Computer Sciences Commons^™

Sparsity And Weak Supervision In Quantum Machine Learning, Seyran Saeedi

Theses and Dissertations

Quantum computing is an interdisciplinary field at the intersection of computer science, mathematics, and physics that studies information processing tasks on a quantum computer. A quantum computer is a device whose operations are governed by the laws of quantum mechanics. As building quantum computers is nearing the era of commercialization and quantum supremacy, it is essential to think of potential applications that we might benefit from. Among many applications of quantum computation, one of the emerging fields is quantum machine learning. We focus on predictive models for binary classification and variants of Support Vector Machines that we expect to be …

Automatically Categorizing Software Technologies, Mathieu Nassif, Christoph Treude, Martin P. Robillard

Research Collection School Of Computing and Information Systems

Informal language and the absence of a standard taxonomy for software technologies make it difficult to reliably analyze technology trends on discussion forums and other on-line venues. We propose an automated approach called Witt for the categorization of software technologies (an expanded version of the hypernym discovery problem). Witt takes as input a phrase describing a software technology or concept and returns a general category that describes it (e.g., integrated development environment), along with attributes that further qualify it (commercial, php, etc.). By extension, the approach enables the dynamic creation of lists of all technologies of a given type (e.g., …

Vietnamese Punctuation Prediction Using Deep Neural Networks, Thuy Pham, Nhu Nguyen, Hong Quang Pham, Han Cao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Adding appropriate punctuation marks into text is an essential step in speech-to-text where such information is usually not available. While this has been extensively studied for English, there is no large-scale dataset and comprehensive study in the punctuation prediction problem for the Vietnamese language. In this paper, we collect two massive datasets and conduct a benchmark with both traditional methods and deep neural networks. We aim to publish both our data and all implementation codes to facilitate further research, not only in Vietnamese punctuation prediction but also in other related fields. Our project, including datasets and implementation details, is publicly …

Remote Communication In Wilderness Search And Rescue: Implications For The Design Of Emergency Distributed-Collaboration Tools For Network-Sparse Environments, Brennan Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) requires careful communication between workers in different locations. To understand the contexts from which WSAR workers communicate and the challenges they face, we interviewed WSAR workers and observed a mock-WSAR scenario. Our findings illustrate that WSAR workers face challenges in maintaining a shared mental model. This is primarily done through distributed communication using two-way radios and cell phones for text and photo messaging; yet both implicit and explicit communication suffer. WSAR workers send messages for various reasons and share different types of information with varying levels of urgency. This warrants the use of multiple communication …

Systematic Classification Of Attackers Via Bounded Model Checking, Eric Rothstein-Morris, Jun Sun, Sudipta Chattopadyay

Research Collection School Of Computing and Information Systems

In this work, we study the problem of verification of systems in the presence of attackers using bounded model checking. Given a system and a set of security requirements, we present a methodology to generate and classify attackers, mapping them to the set of requirements that they can break. A naive approach suffers from the same shortcomings of any large model checking problem, i.e., memory shortage and exponential time. To cope with these shortcomings, we describe two sound heuristics based on cone-of-influence reduction and on learning, which we demonstrate empirically by applying our methodology to a set of hardware benchmark …

Spatial Multi-Objective Land Use Optimization Toward Livability Based On Boundary-Based Genetic Algorithm: A Case Study In Singapore, Kai Cao, Muyang Liu, Shu Wang, Mengqi Liu, Wenting Zhang, Qiang Meng, Bo Huang

Research Collection School Of Computing and Information Systems

In this research, the concept of livability has been quantitatively and comprehensively reviewed and interpreted to contribute to spatial multi-objective land use optimization modelling. In addition, a multi-objective land use optimization model was constructed using goal programming and a weighted-sum approach, followed by a boundary-based genetic algorithm adapted to help address the spatial multi-objective land use optimization problem. Furthermore, the model is successfully and effectively applied to the case study in the Central Region of Queenstown Planning Area of Singapore towards livability. In the case study, the experiments based on equal weights and experiments based on different weights combination have …

