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Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, Erick Serrano 2021 University of Nevada, Las Vegas

Toward A Quantum Neural Network: Proposing The Qaoa Algorithm To Replace A Feed Forward Neural Network, Erick Serrano

Undergraduate Research Symposium Posters

With a surge in popularity of machine learning as a whole, many researchers have sought optimization methods to reduce the complexity of neural networks; however, only recent attempts have been made to optimize neural networks via quantum computing methods. In this paper, we describe the training process of a feed forward neural network (FFNN) and the time complexity of the training process. We highlight the inefficiencies of the FFNN training process, particularly when implemented with gradient descent, and introduce a call to action for optimization of a FFNN. Afterward, we discuss the strides made in quantum computing to improve the …


Learning Network-Based Multi-Modal Mobile User Interface Embeddings, Gary ANG, Ee-Peng LIM 2021 Singapore Management University

Learning Network-Based Multi-Modal Mobile User Interface Embeddings, Gary Ang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Rich multi-modal information - text, code, images, categorical and numerical data - co-exist in the user interface (UI) design of mobile applications. UI designs are composed of UI entities supporting different functions which together enable the application. To support effective search and recommendation applications over mobile UIs, we need to be able to learn UI representations that integrate latent semantics. In this paper, we propose a novel unsupervised model - Multi-modal Attention-based Attributed Network Embedding (MAAN) model. MAAN is designed to capture both multi-modal and structural network information. Based on the encoder-decoder framework, MAAN aims to learn UI representations that …


Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples, Alvin CHAN, Lei MA, Felix JUEFEI-XU, Yew-Soon ONG, Xiaofei XIE, Minhui XUE, Yang LIU 2021 Singapore Management University

Breaking Neural Reasoning Architectures With Metamorphic Relation-Based Adversarial Examples, Alvin Chan, Lei Ma, Felix Juefei-Xu, Yew-Soon Ong, Xiaofei Xie, Minhui Xue, Yang Liu

Research Collection School Of Computing and Information Systems

The ability to read, reason, and infer lies at the heart of neural reasoning architectures. After all, the ability to perform logical reasoning over language remains a coveted goal of Artificial Intelligence. To this end, models such as the Turing-complete differentiable neural computer (DNC) boast of real logical reasoning capabilities, along with the ability to reason beyond simple surface-level matching. In this brief, we propose the first probe into DNC's logical reasoning capabilities with a focus on text-based question answering (QA). More concretely, we propose a conceptually simple but effective adversarial attack based on metamorphic relations. Our proposed adversarial attack …


Practical Server-Side Wifi-Based Indoor Localization: Addressing Cardinality & Outlier Challenges For Improved Occupancy Estimation, Anuradha RAVI, Archan MISRA 2021 Singapore Management University

Practical Server-Side Wifi-Based Indoor Localization: Addressing Cardinality & Outlier Challenges For Improved Occupancy Estimation, Anuradha Ravi, Archan Misra

Research Collection School Of Computing and Information Systems

Server-side WiFi-based indoor localization offers a compelling approach for passive occupancy estimation (i.e., without requiring active participation by client devices, such as smartphones carried by visitors), but is known to suffer from median error of 6–8 meters. By analyzing the characteristics of an operationally-deployed, WiFi-based passive indoor location system, based on the classical RADAR algorithm, we identify and tackle 2 practical challenges for accurate individual device localization. The first challenge is the low-cardinality issue, whereby only the associated AP generates sufficiently frequent RSSI reports, causing a client to experience large localization error due to the absence of sufficient measurements from …


On The Robustness Of Diffusion In A Network Under Node Attacks, Alvis LOGINS, Yuchen LI, Panagiotis KARRAS 2021 Singapore Management University

On The Robustness Of Diffusion In A Network Under Node Attacks, Alvis Logins, Yuchen Li, Panagiotis Karras

