Computer Sciences Commons^™

Neighbourhood Structure Preserving Cross-Modal Embedding For Video Hyperlinking, Yanbin Hao, Chong-Wah Ngo, Benoit Huet

Research Collection School Of Computing and Information Systems

Video hyperlinking is a task aiming to enhance the accessibility of large archives, by establishing links between fragments of videos. The links model the aboutness between fragments for efficient traversal of video content. This paper addresses the problem of link construction from the perspective of cross-modal embedding. To this end, a generalized multi-modal auto-encoder is proposed.& x00A0;The encoder learns two embeddings from visual and speech modalities, respectively, whereas each of the embeddings performs self-modal and cross-modal translation of modalities. Furthermore, to preserve the neighbourhood structure of fragments, which is important for video hyperlinking, the auto-encoder is devised to model data …

Security Techniques For Intelligent Spam Sensing And Anomaly Detection In Online Social Platforms, Monther Aldwairi, Lo'ai Tawalbeh

All Works

Copyright © 2020 Institute of Advanced Engineering and Science. All rights reserved. The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due …

A Survey Of Spatial Crowdsourcing, Yongxin Tong, Zimu Zhou, Yuxiang Zeng, Lei Chen, Cyrus Shahabi

Research Collection School Of Computing and Information Systems

Crowdsourcing is a computing paradigm where humans are actively involved in a computing task, especially for tasks that are intrinsically easier for humans than for computers. Spatial crowdsourcing (SC) is an increasing popular category of crowdsourcing in the era of mobile Internet and sharing economy, where tasks are spatiotemporal and must be completed at a specific location and time. In fact, spatial crowdsourcing has stimulated a series of recent industrial successes including sharing economy for urban services (Uber and Gigwalk) and spatiotemporal data collection (OpenStreetMap and Waze). This survey dives deep into the challenges and techniques brought by the unique …

An Exact Single-Agent Task Selection Algorithm For The Crowdsourced Logistics, Chung-Kyun Han, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

The trend of moving online in the retail industry has created great pressure for the logistics industry to catch up both in terms of volume and response time. On one hand, volume is fluctuating at greater magnitude, making peaks higher; on the other hand, customers are also expecting shorter response time. As a result, logistics service providers are pressured to expand and keep up with the demands. Expanding fleet capacity, however, is not sustainable as capacity built for the peak seasons would be mostly vacant during ordinary days. One promising solution is to engage crowdsourced workers, who are not employed …

Values Of Artificial Intelligence In Marketing, Yingrui Xi

Masters Theses

“Artificial Intelligence (AI) is causing radical changes in marketing and emerging as a competent assistant supporting all areas of the marketing field. The influences and impacts AI has created in various marketing segments have aroused much interest among marketing professionals and academic scholars. Comprehensive and systematic studies on the values of AI in marketing, however, are still lacking and the existing literature fragmented. This research provides a comprehensive review of the existing literature in the relevant fields as well as a series of systematic interviews using the Value-Focused Thinking approach to understand the values of AI in marketing. This research …

Observer-Based Event-Triggered And Set-Theoretic Neuro-Adaptive Controls For Constrained Uncertain Systems, Abdul Ghafoor

Doctoral Dissertations

"In this study, several new observer-based event-triggered and set-theoretic control schemes are presented to advance the state of the art in neuro-adaptive controls. In the first part, six new event-triggered neuro-adaptive control (ETNAC) schemes are presented for uncertain linear systems. These comprehensive designs offer flexibility to choose a design depending upon system performance requirements. Stability proofs for each scheme are presented and their performance is analyzed using benchmark examples. In the second part, the scope of the ETNAC is extended to uncertain nonlinear systems. It is applied to a case of precision formation flight of the microsatellites at the Sun-Earth/Moon …

คลังข้อมูลและระบบสนับสนุนการตัดสินใจของธุรกิจบริการขนส่งทางทะเลสําหรับผลิตภัณฑ์ปิโตรเลียม, กฤติยา ถมยาบัตร

Chulalongkorn University Theses and Dissertations (Chula ETD)

