Digital Commons Network^™

A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System, Jianfei Sun, Guowen Xu, Tianwei Zhang, Mamoun Alazab, Robert H. Deng

Research Collection School Of Computing and Information Systems

Aiming for minimizing passengers’ waiting time and vehicles’ vacancy rate, online car-hailing service systems with fog computing has been deployed in various scenarios. In this paper, we focus on addressing the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) private, finegrained and bilateral order matching between passengers and drivers; (2) authenticity verification of passengers’ orders in the form of ciphertext, and (3) temporal assurance of passengers’ ciphertext orders. To the best of our knowledge, no previous system has been designed to meet all three requirements. Existing …

Automating App Review Response Generation Based On Contextual Knowledge, Cuiyun Gao, Wenjie Zhou, Xin Xia, David Lo, Qi Xie, Michael R. Lyu

Research Collection School Of Computing and Information Systems

User experience of mobile apps is an essential ingredient that can influence the user base and app revenue. To ensure good user experience and assist app development, several prior studies resort to analysis of app reviews, a type of repository that directly reflects user opinions about the apps. Accurately responding to the app reviews is one of the ways to relieve user concerns and thus improve user experience. However, the response quality of the existing method relies on the pre-extracted features from other tools, including manually labelled keywords and predicted review sentiment, which may hinder the generalizability and flexibility of …

Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy

Research Collection School Of Computing and Information Systems

The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts (SCs) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and …

Cyber Security Curriculum In Western Australian Primary And Secondary Schools: Interim Report: Curriculum Mapping, Nicola Johnson, Ahmed Ibrahim, Leslie Sikos, Cheryl Glowrey

Research outputs 2022 to 2026

Cyber-crime poses a significant threat to Australians—think of, for example, how scams take advantage of vulnerable people and systems. There is a need to educate people from an early age to protect them from cyberthreats.

Consistent with the increasing prevalence of cyberthreats to individuals and organisations in Australia, the national Australian curriculum has been updated (version 9.0) to include specific content for cyber security for primary and secondary students up to Year 10. Endorsed by Education Ministers in April 2022, the Western Australian School Curriculum and Standards Authority (SCSA) completed a detailed audit of the endorsed Australian Curriculum version 9.0 …

Finding Geodesics Joining Given Points, Lyle Noakes, Erchuan Zhang

Research outputs 2022 to 2026

Finding a geodesic joining two given points in a complete path-connected Riemannian manifold requires much more effort than determining a geodesic from initial data. This is because it is much harder to solve boundary value problems than initial value problems. Shooting methods attempt to solve boundary value problems by solving a sequence of initial value problems, and usually need a good initial guess to succeed. The present paper finds a geodesic γ: [0 , 1] → M on the Riemannian manifold M with γ(0) = x0 and γ(1) = x1 by dividing the interval [0,1] into several sub-intervals, preferably just …

Designing And Using Innovative Learning Spaces: What Teachers Have To Say, Julia E. Morris, Wesley Imms

Research outputs 2022 to 2026

There is no universal definition of what constitutes an innovative learning environment, because each school is unique. Plans to Pedagogy, developed by the University of Melbourne’s Learning Environments Applied Research Network (LEaRN) team, is exploring issues schools identify as they transition to and use innovative learning environments. Embedded in a range of schools across Australia and New Zealand, each school is assigned an academic who works with them to co-design a project targeting the school’s identified spatial challenge. This paper overviews the eight current Plans to Pedagogy projects to give a sense of the issues faced by teachers in terms …

Early Wildfire Detection Using Uavs Integrated With Air Quality And Lidar Sensors, Doaa Rjoub, Ahmad Alsharoa, Ala'eddin Masadeh

Electrical and Computer Engineering Faculty Research & Creative Works

Every year, wildfires burn out countless hectares of lands, resulting in ecological, environmental, and economic damage. This paper presents an energy management system that consists of an unmanned aerial vehicle (UAV) equipped with air quality and light detection and ranging (LiDAR) sensors for monitoring forests and recognizing flames early. We develop a novel approach for autonomous patrolling system. This approach has the advantage of effectively detecting wildfire incidents, while optimizing the energy consumption of the UAV's battery to cover large areas. When a wildfire is detected, the UAV is able to transmit real-time data, such as sensor readings and LiDAR …