Deepdrawing: A Deep Learning Approach To Graph Drawing, Yong Wang, Zhihua Jin, Qianwen Wang, Weiwei Cui, Tengfei Ma, Huamin Qu

Research Collection School Of Computing and Information Systems

Node-link diagrams are widely used to facilitate network explorations. However, when using a graph drawing technique to visualize networks, users often need to tune different algorithm-specific parameters iteratively by comparing the corresponding drawing results in order to achieve a desired visual effect. This trial and error process is often tedious and time-consuming, especially for non-expert users. Inspired by the powerful data modelling and prediction capabilities of deep learning techniques, we explore the possibility of applying deep learning techniques to graph drawing. Specifically, we propose using a graph-LSTM-based approach to directly map network structures to graph drawings. Given a set of …

Emoco: Visual Analysis Of Emotion Coherence In Presentation Videos, Haipeng Zeng, Xingbo Wang, Aoyu Wu, Yong Wang, Quan Li, Alex Endert, Huamin Qu

Research Collection School Of Computing and Information Systems

Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo, an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features …

A Review Of Emotion Sensing: Categorization Models And Algorithms, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis consists in the identification of the sentiment polarity associated with a target object, such as a book, a movie or a phone. Sentiments reflect feelings and attitudes, while emotions provide a finer characterization of the sentiments involved. With the huge number of comments generated daily on the Internet, besides sentiment analysis, emotion identification has drawn keen interest from different researchers, businessmen and politicians for polling public opinions and attitudes. This paper reviews and discusses existing emotion categorization models for emotion analysis and proposes methods that enhance existing emotion research. We carried out emotion analysis by inviting experts from …

Deterministic Identity-Based Encryption From Lattice-Based Programmable Hash Functions With High Min-Entropy, Daode Zhang, Jie Li, Bao Li, Xianhui Lu, Haiyang Xue, Dingding Jia, Yamin Liu

Research Collection School Of Computing and Information Systems

There only exists one deterministic identity-based encryption (DIBE) scheme which is adaptively secure in the auxiliary-input setting, under the learning with errors (LWE) assumption. However, the master public key consists of basic matrices. In this paper, we consider to construct adaptively secure DIBE schemes with more compact public parameters from the LWE problem. (i) On the one hand, we gave a generic DIBE construction from lattice-based programmable hash functions with high min-entropy. (ii) On the other hand, when instantiating our generic DIBE construction with four LPHFs with high min-entropy, we can get four adaptively secure DIBE schemes with more compact …

Food Fraud In Nigeria: Challenges, Risks And Solutions, Joy Ewomazino Opia

Theses

Food fraud is one of the most urgent and active food research and regulatory areas. It is an evolving problem in Nigeria that has led to the deaths of many people especially the vunerable groups that includes mostly children, the elderly and immunocomprised persons. Therefore the aim of this study is to investigate the current challenges of food fraud in Nigeria, identify the risks it poses on the health and wellbeing of Nigerians and propose measures to tackle food fraud at local and international levels by regulatory and government agencies. This study explored the relationship between food fraud, food security …

Usability Of Portable Eeg For Monitoring Students’ Attention In Online Learning, Arisaphat Suttidee

CCAC Theses and Dissertations

Current research demonstrates that distractions while participating in online courses affect students’ performance in online tasks. Electroencephalography (EEG) devices are currently being used in education to help students maintain attention when engaged in online classes. Previous studies have focused predominantly on comparing EEG devices, EEG signal quality, and EEG effectiveness. However, there is no comprehensive study examining the usability of the portable EEG headset to monitor students' attention in online courses.