การขนส่งสินค้าทางทะเลเป็นการขนส่งหลักที่คิดเป็นร้อยละ 90 - 95 จากการขนส่งทั่วโลก การให้บริการขนส่งผลิตภัณฑ์ปิโตรเลียมทางทะเลเป็นการขนส่งในประเภทการใช้เรือขนส่งสินค้าเหลว ประเภทเรือบรรทุกน้ำมัน (Oil Tankers) โดยให้บริการขนส่งแก่คู่ค้าธุรกิจโรงกลั่นน้ำมัน และผู้ค้าน้ำมันรายใหญ่เพื่อขนส่งสินค้า เช่น น้ำมันดิบ หรือ น้ำมันเบนซิน เป็นต้น สำนักงานพลังงานระหว่างประเทศ International Energy Agency (IEA) คาดการณ์ความต้องการใช้น้ำมันทั่วโลกจะเพิ่มขึ้นเป็น 104.7 ล้านบาร์เรลต่อวันในปี 2566 เพราะฉะนั้นจึงทำให้ธุรกิจการขนส่งน้ำมันเชื้อเพลิงทางทะเลเป็นที่ต้องการเพิ่มมากขึ้นและยังเป็นโอกาสทางธุรกิจอีกด้วย อย่างไรก็ตามธุรกิจการขนส่งผลิตภัณฑ์ปิโตรเลียมทางทะเลเป็นธุรกิจเฉพาะทางเพราะมีข้อกำหนดและความเสี่ยงที่เกี่ยวข้องกับธุรกิจหลายด้าน เช่น ความเสี่ยงจากความผันผวนของราคาน้ำมัน ซึ่งมีผลกระทบต่อต้นทุนและการดำเนินธุรกิจได้ โครงการ “คลังข้อมูลและระบบสนับสนุนการตัดสินใจของธุรกิจบริการขนส่งทางทะเลสำหรับผลิตภัณฑ์ปิโตรเลียม” นี้ประกอบด้วย 5 ระบบ ได้แก่ (1) ระบบวิเคราะห์ภาพรวมธุรกิจ (2) ระบบวิเคราะห์รายได้และกำไร (3) ระบบวิเคราะห์การควบคุมประสิทธิภาพการขนส่ง (4) ระบบวิเคราะห์การใช้งานเรือ และ (5) ระบบวิเคราะห์ลูกค้า ระบบได้ถูกพัฒนาขึ้นบนฐานข้อมูล Microsoft Azure SQL Database โดยใช้โปรแกรม Tableau Desktop 2020.3 ในการจัดทำระบบวิเคราะห์และแสดงผลข้อมูล ระบบสารสนเทศจากโครงการพิเศษนี้จะช่วยให้ผู้บริหารสามารถวิเคราะห์ข้อมูลในมุมมองต่าง ๆ ได้อย่างถูกต้อง

Modeling And Counteracting Exposure Bias In Recommender Systems, Sami Khenissi, Olfa Nasraoui

Faculty and Staff Scholarship

What we discover and see online, and consequently our opinions and decisions, are becoming increasingly affected by automated machine learned predictions. Similarly, the predictive accuracy of learning machines heavily depends on the feedback data that we provide them. This mutual influence can lead to closed-loop interactions that may cause unknown biases which can be exacerbated after several iterations of machine learning predictions and user feedback. Machine-caused biases risk leading to undesirable social effects ranging from polarization to unfairness and filter bubbles. In this paper, we study the bias inherent in widely used recommendation strategies such as matrix factorization. Then we …

Internal Sorting Of Streaming Numbers Constrained By Limited Memory Size, Suluk Chaikhan

Chulalongkorn University Theses and Dissertations (Chula ETD)

Due to technological advancements, data consumption is exponentially increasing. However, the size of memory storage increases linearly. Therefore, memory storage is insufficient to store big data. Besides, all existing sorting algorithms cannot sort big data in the memory storage with limited capacity, which is much smaller than the data size. Although external sorting algorithms can sort big data, they cannot keep a sorting result on limited memory storage. This dissertation proposed two new sorting algorithms to handle the sorting issue of streaming data in limited memory storage, namely, streaming data sort, and fast streaming data sort. Both sorting algorithms are …

A Content Analysis-Based Approach To Explore Simulation Verification And Identify Its Current Challenges, Christopher J. Lynch, Saikou Y. Diallo, Hamdi Kavak, Jose J. Padilla

VMASC Publications

Verification is a crucial process to facilitate the identification and removal of errors within simulations. This study explores semantic changes to the concept of simulation verification over the past six decades using a data-supported, automated content analysis approach. We collect and utilize a corpus of 4,047 peer-reviewed Modeling and Simulation (M&S) publications dealing with a wide range of studies of simulation verification from 1963 to 2015. We group the selected papers by decade of publication to provide insights and explore the corpus from four perspectives: (i) the positioning of prominent concepts across the corpus as a whole; (ii) a comparison …