Research Collection School Of Computing and Information Systems

How can we assess a network's ability to maintain its functionality under attacks Network robustness has been studied extensively in the case of deterministic networks. However, applications such as online information diffusion and the behavior of networked public raise a question of robustness in probabilistic networks. We propose three novel robustness measures for networks hosting a diffusion under the Independent Cascade or Linear Threshold model, susceptible to attacks by an adversarial attacker who disables nodes. The outcome of such a process depends on the selection of its initiators, or seeds, by the seeder, as well as on two factors outside …


Optimizing Networking Topologies With Shortest Path Algorithms, Jordan Sahs 2021 University of Nebraska at Omaha

Optimizing Networking Topologies With Shortest Path Algorithms, Jordan Sahs

UNO Student Research and Creative Activity Fair

Communication networks tend to contain redundant devices and mediums of transmission, thus the need to locate, document, and optimize networks is increasingly becoming necessary. However, many people do not know where to start the optimization progress. What is network topology? What is this “Shortest Path Problem”, and how can it be used to better my network? These questions are presented, taught, and answered within this paper. To supplement the reader’s understanding there are thirty-eight figures in the paper that are used to help convey and compartmentalize the learning process needed to grasp the materials presented in the ending sections.

In …


Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews 2021 Dakota State University

Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews

Masters Theses & Doctoral Dissertations

This single-case mechanism study examined the effects of cryptojacking on Internet of Things (IoT) device performance metrics. Cryptojacking is a cyber-threat that involves stealing the computational resources of devices belonging to others to generate cryptocurrencies. The resources primarily include the processing cycles of devices and the additional electricity needed to power this additional load. The literature surveyed showed that cryptojacking has been gaining in popularity and is now one of the top cyberthreats. Cryptocurrencies offer anyone more freedom and anonymity than dealing with traditional financial institutions which make them especially attractive to cybercriminals. Other reasons for the increasing popularity of …


Analyzing The Effectiveness Of Legal Regulations And Social Consequences For Securing Data, Howard B. Goodman 2021 Dakota State Universi

Analyzing The Effectiveness Of Legal Regulations And Social Consequences For Securing Data, Howard B. Goodman

Masters Theses & Doctoral Dissertations

There is a wide range of concerns and challenges related to stored data security – which range from privacy and management to operations readiness, These challenges span from financial to personal and public impact. With an abundance of regulations for the enforcement of data security and emerging requirements proposed every year, organizations cannot avoid the legal or social implications of inadequate data protection. Today, public spotlight and awareness are challenging organizations to enhance how data is protected more than at any other time. For this reason, organizations have made significant efforts to improve security.

When looking at precautions or changes, …


On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead 2021 Chapman University

On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead

Engineering Faculty Articles and Research

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can …


Traversing Nat: A Problem, Tyler Flaagan 2021 Dakota State University

Traversing Nat: A Problem, Tyler Flaagan

Masters Theses & Doctoral Dissertations

This quasi-experimental before-and-after study measured and analyzed the impacts of adding security to a new bi-directional Network Address Translation (NAT). Literature revolves around various types of NAT, their advantages and disadvantages, their security models, and networking technologies’ adoption. The study of the newly created secure bi-directional model of NAT showed statistically significant changes in the variables than another model using port forwarding. Future research of how data will traverse networks is crucial in an ever-changing world of technology.


Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur 2021 Dakota State University

Towards Identity Relationship Management For Internet Of Things, Mohammad Muntasir Nur

Masters Theses & Doctoral Dissertations

Identity and Access Management (IAM) is in the core of any information systems. Traditional IAM systems manage users, applications, and devices within organizational boundaries, and utilize static intelligence for authentication and access control. Identity federation has helped a lot to deal with boundary limitation, but still limited to static intelligence – users, applications and devices must be under known boundaries. However, today’s IAM requirements are much more complex. Boundaries between enterprise and consumer space, on premises and cloud, personal devices and organization owned devices, and home, work and public places are fading away. These challenges get more complicated for Internet …


