. The …

Energy And Performance-Optimized Scheduling Of Tasks In Distributed Cloud And Edge Computing Systems, Haitao Yuan

Dissertations

Infrastructure resources in distributed cloud data centers (CDCs) are shared by heterogeneous applications in a high-performance and cost-effective way. Edge computing has emerged as a new paradigm to provide access to computing capacities in end devices. Yet it suffers from such problems as load imbalance, long scheduling time, and limited power of its edge nodes. Therefore, intelligent task scheduling in CDCs and edge nodes is critically important to construct energy-efficient cloud and edge computing systems. Current approaches cannot smartly minimize the total cost of CDCs, maximize their profit and improve quality of service (QoS) of tasks because of aperiodic arrival …

Global Optimization Algorithms For Image Registration And Clustering, Cuicui Zheng

Dissertations

Global optimization is a classical problem of finding the minimum or maximum value of an objective function. It has applications in many areas, such as biological image analysis, chemistry, mechanical engineering, financial analysis, deep learning and image processing. For practical applications, it is important to understand the efficiency of global optimization algorithms. This dissertation develops and analyzes some new global optimization algorithms and applies them to practical problems, mainly for image registration and data clustering.

First, the dissertation presents a new global optimization algorithm which approximates the optimum using only function values. The basic idea is to use the points …

Changing The Focus: Worker-Centric Optimization In Human-In-The-Loop Computations, Mohammadreza Esfandiari

Dissertations

A myriad of emerging applications from simple to complex ones involve human cognizance in the computation loop. Using the wisdom of human workers, researchers have solved a variety of problems, termed as “micro-tasks” such as, captcha recognition, sentiment analysis, image categorization, query processing, as well as “complex tasks” that are often collaborative, such as, classifying craters on planetary surfaces, discovering new galaxies (Galaxyzoo), performing text translation. The current view of “humans-in-the-loop” tends to see humans as machines, robots, or low-level agents used or exploited in the service of broader computation goals. This dissertation is developed to shift the focus back …

An Automated Feedback System To Support Student Learning Of Conceptual Knowledge In Writing-To-Learn Activities, Ye Xiong

Dissertations

As a pedagogical strategy, Writing-to-Learn (WTL) intends to use writing to improve students’ understanding of course content. However, most of the existing feedback systems for writing are mainly focused on improving students’ writing skills rather than their conceptual development. In this dissertation, an automatic approach is proposed to generate timely, actionable, and individualized feedback based on comparing knowledge representations extracted from lecture slides and individual students’ writing assignments. The novelty of the proposed approach lies in the feedback generation: to help students assimilate new knowledge into their existing knowledge better, their current knowledge is modeled as a set of matching …

Mind Maps And Machine Learning: An Automation Framework For Qualitative Research In Entrepreneurship Education, Yasser Farha

Dissertations

Entrepreneurship Education researchers often measure entrepreneurial motivation of college students. It is important for stakeholders, such as policymakers and educators, to assert if entrepreneurship education can encourage students to become entrepreneurs, as well as to understand factors that influence entrepreneurial motivation. For that purpose, researchers have used different methods and instruments to measure students' entrepreneurial motivation. Most of these methods are quantitative, e.g., closed-ended surveys, whereas qualitative methods, e.g., open-ended surveys, are rarely used.

Mind maps are an attractive qualitative survey tool because they capture the individual's reflections, thoughts, and experiences. For Entrepreneurship Education, mind maps can be utilized to …

Towards Practical Homomorphic Encryption And Efficient Implementation, Gyana R. Sahu

Dissertations

Cloud computing has gained significant traction over the past few years and its application continues to soar as evident from its rapid adoption in various industries. One of the major challenges involved in cloud computing services is the security of sensitive information as cloud servers have been often found to be vulnerable to snooping by malicious adversaries. Such data privacy concerns can be addressed to a greater extent by enforcing cryptographic measures. Fully homomorphic encryption (FHE), a special form of public key encryption has emerged as a primary tool in deploying such cryptographic security assurances without sacrificing many of the …

Analyzing And Assisting Patient Decision-Making In Online Health Communities, Mingda Li

Dissertations

In recent years, many users have joined online health communities (OHC) to seek information, suggestions and social support. However, little has been studied about the roles of OHCs in patients' decision-making processes. The aim of this research is to analyze OHC data to better understand patient decision-making processes, and to provide assistance to OHC users in their decision-making.

In order to analyze or assist patients in their decision-making, a novel classification model is designed to identify discussion threads in OHC that are related to decision making. This is achieved by building a two-step combined deep learning model. Empirical evaluation shows …

Understanding The Complex Ethical Landscape Of Artificial Intelligence Adoptions, Chrissann R. Ruehle

USF Tampa Graduate Theses and Dissertations

Although Artificial Intelligence (AI) has existed since the 1950’s, it has experienced a series of expansions and declines over the years. Currently, AI is on an upward trajectory and has prompted the fourth industrial revolution as many scientists have noted. Some firms have rapidly embraced this technology and experienced growth while others have been slow to adopt. Naturally, this expansion often has societal impacts. The aim of this study is to explore ethical considerations that arise during the adoption of this technology. This research addressed three questions: 1. How do market and regulatory forces reportedly shape Artificial Intelligence adoptions? 2. …

Systems For Free Parking Assignment, Abeer M. Hakeem

Dissertations

Finding a free, curbside parking spaces in metropolitan areas, especially during rush hours, is difficult for drivers. The difficulty arises from not knowing where the available spaces may be at that time; and, even if the spaces are known, many vehicles may pursue the same spaces, causing serious parking contention and traffic congestion. This dissertation presents three cost-effective and easily deployable free parking assignment systems that optimize the travel time of the drivers.

The first contribution is the Free Parking System (FPS), a centralized solution that solves the curbside parking problem. Unlike existing solutions, FPS is cost-effective, as it does …

Ranking Volatility In Building Energy Consumption Using Ensemble Learning And Information Entropy, Kunal Sharma, Jung-Ho Lewe

Georgia Journal of Science

Given the rise in building energy consumption and demand worldwide, energy inefficiency detection has become extremely important. A significant portion of the energy used in commercial buildings is wasted as a result of poor maintenance, degradation or improperly controlled equipment. Most facilities employ sensors to track energy consumption across multiple buildings. Smart fault detection and diagnostic systems use various anomaly detection techniques to discover point anomalies in consumption. While these systems work reasonably well in detecting equipment anomalies over short-term intervals, further exploration is needed in finding methods that consider long-term consumption to detect anomalous buildings. This paper presents a …

An Effective Method For Attribute Subset Selection, Considering The Resource In Pattern Recognition, Bakhtiyorjon Bakirovich Akbaraliev

Chemical Technology, Control and Management

An analytical method for determining informative sets of features (INP) is developed, taking into account the resource for criteria based on the use of a measure of dispersion of classified objects. The areas of existence of the solution are defined. The statements and properties for the Fischer-type information criterion are proved, using which the proposed analytical method for determining the INP guarantees optimal results in the sense of maximizing the selected functional. The appropriateness of choosing this type of informative criterion is justified. A method for transforming attributes is proposed. The universality of the method in relation to the type …

Prerequisite Course Recommendation Based On Course Description And Students’ Grades, Haozhe Zhou

The Journal of Purdue Undergraduate Research

No abstract provided.