Systems Architecture Commons^™

A Framework For Identifying Host-Based Artifacts In Dark Web Investigations, Arica Kulm

Masters Theses & Doctoral Dissertations

The dark web is the hidden part of the internet that is not indexed by search engines and is only accessible with a specific browser like The Onion Router (Tor). Tor was originally developed as a means of secure communications and is still used worldwide for individuals seeking privacy or those wanting to circumvent restrictive regimes. The dark web has become synonymous with nefarious and illicit content which manifests itself in underground marketplaces containing illegal goods such as drugs, stolen credit cards, stolen user credentials, child pornography, and more (Kohen, 2017). Dark web marketplaces contribute both to illegal drug usage …

Modular Neural Networks For Low-Power Image Classification On Embedded Devices, Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, George K. Thiruvathukal, Yung-Hisang Lu

Computer Science: Faculty Publications and Other Works

Embedded devices are generally small, battery-powered computers with limited hardware resources. It is difficult to run deep neural networks (DNNs) on these devices, because DNNs perform millions of operations and consume significant amounts of energy. Prior research has shown that a considerable number of a DNN’s memory accesses and computation are redundant when performing tasks like image classification. To reduce this redundancy and thereby reduce the energy consumption of DNNs, we introduce the Modular Neural Network Tree architecture. Instead of using one large DNN for the classifier, this architecture uses multiple smaller DNNs (called modules) to progressively classify images …

Tpr: Text-Aware Preference Ranking For Recommender Systems, Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Textual data is common and informative auxiliary information for recommender systems. Most prior art utilizes text for rating prediction, but rare work connects it to top-N recommendation. Moreover, although advanced recommendation models capable of incorporating auxiliary information have been developed, none of these are specifically designed to model textual information, yielding a limited usage scenario for typical user-to-item recommendation. In this work, we present a framework of text-aware preference ranking (TPR) for top-N recommendation, in which we comprehensively model the joint association of user-item interaction and relations between items and associated text. Using the TPR framework, we construct a joint …

A Statistical Impulse Response Model Based On Empirical Characterization Of Wireless Underground Channel, Abdul Salam, Mehmet C. Vuran, Suat Irmak

Faculty Publications

Wireless underground sensor networks (WUSNs) are becoming ubiquitous in many areas. The design of robust systems requires extensive understanding of the underground (UG) channel characteristics. In this paper, an UG channel impulse response is modeled and validated via extensive experiments in indoor and field testbed settings. The three distinct types of soils are selected with sand and clay contents ranging from $13\%$ to $86\%$ and $3\%$ to $32\%$, respectively. The impacts of changes in soil texture and soil moisture are investigated with more than $1,200$ measurements in a novel UG testbed that allows flexibility in soil moisture control. Moreover, the …

Direct Digital Synthesis: A Flexible Architecture For Advanced Signals Research For Future Satellite Navigation Payloads, Pranav R. Patel

Theses and Dissertations

In legacy Global Positioning System (GPS) Satellite Navigation (SatNav) payloads, the architecture does not provide the flexibility to adapt to changing circumstances and environments. GPS SatNav payloads have largely remained unchanged since the system became fully operational in April 1995. Since then, the use of GPS has become ubiquitous in our day-to-day lives. GPS availability is now a basic assumption for distributed infrastructure; it has become inextricably tied to our national power grids, cellular networks, and global financial systems. Emerging advancements of easy to use radio technologies, such as software-defined radios (SDRs), have greatly lowered the difficulty of discovery and …

Using High-Performance Computing Profilers To Understand The Performance Of Graph Algorithms, Costain Nachuma

University of New Orleans Theses and Dissertations

An algorithm designer working with parallel computing systems should know how the characteristics of their implemented algorithm affects various performance aspects of their parallel program. It would be beneficial to these designers if each algorithm came with a specific set of standards that identified which algorithms worked better for a specified system. Therefore, the goal of this paper is to take implementations of four graphing algorithms, extract their features such as memory consumption, scalability using profilers (Vtunes /Tau) to determine which algorithms work to their fullest potential in one of the three systems: GPU, shared memory system, or distributed memory …

A Framework To Support Automatic Certification For Self-Adaptive Systems, Ioannis Nearchou