Benchmarking Machine Learning Methods For Molecular Property Prediction, Govinda Bahadur Kc

Open Access Theses & Dissertations

Machine learning (ML) techniques have been widely applied in a variety of areas ranging from pattern recognition, natural language processing, and computer games to self-driving cars, clinical diagnostics, and molecular structure prediction easing day to day life of human beings. Drug discovery is an expensive, complex, and time taking process. Currently, the pharma industry is hoping to leverage machine learning methods in expediting the drug discovery process. Molecular property prediction is one of the most important tasks in drug discovery. While developing a new drug relies on a proper understanding of molecular properties, there has been great interest in the …

Towards The Development Of A Cohesive Design-Driven Code Quality Metrics, Omar Masmali

Open Access Theses & Dissertations

Software complexity is an indicator of expected future maintenance and sustainability. Excessive complexity suggests that software or a component of software has a design or implementation that is difficult to understand, modify, and maintain. Several complexity measures have been developed by researchers to identify and characterize degrees of complexity. Code smells are widely adopted as indicators for low code quality. Many studies have adopted fixed threshold values for code smells and other quality metrics. These fixed threshold values often ignore the uniqueness of each software system and the unique roles each component play. Moreover, these thresholds are largely fixed throughout …

Brian Valdez - Dynamics And Control Of A 3-Dof Manipulator With Deep Learning Feedback, Brian Orlando Valdez

Open Access Theses & Dissertations

With the ever-increasing demands in the space domain and accessibility to low-cost small satellite platforms for educational and scientific projects, efforts are being made in various technology capacities including robotics and artificial intelligence in microgravity. The MIRO Center for Space Exploration and Technology Research (cSETR) prepares the development of their second nanosatellite to launch to space and it is with that opportunity that a 3-DOF robotic arm is in development to be one of the payloads in the nanosatellite. Analyses, hardware implementation, and testing demonstrate a potential positive outcome from including the payload in the nanosatellite and a deep learning …

A Comprehensive And Modular Robotic Control Framework For Model-Less Control Law Development Using Reinforcement Learning For Soft Robotics, Charles Sullivan

Open Access Theses & Dissertations

Soft robotics is a growing field in robotics research. Heavily inspired by biological systems, these robots are made of softer, non-linear, materials such as elastomers and are actuated using several novel methods, from fluidic actuation channels to shape changing materials such as electro-active polymers. Highly non-linear materials make modeling difficult, and sensors are still an area of active research. These issues have rendered typical control and modeling techniques often inadequate for soft robotics. Reinforcement learning is a branch of machine learning that focuses on model-less control by mapping states to actions that maximize a specific reward signal. Reinforcement learning has …

Abstraction Techniques In Security Games With Underlying Network Structure, Anjon Basak

Open Access Theses & Dissertations

In a multi-agent system, multiple intelligent agents interact with each other in an environment to achieve their objectives. They can do this because they know which actions are available to them and which actions they prefer to take in a particular situation. The job of game theory is to analyze the interactions of the intelligent agents by different solution techniques and provide analysis such as predicting outcomes or recommending courses of action to specific players. To do so game theory works with a model of real-world scenarios which helps us to make a better decision in our already complex daily …

Understanding The Digital Lives Of Transnational Students: A Case Study, Chowaing Chagra Belekeh

Open Access Theses & Dissertations

The proliferation and the fast-paced evolution of digital information communication technologies (ICTs) in contemporary times have arguably raised concern for us to comprehend what we do with these technologies and what these technologies do for us. The experience of engaging these technologies may not necessarily be the same for everyone—especially students who come from around the world to attain post-graduate degrees in the United States. This research focused on understanding the digital lives, choices, and experiences of transnational students who navigate and negotiate geopolitical borders and boundaries (physical)– in their quest for education. Using a case study analysis and collecting …

A Comparative Study Of The Impact Of Depth In Deep Learning Architectures, Kirsten Byers

Open Access Theses & Dissertations

Machine Learning continues to evolve as applications become more complex. Neural Networks, or Deep Networks, are integral to machine learning and the entire taxonomy of Artificial Intelligence [Sze17]. Intelligent structures and algorithms continue to advance, keeping pace with the complexi-ty of data. Changes in architecture, algorithms, and parameters are necessary to keep up with com-putational complexity and data available. This study focuses on how changes in depth of the archi-tecture affect performance on three distinct datasets, including one on Heart Disease. An adaptable network is created in original code, trained, and tested on these datasets. Its performance parameters are observed …