Deep Learning For Anomaly Detection: Challenges, Methods, And Opportunities, Guansong PANG, Longbing CAO, Charu AGGARWAL 2021 Singapore Management University

Deep Learning For Anomaly Detection: Challenges, Methods, And Opportunities, Guansong Pang, Longbing Cao, Charu Aggarwal

Research Collection School Of Computing and Information Systems

In this tutorial we aim to present a comprehensive survey of the advances in deep learning techniques specifically designed for anomaly detection (deep anomaly detection for short). Deep learning has gained tremendous success in transforming many data mining and machine learning tasks, but popular deep learning techniques are inapplicable to anomaly detection due to some unique characteristics of anomalies, e.g., rarity, heterogeneity, boundless nature, and prohibitively high cost of collecting large-scale anomaly data. Through this tutorial, audiences would gain a systematic overview of this area, learn the key intuitions, objective functions, underlying assumptions, advantages and disadvantages of different categories of …


Deepis: Susceptibility Estimation On Social Networks, Wenwen XIA, Yuchen LI, Jun WU, Shenghong LI 2021 Singapore Management University

Deepis: Susceptibility Estimation On Social Networks, Wenwen Xia, Yuchen Li, Jun Wu, Shenghong Li

Research Collection School Of Computing and Information Systems

Influence diffusion estimation is a crucial problem in social network analysis. Most prior works mainly focus on predicting the total influence spread, i.e., the expected number of influenced nodes given an initial set of active nodes (aka. seeds). However, accurate estimation of susceptibility, i.e., the probability of being influenced for each individual, is more appealing and valuable in real-world applications. Previous methods generally adopt Monte Carlo simulation or heuristic rules to estimate the influence, resulting in high computational cost or unsatisfactory estimation error when these methods are used to estimate susceptibility. In this work, we propose to leverage graph neural …


Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel AlOrbani 2021 The University of Western Ontario

Load Balancing And Resource Allocation In Smart Cities Using Reinforcement Learning, Aseel Alorbani

Electronic Thesis and Dissertation Repository

Today, smart city technology is being adopted by many municipal governments to improve their services and to adapt to growing and changing urban population. Implementing a smart city application can be one of the most challenging projects due to the complexity, requirements and constraints. Sensing devices and computing components can be numerous and heterogeneous. Increasingly, researchers working in the smart city arena are looking to leverage edge and cloud computing to support smart city development. This approach also brings a number of challenges. Two of the main challenges are resource allocation and load balancing of tasks associated with processing data …


Neural Architecture Search As Sparse Supernet, Y. WU, A. LIU, Zhiwu HUANG, S. ZHANG, Gool L. VAN 2021 Singapore Management University

Neural Architecture Search As Sparse Supernet, Y. Wu, A. Liu, Zhiwu Huang, S. Zhang, Gool L. Van

Research Collection School Of Computing and Information Systems

This paper aims at enlarging the problem of Neural Architecture Search (NAS) from Single-Path and Multi-Path Search to automated Mixed-Path Search. In particular, we model the NAS problem as a sparse supernet using a new continuous architecture representation with a mixture of sparsity constraints. The sparse supernet enables us to automatically achieve sparsely-mixed paths upon a compact set of nodes. To optimize the proposed sparse supernet, we exploit a hierarchical accelerated proximal gradient algorithm within a bi-level optimization framework. Extensive experiments on Convolutional Neural Network and Recurrent Neural Network search demonstrate that the proposed method is capable of searching for …


Understanding Adversarial Robustness Via Critical Attacking Route, Tianlin LI, Aishan LIU, Xianglong LIU, Yitao XU, Chongzhi ZHANG, Xiaofei XIE 2021 Singapore Management University

Understanding Adversarial Robustness Via Critical Attacking Route, Tianlin Li, Aishan Liu, Xianglong Liu, Yitao Xu, Chongzhi Zhang, Xiaofei Xie