New Hybrid Model For Evaluating The Frequency-Dependent Leakage Inductance Of A Variable Inductance Transformer (Vit), Angshuman Sharma, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Skin and proximity effects can cause a significant drop in the effective leakage inductance of a transformer when the operating frequency is increased. Although the magnetic image method-based double-2-D model can calculate the low-frequency leakage inductance with sufficient accuracy, it is inherently a frequency-independent model. While Dowell's 1-D model uses frequency-dependent relations to account for both skin and proximity effects, its accuracy is severely affected by the assumed winding geometry. In this paper, a hybrid model is proposed that uses superposition to combine a modified Dowell's model with the double-2-D model. The proposed model is investigated on a variable inductance …

A Spline-Based Partial Element Equivalent Circuit Method For Electrostatics, Riccardo Torchio, Maximilian Nolte, Sebastian Schops, Albert E. Ruehli

Electrical and Computer Engineering Faculty Research & Creative Works

This contribution investigates the connection between Isogeometric Analysis (IgA) and the Partial Element Equivalent Circuit (PEEC) method for electrostatic problems. We demonstrate that using the spline-based geometry concepts from IgA allows for extracting circuit elements without an explicit meshing step. Moreover, the proposed IgA-PEEC method converges for complex geometries up to three times faster than the conventional PEEC approach and, in turn, it requires a significantly lower number of degrees of freedom to solve a problem with comparable accuracy. The resulting method is closely related to the isogeometric boundary element method. However, it uses lowest-order basis functions to allow for …

Improving Virtual Synchronous Generator Control In Microgrids Using Fuzzy Logic Control, Oroghene Oboreh-Snapps, Rui Bo, Buxin She, Fangxing Fran Li, Hantao Cui

Electrical and Computer Engineering Faculty Research & Creative Works

Virtual synchronous generators (VSG) are designed to mimic the inertia and damping characteristics of synchronous generators (SG), which can improve the frequency response of a microgrid. Unlike synchronous generators whose inertia and damping are restricted by the physical characteristics of the SG, VSG parameters can be more flexibly controlled to adapt to different disturbances. This paper therefore proposes a fuzzy logic controller designed to adaptively set the parameters of the VSG during a frequency event to ensure an improved frequency nadir and rate of change of frequency (ROCOF) response. The proposed control method is implemented and tested on the power …

Chimeranet: U-Net For Hair Detection In Dermoscopic Skin Lesion Images, Norsang Lama, Reda Kasmi, Jason R. Hagerty, R. Joe Stanley, Reagan Harris Young, Jessica Miinch, Januka Nepal, Anand Nambisan, William V. Stoecker

Electrical and Computer Engineering Faculty Research & Creative Works

Hair and ruler mark structures in dermoscopic images are an obstacle preventing accurate image segmentation and detection of critical network features. Recognition and removal of hairs from images can be challenging, especially for hairs that are thin, overlapping, faded, or of similar color as skin or overlaid on a textured lesion. This paper proposes a novel deep learning (DL) technique to detect hair and ruler marks in skin lesion images. Our proposed ChimeraNet is an encoder-decoder architecture that employs pretrained EfficientNet in the encoder and squeeze-and-excitation residual (SERes) structures in the decoder. We applied this approach at multiple image sizes …

Hamiltonian-Driven Adaptive Dynamic Programming With Efficient Experience Replay, Yongliang Yang, Yongping Pan, Cheng Zhong Xu, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a novel efficient experience-replay-based adaptive dynamic programming (ADP) for the optimal control problem of a class of nonlinear dynamical systems within the Hamiltonian-driven framework. The quasi-Hamiltonian is presented for the policy evaluation problem with an admissible policy. With the quasi-Hamiltonian, a novel composite critic learning mechanism is developed to combine the instantaneous data with the historical data. In addition, the pseudo-Hamiltonian is defined to deal with the performance optimization problem. Based on the pseudo-Hamiltonian, the conventional Hamilton–Jacobi–Bellman (HJB) equation can be represented in a filtered form, which can be implemented online. Theoretical analysis is investigated in terms …

De-Embedding For Coupled Three-Port Devices, Yuandong Guo, Bo Pu, Donghyun Kim, Jun Fan