Masters Theses

Presently, cyber-physical systems are increasingly being integrated into societies, from the economic sector to the nuclear energy sector. Cyber-physical systems are systems that combine physical, digital, human, and other components, which operate through physical means and software. When system errors occur, the consequences of malfunction could negatively impact human life. Academic studies have relied on the MAPE-K feedback loop model to develop various system components to satisfy the self-adaptive features, such that violation of the safety requirements can be minimized. Assurance of system requirement satisfaction is argued through an industrial standard form, called an assurance case, which is usually applied …

Investigating Single Precision Floating General Matrix Multiply In Heterogeneous Hardware, Steven Harris

McKelvey School of Engineering Theses & Dissertations

The fundamental operation of matrix multiplication is ubiquitous across a myriad of disciplines. Yet, the identification of new optimizations for matrix multiplication remains relevant for emerging hardware architectures and heterogeneous systems. Frameworks such as OpenCL enable computation orchestration on existing systems, and its availability using the Intel High Level Synthesis compiler allows users to architect new designs for reconfigurable hardware using C/C++. Using the HARPv2 as a vehicle for exploration, we investigate the utility of several of the most notable matrix multiplication optimizations to better understand the performance portability of OpenCL and the implications for such optimizations on this and …

Resource Optimization In Support Of Iot Applications, Ihab Ahmed Mohammed

Dissertations

With the rise of the Internet of Things (IoT) and smart communities, managing computation and communication resources required by billions of smart devices becomes a concern. To tackle this problem, we develop algorithms for resource management to ensure better Quality of Service (QoS), safety, and performance. We focus our efforts on three problems.

In the first problem, we studied the strict QoS requirements of applications and differentiated service requirements in different situations of vehicular networks. We propose a generic prioritization and resource management algorithm that can be used to prioritize the processing of received packets in vehicular networks. We formulate …

Off-Policy Reinforcement Learning For Efficient And Effective Gan Architecture Search, Tian Yuan, Wang Qin, Zhiwu Huang, Wen Li, Dengxin Dai, Minghao Yang, Jun Wang, Olga Fink

Research Collection School Of Computing and Information Systems

In this paper, we introduce a new reinforcement learning (RL) based neural architecture search (NAS) methodology for effective and efficient generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling, which enables a more effective RL-based search algorithm by targeting the potential global optimal architecture. To improve efficiency, we exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies. Evaluation on two standard benchmark datasets (i.e., CIFAR-10 and STL-10) demonstrates that the proposed method is …

Reconstructability Analysis & Its Occam Implementation, Martin Zwick

Systems Science Faculty Publications and Presentations

This talk will describe Reconstructability Analysis (RA), a probabilistic graphical modeling methodology deriving from the 1960s work of Ross Ashby and developed in the systems community in the 1980s and afterwards. RA, based on information theory and graph theory, resembles and partially overlaps Bayesian networks (BN) and log-linear techniques, but also has some unique capabilities. (A paper explaining the relationship between RA and BN will be given in this special session.) RA is designed for exploratory modeling although it can also be used for confirmatory hypothesis testing. In RA modeling, one either predicts some DV from a set of IVs …

Joint Lattice Of Reconstructability Analysis And Bayesian Network General Graphs, Marcus Harris, Martin Zwick

Systems Science Faculty Publications and Presentations

This paper integrates the structures considered in Reconstructability Analysis (RA) and those considered in Bayesian Networks (BN) into a joint lattice of probabilistic graphical models. This integration and associated lattice visualizations are done in this paper for four variables, but the approach can easily be expanded to more variables. The work builds on the RA work of Klir (1985), Krippendorff (1986), and Zwick (2001), and the BN work of Pearl (1985, 1987, 1988, 2000), Verma (1990), Heckerman (1994), Chickering (1995), Andersson (1997), and others. The RA four variable lattice and the BN four variable lattice partially overlap: there are ten …

Real-Time Counting Of Vehicles Stopped At A Traffic Light Using Vehicular Network Technology, Manuel Contreras, Eric Gamess