This study aimed to examine the usability of EEG devices while monitoring student attention levels during online educational tasks. Specifically, twenty (20) participants who intend to enroll in online …

Implementing Algorithmic Crisis Alerts In Mhealth Systems For Veterans With Ptsd, Md Sazzad Hossain, Priyanka Annapureddy, Sheikh Iqbal Ahamed, Praveen Madiraju, Mark Flower, Lisa Rein, Thomas Kissane, Wylie Frydrychowicz, Naveen K. Bansal, Niharika Jain, Katinka Hooyer, Zeno Franco

Computer Science Faculty Research and Publications

This paper seeks to establish a machine learning driven method by which a military veteran with Post-Traumatic Stress Disorder (PTSD) is classified as being in a crisis situation or not, based upon a given set of criteria. Optimizing alerting decision rules is critical to ensure that veterans at highest risk for mental health crisis rapidly receive additional attention. Subject matter experts in our team (a psychologist, a medical anthropologist, and an expert veteran), defined acute crisis, early warning signs and long-term crisis from this dataset. First, we used a decision tree to find an early time point when the peer …

The Trust Principles For Digital Repositories, Dawei Lin, Jonathan Crabtree, Ingrid Dillo, Robert R. Downs, Rorie Edmunds, David Giaretta, Marisa De Giusti, Hervé L'Hours, Wim Hugo, Reyna Jenkyns, Varsha Khodiyar, Maryann E. Martone, Mustapha Mokrane, Vivek Navale, Jonathan Petters, Barbara Sierman, Dina V. Sokolova, Martina Stockhause, John Westbrook

Copyright, Fair Use, Scholarly Communication, etc.

As information and communication technology has become pervasive in our society, we are increasingly dependent on both digital data and repositories that provide access to and enable the use of such resources. Repositories must earn the trust of the communities they intend to serve and demonstrate that they are reliable and capable of appropriately managing the data they hold.

Following a year-long public discussion and building on existing community consensus , several stakeholders, representing various segments of the digital repository community, have collaboratively developed and endorsed a set of guiding principles to demonstrate digital repository trustworthiness. Transparency, Responsibility, User focus, …

Optimal Feature Selection For Learning-Based Algorithms For Sentiment Classification, Zhaoxia Wang, Zhiping Lin

Research Collection School Of Computing and Information Systems

Sentiment classification is an important branch of cognitive computation—thus the further studies of properties of sentiment analysis is important. Sentiment classification on text data has been an active topic for the last two decades and learning-based methods are very popular and widely used in various applications. For learning-based methods, a lot of enhanced technical strategies have been used to improve the performance of the methods. Feature selection is one of these strategies and it has been studied by many researchers. However, an existing unsolved difficult problem is the choice of a suitable number of features for obtaining the best sentiment …

Synthesizing Aspect-Driven Recommendation Explanations From Reviews, Trung-Hoang Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Explanations help to make sense of recommendations, increasing the likelihood of adoption. However, existing approaches to explainable recommendations tend to rely on rigid, standardized templates, customized only via fill-in-the-blank aspect sentiments. For more flexible, literate, and varied explanations covering various aspects of interest, we synthesize an explanation by selecting snippets from reviews, while optimizing for representativeness and coherence. To fit target users' aspect preferences, we contextualize the opinions based on a compatible explainable recommendation model. Experiments on datasets of several product categories showcase the efficacies of our method as compared to baselines based on templates, review summarization, selection, and text …

Aateam: Achieving The Ad Hoc Teamwork By Employing The Attention Mechanism, Shuo Chen, Ewa Andrejczuk, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior coordination. The most advanced approach learns policies based on previous experiences and reuses one of the policies to interact with new teammates. However, the selected policy in many cases is sub-optimal. Switching between policies to adapt to new teammates' behaviour takes time, which threatens the successful performance of a task. In this paper, we propose AATEAM – a method that uses the attention-based neural networks to cope with new teammates' behaviour in real-time. We train one attention network per teammate type. The attention …