Research Collection School Of Computing and Information Systems

Deep neural networks (DNNs) are vulnerable to adversarial examples which are generated by inputs with imperceptible perturbations. Understanding adversarial robustness of DNNs has become an important issue, which would for certain result in better practical deep learning applications. To address this issue, we try to explain adversarial robustness for deep models from a new perspective of critical attacking route, which is computed by a gradient-based influence propagation strategy. Similar to rumor spreading in social net-works, we believe that adversarial noises are amplified and propagated through the critical attacking route. By exploiting neurons' influences layer by layer, we compose the critical …


Decision-Guided Weighted Automata Extraction From Recurrent Neural Networks, Xiyue ZHANG, Xiaoning DU, Xiaofei XIE, Lei MA, Yang LIU, Meng SUN 2021 Singapore Management University

Decision-Guided Weighted Automata Extraction From Recurrent Neural Networks, Xiyue Zhang, Xiaoning Du, Xiaofei Xie, Lei Ma, Yang Liu, Meng Sun

Research Collection School Of Computing and Information Systems

Recurrent Neural Networks (RNNs) have demonstrated their effectiveness in learning and processing sequential data (e.g., speech and natural language). However, due to the black-box nature of neural networks, understanding the decision logic of RNNs is quite challenging. Some recent progress has been made to approximate the behavior of an RNN by weighted automata. They provide better interpretability, but still suffer from poor scalability. In this paper, we propose a novel approach to extracting weighted automata with the guidance of a target RNN’s decision and context information. In particular, we identify the patterns of RNN’s step-wise predictive decisions to instruct the …


Learning To Pre-Train Graph Neural Networks, Yuanfu LU, Xunqiang JIANG, Yuan FANG, Chuan SHI 2021 Singapore Management University

Learning To Pre-Train Graph Neural Networks, Yuanfu Lu, Xunqiang Jiang, Yuan Fang, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) have become the de facto standard for representation learning on graphs, which derive effective node representations by recursively aggregating information from graph neighborhoods. While GNNs can be trained from scratch, pre-training GNNs to learn transferable knowledge for downstream tasks has recently been demonstrated to improve the state of the art. However, conventional GNN pre-training methods follow a two-step paradigm: 1) pre-training on abundant unlabeled data and 2) fine-tuning on downstream labeled data, between which there exists a significant gap due to the divergence of optimization objectives in the two steps. In this paper, we conduct an …


Treecaps: Tree-Based Capsule Networks For Source Code Processing, Duy Quoc Nghi BUI, Yijun YU, Lingxiao JIANG 2021 Singapore Management University

Treecaps: Tree-Based Capsule Networks For Source Code Processing, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., Abstract Syntax Trees) and/or semantic information (e.g., Dependency Graphs). While graphs may be better at capturing various viewpoints of code semantics than trees, constructing graph inputs from code need static code semantic analysis that may not be accurate and introduces noise during learning. On the other hand, syntax trees are precisely defined according to the language grammar and easier to construct and process than graphs. We propose a new tree-based learning technique, named TreeCaps, by fusing capsule networks with tree-based convolutional neural networks, to …


Building Effective Network Security Frameworks Using Deep Transfer Learning Techniques, Harsh Dhillon 2021 The University of Western Ontario

Building Effective Network Security Frameworks Using Deep Transfer Learning Techniques, Harsh Dhillon

Electronic Thesis and Dissertation Repository

Network traffic is growing at an outpaced speed globally. According to the 2020 Cisco Annual Report, nearly two-thirds of the global population will have internet connectivity by the year 2023. The number of devices connected to IP networks will also triple the total world population's size by the same year. The vastness of forecasted network infrastructure opens opportunities for new technologies and businesses to take shape, but it also increases the surface of security vulnerabilities. The number of cyberattacks are growing worldwide and are becoming more diverse and sophisticated. Classic network intrusion detection architectures monitor a system to detect malicious …


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