Research, Publications & Creative Work

Vehicular Adhoc NETworks (VANETs) is a new and emerging technology for wireless communications that has attracted considerable attention in the last years from the academic, scientific, industrial and governmental communities, due to the improvements and the new features that it brings to the Intelligent Transportation Systems (ITSs). In this paper, we present an algorithm that uses VANET technology to determine the total number of vehicles that are stopped at a traffic light at an intersection. To facilitate an efficient counting, we divide the road into fixed-size road regions and through a leader election mechanism, we designate one vehicle in each …

Functional Programming For Systems Software: Implementing Baremetal Programs In Habit, Donovan Ellison

University Honors Theses

Programming in a baremetal environment, directly on top of hardware with very little to help manage memory or ensure safety, can be dangerous even for experienced programmers. Programming languages can ease the burden on developers and sometimes take care of entire sets of errors. This is not the case for a language like C that will do almost anything you want, for better or worse. To operate in a baremetal environment often requires direct control over memory, but it would be nice to have that capability without sacrificing safety guarantees. Rust is a new language that aims to fit this …

Towards A Cyber-Physical Manufacturing Cloud Through Operable Digital Twins And Virtual Production Lines, Md Rakib Shahriar

Graduate Theses and Dissertations

In last decade, the paradigm of Cyber-Physical Systems (CPS) has integrated industrial manufacturing systems with Cloud Computing technologies for Cloud Manufacturing. Up to 2015, there were many CPS-based manufacturing systems that collected real-time machining data to perform remote monitoring, prognostics and health management, and predictive maintenance. However, these CPS-integrated and network ready machines were not directly connected to the elements of Cloud Manufacturing and required human-in-the-loop. Addressing this gap, we introduced a new paradigm of Cyber-Physical Manufacturing Cloud (CPMC) that bridges a gap between physical machines and virtual space in 2017. CPMC virtualizes machine tools in cloud through web services …

Scalable Multi-Agent Reinforcement Learning For Aggregation Systems, Tanvi Verma

Dissertations and Theses Collection (Open Access)

Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc. for matching restaurants to customers. In these systems, a centralized entity (e.g., Uber) aggregates supply and assigns them to demand so as to optimize a central metric such as profit, number of requests, delay etc. However, individuals (e.g., drivers, delivery boys) in the system are self interested and they try to maximize their own long term profit. The central entity has the full view of the system and …

Online Spatio - Temporal Demand Supply Matching, Meghna Lowalekar

Dissertations and Theses Collection (Open Access)

The rapid growth of cities in developing world coupled with the increase in rural to urban migration have led to cities being identified as the key actor for any nation's economy. Shared mobility has become an integral part of life of people in cities as it improves efficiency and enhances transportation accessibility. As a result, the mismatch between the demand and supply of shared mobility resources has a direct impact on people's life. Thus, the goal of my dissertation is to develop solution strategies for these real-time (online) spatio-temporal demand supply matching problems for shared mobility resources which can enhance …

Edge-Cloud Iot Data Analytics: Intelligence At The Edge With Deep Learning, Ananda Mohon M. Ghosh

Electronic Thesis and Dissertation Repository

Rapid growth in numbers of connected devices, including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Load Balancing In Cloud Computing, Snehal Dhumal

Master's Projects

Cloud computing is one of the top trending technologies which primarily focuses on the end user’s use cases. The service provider needs to provide services to many clients. These increasing number of requests from the clients are giving rise to the new inventions in the load scheduling algorithms. There are different scheduling algorithms which are already present in the cloud computing, and some of them includes the Shortest Job First (SJF), First Come First Serve (FCFS), Round Robin (RR) etc. Though there are different parameters to consider when load balancing in cloud computing, makespan (time difference between start time of …

Stay-At-Home Motor Rehabilitation: Optimizing Spatiotemporal Learning On Low-Cost Capacitive Sensor Arrays, Reid Sutherland

Graduate Theses and Dissertations

Repeated, consistent, and precise gesture performance is a key part of recovery for stroke and other motor-impaired patients. Close professional supervision to these exercises is also essential to ensure proper neuromotor repair, which consumes a large amount of medical resources. Gesture recognition systems are emerging as stay-at-home solutions to this problem, but the best solutions are expensive, and the inexpensive solutions are not universal enough to tackle patient-to-patient variability. While many methods have been studied and implemented, the gesture recognition system designer does not have a strategy to effectively predict the right method to fit the needs of a patient. …