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 …

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 …

Performing The Digital Self: Understanding Location-Based Social Networking, Territory, Space, And Identity In The City, Konstantinos Papangelis, Alan Chamberlain, Ioanna Lykourentzou, Vassilis-Javed Khan, Michael Saker, Hai-Ning Liang, Irwyn Sadien, Ting Cao

Articles

Expressions of territoriality have been positioned as one of the main reasons users alter their behaviors and perceptions of spatiality and sociality while engaging with location-based social networks (LBSN). Despite the potential for this interplay to further our understanding of LBSN usage in the context of identity, very little work has actually been done towards this. Addressing this gap in the literature is one the chief aims of the article. Drawing on an original six-week study with 42 participants utilizing a bespoke LBSN entitled ‘GeoMoments’, our research explores: (1) the way that territoriality is linked to self-identity; and (2) how …

A Review On Embedded Field Programmable Gate Array Architectures And Configuration Tools, Khouloud Bouaziz, Abdulfattah M. Obeid, Sonda Chtourou, Mohamed Abid

Turkish Journal of Electrical Engineering and Computer Sciences

Nowadays, systems-on-chip have reached a level where nonrecurring engineering costs have become a great challenge due to the increase of design complexity and postfabrication errors. Embedded field programmable gate arrays (eFPGAs) represent a viable alternative to overcome these issues since they provide postmanufacturing flexibility that can reduce the number of chip redesigns and amortize chip fabrication cost. In this paper, we present an overview on eFPGAs and their architectures, computer aided design (CAD) tools, and design challenges. An eFPGA must be well-designed and accompanied by an optimized CAD tool suite to respond to target application's requirements in terms of power …

Simulation And Analysis Of Wind Turbine Radar Echo Based On 3-D Scattering Point Model, Jiangong Zhang, Bin Hao, Bo Tang, Li Huang, Jiawei Yang

Turkish Journal of Electrical Engineering and Computer Sciences

Wind turbine (WT) arrays in wind farms can cause serious interference on nearby radar stations. This interference could be filtered out if wind turbine radar echo (WTRE) can be obtained accurately. Considering the singleness of in-field experiments, numerical simulation became the majority among such works, but few of them reached necessary accuracy. Therefore, we propose a solution method of WTRE based on three-dimensional (3-D) scattering point model. Firstly, we use the nonuniform rational B-spline to build the 3-D model of WT. Secondly, based on the method of moments (MoM), the Rao-Wilton-Gisson (RWG) basis function is adopted to discretize the integral …

Design Of A High Performance Narrowband Low Noise Amplifier Using An On-Chip Orthogonal Series Stacked Differential Fractal Inductor For 5g Applications, Sunil Kumar Tumma, Bheemarao Nistala

Turkish Journal of Electrical Engineering and Computer Sciences

Inductors play a crucial role in the design of radio frequency integrated circuits (RFICs) and they typically consume a considerably large area and have a low-quality factor at high frequencies. The employment of fractal structure in on-chip inductors helps in improving the quality factor and also reduces the overall area besides improving the inductance value. In this paper, an orthogonal series stacked differential fractal inductor is proposed and the same is used to design a low noise amplifier (LNA) for 5G band (27--30 GHz) applications. The proposed inductor is fabricated on a multilayer printed circuit board and the measurement results …

A Hybrid Model Based On The Convolutional Neural Network Model And Artificial Bee Colony Or Particle Swarm Optimization-Based Iterative Thresholding For The Detection Of Bruised Apples, Mahmut Heki̇m, Onur Cömert, Kemal Adem

Turkish Journal of Electrical Engineering and Computer Sciences

In this study, apple images taken with near-infrared (NIR) cameras were classified as bruised and healthy objects using iterative thresholding approaches based on artificial bee colony (ABC) and particle swarm optimization (PSO) algorithms supported by a convolutional neural network (CNN) deep learning model. The proposed model includes the following stages: image acquisition, image preprocessing, the segmentation of anatomical regions (stem-calyx regions) to be discarded, the detection of bruised areas on the apple images, and their classification. For this aim, by using the image acquisition platform with a NIR camera, a total of 1200 images at 6 different angles were taken …