Electrical and Computer Engineering Faculty Research & Creative Works

In many applications, the device under test (DUT) is embedded into a test setup. Various de-embedding techniques have been proposed to expose the real electrical behaviors of a DUT, e.g., the traditional thru-reflect-line and short-open-load-thru algorithms, where the T-matrix and its inverse form are adopted in the mathematical process. In the fields of radiofrequency and electromagnetic compatibility, a DUT may have three coupled ports, and the symmetry in the associated S-matrix breaks down, because the numbers of entry and exist ports are not equal, which results in a non-square T-matrix based upon the definitions. Given that the inverse expression of …

Concurrent Learning-Based Neuro-Adaptive Robust Tracking Control Of Wheeled Mobile Robot: An Event-Triggered Design, Krishanu Nath, Manas Kumar Bera, Sarangapani Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an event-based neuro-adaptive robust tracking controller for a perturbed and networked differential drive mobile robot (DMR) is designed with concurrent learning. A radial basis function neural network, which approximates an unknown perturbation, is used to design an adaptive sliding mode controller (SMC). The RBFNN weights and SMC parameters are estimated online using an adaptive tuning law to ensure performance with reduced chattering. To improve the convergence of RBFNN weight estimation error, a concurrent learning-based adaptive law is derived, which uses measured online and recorded data. Further, a suitable triggering condition is designed to achieve a reduced number …

A New Discontinuous Conduction Mode In A Transformer Coupled High Gain Dc-Dc Converter, Kartikeya J.P. Veeramraju, Jacob Eisen, Joshua L. Rovey, Jonathan W. Kimball

Electrical and Computer Engineering Faculty Research & Creative Works

Electrospray space propulsion thrusters often employ transformer coupled high gain dc-dc converters. These thrusters have a unique load resistance profile that can vary dramatically during operation. Transformer coupled high gain dc-dc converters working in very low power modes can enter a unique discontinuous conduction mode (DCM) caused by a coupling effect between the boost and magnetization inductors in the converter. The transformer magnetization current prevents the boost inductors from discharging properly, which causes premature loss of boost action and higher than expected boost inductor voltages. This mechanism generates abnormally high voltages on the converter's output side, leading to converter and …

Use Of Technology And Its Impact On Higher Order Thinking In The Science Classroom, Mauree Angelina Haage

Dissertations and Theses @ UNI

This study focuses on examining the impact of technology on higher order thinking in the science classroom by first examining the impact of a teacher’s knowledge of Bloom’s Revised Taxonomy on the integration of higher order thinking activities and then by examining what levels of higher order thinking exist when technology was utilized. Previous research has found correlations between the levels of Bloom’s Taxonomy exhibited when using technology, particularly probeware, simulations/virtual labs, and special software like LoggerPro. In addition, previous research has shown a trend of teachers inaccurately categorizing their lessons and activities as having a higher order thinking level …

Mm Percussion Recital, Tyler Darnall

Dissertations and Theses @ UNI

This recital abstract serves as supplemental material to the Master of Music degree recital by Tyler Darnall. The recital will take place on April 15, 2022, at 6:00 p.m. Central Standard Time in Davis Hall at the University of Northern Iowa. This recital will feature works by Jeffrey Dennis Smith, Elliott Carter, J.S. Bach, Alyssa Weinberg, Nicolas Martynciow, John Cage, and Iannis Xenakis. Aiden Endres, Nicole Loftus, Xander Webb, and Matthew Kokotivich will be collaborating with Darnall for his recital. In addition to providing musical and historical context, this document will address specific considerations regarding performance practice for each piece.

Educational Experiences Of First Generation Black African Students With And Without Dis/Abilities, Shehreen Iqtadar

Dissertations and Theses @ UNI

Several studies have focused on the disproportionate representation of students from historically multiply marginalized communities in special education (Artiles, 2011, 2013; Artiles et al., 2005; Brayboy et al., 2007; Cavendish et al., 2018; Cooc & Kiru, 2018; Dunn, 1968; Hosp & Reschly, 2004; O’Connor & Fernandez, 2006; Voulgarides et al., 2017). Only recently, researchers have begun to explore the connections between immigrant and refugee students and special education (Migliarini, 2017; Song, 2018; Qing, 2018). Missing in this literature are the critical accounts of students and (a) their perspectives about the nature of dis/ability and their placement within special education and …