Deep Learning For Overhead Imagery: Algorithms And Applications, Anthony Manuel Ortiz Cepeda

Open Access Theses & Dissertations

Remote sensing using overhead imagery has critical impact to the way we understand our environment and offers crucial information for scene understanding, climate change research, disaster response, urban planning, forest management, and many other applications. At present, deep learning is increasingly used in remote sensing, but mostly borrowing algorithms developed for natural images in the computer vision community. Specific challenges arise while applying deep learning to remote sensing. These challenges include issues related to the high dimensionality and limited labeled data, security and robustness to adversarial attacks, and model generalization. In this Thesis we focus on tackling these key challenges. …

Comparing Predictive Performance Of Statistical Learning Models On Medical Data, Francis Biney

Open Access Theses & Dissertations

This work investigates the predictive performance of 10 Machine learning models on three medical data including Breast cancer, Heart disease and Prostate cancer. Furthermore, we use the models to identify risk factors that contribute significantly to these diseases.

The models considered include; Logistic regression with L1 and L_2 penalties, Principal component logistic regression(PCR-LR), Partial least squares logistic regression(PLS-LR), Multivariate adaptive regression splines(MARS), Support vector machine with Radial Basis Kernel (SVM-RBK), Random Forest(RF), Gradient Boosting Machines(GBM), Elastic Net (Enet) and Feedforward Neural Network(FFNN). The models were grouped according to their similarities and learning style; i) Linear regularized models: LR-Lasso, LR-Ridge and …

Technological Challenges And Innovations In Cybersecurity And Networking Technology Program, Syed R. Zaidi, Ajaz Sana, Aparicio Carranza

Publications and Research

This era is posing a unique challenge to the Cybersecurity and related Engineering Technology areas, stimulated by the multifaceted technological boom expressed in accelerated globalization, digital transformation, the cloud, mobile access apps, and the Internet of Things (IoT)—where more and more devices are connected to the Internet every day. As the use of new Internet-based technologies increase; so does the risk of theft and misuse of sensitive information. This demands the awareness of cyber-criminality and the need for cyber hygiene in corporations, small businesses, and the government. As the need for experienced cybersecurity specialists has skyrocketed in recent years and …

How Location-Aware Access Control Affects User Privacy And Security In Cloud Computing Systems, Wen Zeng, Reem Bashir, Trevor Wood, Francois Siewe, Helge Janicke, Isabel Wagner

Research outputs 2014 to 2021

The use of cloud computing (CC) is rapidly increasing due to the demand for internet services and communications. The large number of services and data stored in the cloud creates security risks due to the dynamic movement of data, connected devices and users between various cloud environments. In this study, we will develop an innovative prototype for location-aware access control and data privacy for CC systems. We will apply location-aware access control policies to role-based access control of Cloud Foundry, and then analyze the impact on user privacy after implementing these policies. This innovation can be used to address the …

Cybersecurity Behavioural Model For Students In The Tertiary Institutions, Boluwatife Fatokun Faith

Student Works (2020-2029)

Humans are majorly identified as the weakest link in cybersecurity. Tertiary institution student’s face lot of cybersecurity issues due to their increased Internet exposure, however cybersecurity behavioural studies focusing on tertiary students is limited. This study focused on investigating tertiary institutions students’ cybersecurity behaviour, via validated cybersecurity factors, Perceived Vulnerability (PV); Perceived Barriers (PBr); Perceived Severity (PS); Security Self-Efficacy (SSE); Response Efficacy (RE); Cues to Action (CA); Peer Behaviour (PBhv); Computer Skills (CS); Internet Skills (IS); Prior Experience with Computer Security Practices (PE); Perceived Benefits (PBnf); and a newly added factor, Familiarity with Cyber-Threats (FCT), to explore the factors relationship …

Feature Extraction And Description For Retinal Fundus Image Registration, Ramli Roziana

Student Works (2020-2029)

Retinal fundus image registration (RIR) is performed to align two or more fundus images. A general framework of a feature-based RIR technique comprises of preprocessing, feature extraction, feature descriptor, matching and estimating geometrical transformation. The RIR is mainly performed for super-resolution, image mosaicking and longitudinal study applications to assist diagnosis and monitoring retinal diseases. Registering image pair from these applications involve a combination of challenges such as overlapping area and rotation between images. The challenges of the overlapping area and rotation can be addressed at feature extraction and feature descriptor stages of the feature-based RIR technique, respectively. To address the …