A Novel Semisupervised Classification Method Via Membership And Polyhedral Conic Functions, Nur Uylaş Sati

Turkish Journal of Electrical Engineering and Computer Sciences

In real-world problems, finding sufficient labeled data for defining classification rules is very difficult. This paper suggests a new semisupervised multiclass classification method. In the initialization, new membership functions are defined by utilizing the labeled data?Äôs medoids and means. Then the unlabeled points are labeled with the class of the highest membership value. In the supervised learning phase, separation via the polyhedral conic functions (PCFs) approach is improved by using defined membership values in the linear programming problem. The suggested algorithm is tested on real-world datasets and compared with the state-of-the-art semisupervised methods. The results obtained indicate that the suggested …

Integrated Topic Modeling And Sentiment Analysis: A Review Rating Prediction Approach For Recommender Systems, Anbazhagan Mahadevan, Michael Arock

Turkish Journal of Electrical Engineering and Computer Sciences

Recommender systems (RSs) are running behind E-commerce websites to recommend items that are likely to be bought by users. Most of the existing RSs are relying on mere star ratings while making recommendations. However, ratings alone cannot help RSs make accurate recommendations, as they cannot properly capture sentiments expressed towards various aspects of the items. The other rich and expressive source of information available that can help make accurate recommendations is user reviews. Because of their voluminous nature, reviews lead to the information overloading problem. Hence, drawing out the user opinion from reviews is a decisive job. Therefore, this paper …

Retinal Vessel Segmentation Using Modified Symmetrical Local Threshold, Umar Özgünalp

Turkish Journal of Electrical Engineering and Computer Sciences

Retinal vessel segmentation is important for the identification of many diseases including glaucoma, hypertensive retinopathy, diabetes, and hypertension. Moreover, retinal vessel diameter is associated with cardiovascular mortality. Accurate detection of blood vessels improves the detection of exudates in color fundus images, as well as detection of the retinal nerve, optic disc, or fovea. A retinal vessel is a darker stripe on a lighter background. Thus, the objective is very similar to the lane detection task for intelligent vehicles. A lane on a road is a light stripe on a darker background (i.e. asphalt). For lane detection, the symmetrical local threshold …

Detailed Modeling Of A Thermoelectric Generator For Maximum Power Point Tracking, Hayati̇ Mamur, Yusuf Çoban

Turkish Journal of Electrical Engineering and Computer Sciences

Thermoelectric generators (TEGs) are used in small power applications to generate electrical energy from waste heats. Maximum power is obtained when the connected load to the ends of TEGs matches their internal resistance. However, impedance matching cannot always be ensured. Therefore, TEGs operate at lower efficiency. For this reason, maximum power point tracking (MPPT) algorithms are utilized. In this study, both TEGs and a boost converter with MPPT were modeled together. Detailed modeling, simulation, and verification of TEGs depending on the Seebeck coefficient, the hot/cold side temperatures, and the number of modules in MATLAB/Simulink were carried out. In addition, a …

Consumer Loans' First Payment Default Detection: A Predictive Model, Utku Koç, Türkan Sevgi̇li̇

Turkish Journal of Electrical Engineering and Computer Sciences

A default loan (also called nonperforming loan) occurs when there is a failure to meet bank conditions and repayment cannot be made in accordance with the terms of the loan which has reached its maturity. In this study, we provide a predictive analysis of the consumer behavior concerning a loan?Äôs first payment default (FPD) using a real dataset of consumer loans with approximately 600,000 records from a bank. We use logistic regression, naive Bayes, support vector machine, and random forest on oversampled and undersampled data to build eight different models to predict FPD loans. A two-class random forest using undersampling …