Monetization For Content Generation And User Engagement On Social Media Platforms: Evidence From Paid Q&A, Jonathan Hua Ye, Cecil Eng Huang Chua

Business and Information Technology Faculty Research & Creative Works

Social media platforms want to increase their valuation in terms of total content quantity and user engagement. Monetization is often used to induce user content generation. However, research documents that while monetization increases the quantity of specific kinds of content, it does not necessarily increase the total content quantity or user engagement (i.e., platform value). Furthermore, the impact of monetization may depend on the social status of content creators. This article investigates paid question and answer (paid Q&A). Based on expectancy theory and relevant research, this article hypothesizes the effects of introducing paid Q&A on both total content quantity and …

Information Systems Analysis And Design: Past Revolutions, Present Challenges, And Future Research Directions, Keng Siau, Carson Woo, Veda C. Storey, Roger H.L. Chiang, Cecil Eng Huang Chua, Jon W. Beard

Business and Information Technology Faculty Research & Creative Works

Systems Analysis and Design (SAND) is Undoubtedly a Pillar in the Field of Information Systems (IS). Some Researchers Have Even Claimed that SAND is the Field that Defines the Information Systems Discipline and is the Core of Information Systems. the Past Decades Have Seen the Development of Structured SAND Methodologies and Object-Oriented Methodologies. in the Early 1990s, Key Players in the Field Collaborated to Develop the Unified Modeling Language and the Unified Process. Agile Approaches Followed, as Did Other Dynamic Methods. These Approaches Remain Heavily Employed in the Development of Contemporary Information Systems. at the Same Time, New Approaches Such …

A Novel Echo State Network Autoencoder For Anomaly Detection In Industrial Iot Systems, Fabrizio De Vita, Giorgio Nocera, Dario Bruneo, Sajal K. Das

Computer Science Faculty Research & Creative Works

The Industrial Internet of Things (IIoT) technology had a very strong impact on the realization of smart frameworks for detecting anomalous behaviors that could be potentially dangerous to a system. In this regard, most of the existing solutions involve the use of Artificial Intelligence (AI) models running on Edge devices, such as Intelligent Cyber Physical Systems (ICPS) typically equipped with sensing and actuating capabilities. However, the hardware restrictions of these devices make the implementation of an effective anomaly detection algorithm quite challenging. Considering an industrial scenario, where signals in the form of multivariate time-series should be analyzed to perform a …

Noise Resilient Learning For Attack Detection In Smart Grid Pmu Infrastructure, Prithwiraj Roy, Shameek Bhattacharjee, Sahar Abedzadeh, Sajal K. Das

Computer Science Faculty Research & Creative Works

Falsified data from compromised Phasor Measurement Units (PMUs) in a smart grid induce Energy Management Systems (EMS) to have an inaccurate estimation of the state of the grid, disrupting various operations of the power grid. Moreover, the PMUs deployed at the distribution layer of a smart grid show dynamic fluctuations in their data streams, which make it extremely challenging to design effective learning frameworks for anomaly-based attack detection. In this paper, we propose a noise resilient learning framework for anomaly-based attack detection specifically for distribution layer PMU infrastructure, that show real time indicators of data falsifications attacks while offsetting the …

Cansafe: An Mtd Based Approach For Providing Resiliency Against Dos Attack Within In-Vehicle Networks, Ayan Roy, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

Trending towards autonomous transportation systems, modern vehicles are equipped with hundreds of sensors and actuators that increase the intelligence of the vehicles with a higher level of autonomy, as well as facilitate increased communication with entities outside the in-vehicle network. However, increase in a contact point with the outside world has exposed the controller area network (CAN) of a vehicle to remote security vulnerabilities. In particular, an attacker can inject fake high priority messages within the CAN through the contact points, while preventing legitimate messages from controlling the CAN (Denial-of-Service (DoS) attack). In this paper, we propose a Moving Target …

Distributed Decision Making For V2v Charge Sharing In Intelligent Transportation Systems, Punyasha Chatterjee, Pratham Majumder, Arpita Debnath, Sajal K. Das