An Intra-Severity Classification And Adaptation Technique To Improve Dysarthric Speech Recognition Accuracy, Al-Qatab Bassam Ali Qasem

Student Works (2020-2029)

Dysarthria is a motor speech impairment at the neurological and/or muscular levels that caused difficulty in pronouncing words clearly. Automatic speech recognition (ASR) system is increasingly applied as assistive technology to aid an individual with physical disability particularly the speech impaired community such as dysarthria speakers. However, the development of an effective ASR system is hindered by the data sparsity, either in the coverage of the language or the size of the existing speech databases. The speaker adaptation (SA) technique is one of the solutions to overcome the data sparsity issue of ASR for dysarthric speakers. Our proposed method introduces …

The Evolving Fuzzy Clustering Approach For Discriminating Neutron And Gamma-Ray Pulses, Shirkhorshidi Ali Seyed

Student Works (2020-2029)

Having a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as …

Intelligent Building Systems: Security And Facility Professionals’ Understanding Of System Threats,Vulnerabilities And Mitigation Practice, David J. Brooks, Michael Coole, Paul Haskell-Dowland

Research outputs 2014 to 2021

Intelligent Buildings or Building Automation and Control Systems (BACS) are becoming common in buildings, driven by the commercial need for functionality, sharing of information, reduced costs and sustainable buildings. The facility manager often has BACS responsibility; however, their focus is generally not on BACS security. Nevertheless, if a BACS-manifested threat is realised, the impact to a building can be significant, through denial, loss or manipulation of the building and its services, resulting in loss of information or occupancy. Therefore, this study garnered a descriptive understanding of security and facility professionals’ knowledge of BACS, including vulnerabilities and mitigation practices. Results indicate …

Divergence Of Safety And Security, David J. Brooks, Michael Coole

Research outputs 2014 to 2021

© 2020, The Author(s). Safety and security have similar goals, to provide social wellness through risk control. Such similarity has led to views of professional convergence; however, the professions of safety and security are distinct. Distinction arises from variances in concept definition, risk drivers, body of knowledge, and professional practice. This chapter explored the professional synergies and tensions between safety and security professionals, using task-related bodies of knowledge. Findings suggest that safety and security only have commonalities at the overarching abstract level. Common knowledge does exist with categories of risk management and control; however, differences are explicit. In safety, risk …

Interpreting Health Events In Big Data Using Qualitative Traditions, Roschelle L. Fritz, Gordana Dermody

Research outputs 2014 to 2021

© The Author(s) 2020. The training of artificial intelligence requires integrating real-world context and mathematical computations. To achieve efficacious smart health artificial intelligence, contextual clinical knowledge serving as ground truth is required. Qualitative methods are well-suited to lend consistent and valid ground truth. In this methods article, we illustrate the use of qualitative descriptive methods for providing ground truth when training an intelligent agent to detect Restless Leg Syndrome. We show how one interdisciplinary, inter-methodological research team used both sensor-based data and the participant’s description of their experience with an episode of Restless Leg Syndrome for training the intelligent agent. …

Cooperative Co-Evolution For Feature Selection In Big Data With Random Feature Grouping, A.N.M. Bazlur Rashid, Mohiuddin Ahmed, Leslie F. Sikos, Paul Haskell-Dowland

Research outputs 2014 to 2021

© 2020, The Author(s). A massive amount of data is generated with the evolution of modern technologies. This high-throughput data generation results in Big Data, which consist of many features (attributes). However, irrelevant features may degrade the classification performance of machine learning (ML) algorithms. Feature selection (FS) is a technique used to select a subset of relevant features that represent the dataset. Evolutionary algorithms (EAs) are widely used search strategies in this domain. A variant of EAs, called cooperative co-evolution (CC), which uses a divide-and-conquer approach, is a good choice for optimization problems. The existing solutions have poor performance because …

Distributed Strategy For Power Re-Allocation In High Performance Applications, Vaibhav Sundriyal, Masha Sosonkina

Electrical & Computer Engineering Faculty Publications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to distribute a given power allocation among the cluster nodes assigned to the application while balancing their performance change. The strategy operates in a timeslice-based manner to estimate the current application performance and power usage per node followed by power redistribution across the nodes. Experiments, performed on four nodes (112 cores) of a modern computing platform interconnected with Infiniband showed that even a significant power budget reduction of 20% may result in …