Computer Science Faculty Research & Creative Works

Electric vehicles (EVs) have emerged in the intelligent transportation system (ITS) to meet the increasing environmental concerns. To facilitate on-demand requirement of EV charging, vehicle-to-vehicle (V2V) charge transfer can be employed. However, most of the existing approaches to V2V charge sharing are centralized or semi-centralized, incurring huge message overhead, long waiting time, and infrastructural cost. In this paper, we propose novel distributed heuristic algorithms for V2V charge sharing based on the multi-criteria decision-making policy. The problem is mapped to an alias classical problem (i.e., optimum matching in weighted bipartite graphs), where the goal is to maximize the matching cardinality while …

Rssafe: Personalized Driver Behavior Prediction For Safe Driving, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

While the increased demand for taxi services like Uber, Lyft, Hailo, Ola, Grab, Cabify etc. provides livelihood to many drivers, the desire to raise income forces the drivers to work very hard without rest. However, continuous journeys not only affect their health, but also lead to abnormal driving behavior such as rash driving, swerving, sideslipping, sudden brakes, or weaving, leading to accidents in the worst cases. Motivated by the severity of rising accidents and health issues among drivers, this paper proposes a recommendation system, called RsSafe, for the safety of drivers. Aiming to improve the driving quality and the driver's …

An Icn-Based Secure Task Cooperation Scheme In Challenging Wireless Edge Networks, Ningchun Liu, Shuai Gao, Teng Liang, Xindi Hou, Sajal K. Das

Computer Science Faculty Research & Creative Works

Task cooperation is an effective way to execute a complex task in challenging wireless edge networks. Existing TCP/IP-based solutions encounter the problem of low network resource utilization and the heavy dependency of infrastructure connections. Information-centric networking (ICN) is a promising architecture to address these issues. In existing ICN-based task cooperation schemes, the data reuse feature of ICN improves the utilization of network resources, which also brings potential security threats to the reused data. To guarantee the security of data reuse in task cooperation without affecting the data reuse feature, we propose an ICN-based secure task cooperation scheme. In our scheme, …

Toward Feature-Preserving Vector Field Compression, Xin Liang, Sheng Di, Franck Cappello, Mukund Raj, Chunhui Liu, Kenji Ono, Zizhong Chen, Tom Peterka, Hanqi Guo

Computer Science Faculty Research & Creative Works

The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress …

Delivery With Uavs: A Simulated Dataset Via Ats, Giulio Rigoni, Cristina M. Pinotti, Bhumika, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

We consider a delivery food service operated by Unmanned Aerial Vehicles (UAVs). Due to the absence of a dataset on UAVs deliveries in the literature, and since it is not possible to perform real tests, we create a dataset using an open-Air Traffic Simulator (ATS). Precisely, we converted a set of food deliveries operated by wheeled vehicles, proposed in the literature [1], into a set of simulated UAVs deliveries. For each delivery, we ran a UAV flight from the source to the destination. The results showed that, as expected, the UAV's course is shorter than the vehicle trajectory on the …

Targeted Content-Sharing In A Multi-Group Dtn Application Using Attribute-Based Encryption, Xiaofei Cao, Shudip Datta, Ram Charan Bolla, Sanjay Kumar Madria

Computer Science Faculty Research & Creative Works

In a battlefield, multiple groups operate with different missions, but their missions and groups can dynamically change based on the evolving situation. Due to the unavailability of network infrastructure after deployment, group members form a Delay Tolerant Network (DTN) which is prone to security attacks. Hence, based on the mission attributes, group memberships, nodes' interests, and data tags determination, targeted contents need to be distributed in a secure fashion to different users. Though existing Attributes Based Encryption (ABE) can provide security of information, revoking a member from a group is always an issue in DTN as the Attribute Authority (AA) …

Active Learning Augmented Folded Gaussian Model For Anomaly Detection In Smart Transportation, Venkata Praveen Kumar Madhavarapu, Prithwiraj Roy, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Smart transportation networks have become instrumental in smart city applications with the potential to enhance road safety, improve the traffic management system and driving experience. A Traffic Message Channel (TMC) is an IoT device that records the data collected from the vehicles and forwards it to the Roadside Units (RSUs). This data is further processed and shared with the vehicles to inquire the fastest route and incidents that can cause significant delays. The failure of the TMC sensors can have adverse effects on the transportation network. In this paper, we propose a Gaussian distribution-based trust scoring model to identify anomalous …