Computer Sciences Commons^™

Twenty Years Of Mobile Banking Services Development And Sustainability: A Bibliometric Analysis Overview (2000–2020), Ayman A. Alsmadi, Ahmed Shuhaiber, Loai N. Alhawamdeh, Rasha Alghazzawi, Manaf Al-Okaily

All Works

The current paper aims to analyze the keywords related to mobile banking (otherwise known as m-banking) issues by focusing on its development from 2000 to 2020, of which the first publication about this issue appeared in the Scopus database. This paper explored and analyzed 1206 research papers using the Scopus database. Bibliometric analysis and content analysis had been conducted through Excel and VOS viewer software to obtain the results. In addition, the findings of this paper reveal that the universal trends and increased production at a global level led to many changes, and the most rampant topic associated with m-banking …

Protection Against Contagion In Complex Networks, Pegah Hozhabrierdi

Dissertations - ALL

In real-world complex networks, harmful spreads, commonly known as contagions, are common and can potentially lead to catastrophic events if uncontrolled. Some examples include pandemics, network attacks on crucial infrastructure systems, and the propagation of misinformation or radical ideas. Thus, it is critical to study the protective measures that inhibit or eliminate contagion in these networks. This is known as the network protection problem.

The network protection problem investigates the most efficient graph manipulations (e.g., node and/or edge removal or addition) to protect a certain set of nodes known as critical nodes. There are two types of critical nodes: (1) …

Characterization Of End-Users’ Engagement And Interaction Experience With Social Media Technologies, Yemisi Oyedele, Darelle Van Greunen

African Conference on Information Systems and Technology

People, particularly digital citizens, gain more technological experiences from their frequent usage of social media technologies. Their experience as end-users occurs before, during, and after their engagement and interaction with the technologies and is popularly described using behaviour-related definitions. However, an end-user's experience with technologies goes beyond the 'click-and-type" definition. This prompts the question, "what are the user experience elements that define and characterise end-users' engagement and interaction with social media technologies?". Using a case study-based approach, end-users' engagement and interaction with social media technologies were identified. The study's findings indicated that several user experience elements were characterised by emotions, …

Interpreting Song Lyrics With An Audio-Informed Pre-Trained Language Model, Yixiao Zhang, Junyan Jiang, Gus Xia, Simon Dixon

Machine Learning Faculty Publications

Lyric interpretations can help people understand songs and their lyrics quickly, and can also make it easier to manage, retrieve and discover songs efficiently from the growing mass of music archives. In this paper we propose BART-fusion, a novel model for generating lyric interpretations from lyrics and music audio that combines a large-scale pre-trained language model with an audio encoder. We employ a cross-modal attention module to incorporate the audio representation into the lyrics representation to help the pre-trained language model understand the song from an audio perspective, while preserving the language model’s original generative performance. We also release the …

Speeding Up The Quantification Of Contrast Sensitivity Functions Using Multidimensional Bayesian Active Learning, Shohaib Shaffiey

McKelvey School of Engineering Graduate Student Theses & Dissertations

No abstract provided.

Sel-Covidnet: An Intelligent Application For The Diagnosis Of Covid-19 From Chest X-Rays And Ct-Scans, Ahmad Al Smadi, Ahed Abugabah, Ahmad Mohammad Al-Smadi, Sultan Almotairi

All Works

COVID-19 detection from medical imaging is a difficult challenge that has piqued the interest of experts worldwide. Chest X-rays and computed tomography (CT) scanning are the essential imaging modalities for diagnosing COVID-19. All researchers focus their efforts on developing viable methods and rapid treatment procedures for this pandemic. Fast and accurate automated detection approaches have been devised to alleviate the need for medical professionals. Deep Learning (DL) technologies have successfully recognized COVID-19 situations. This paper proposes a developed set of nine deep learning models for diagnosing COVID-19 based on transfer learning and implementation in a novel architecture (SEL-COVIDNET). In which …

Enabling Intelligent Iots For Histopathology Image Analysis Using Convolutional Neural Networks, Mohammed H. Alali, Arman Roohi, Shaahin Angizi, Jitender S. Deogun

School of Computing: Faculty Publications

Medical imaging is an essential data source that has been leveraged worldwide in healthcare systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption. We analyze classification accuracy, energy estimation, and hardware utilization …

Positive Dependency Graphs Revisited, Jorge Fandinno, Vladimir Lifschitz

Computer Science Faculty Publications

Theory of stable models is the mathematical basis of answer set programming. Several results in that theory refer to the concept of the positive dependency graph of a logic program. We describe a modification of that concept and show that the new understanding of positive dependency makes it possible to strengthen some of these results.

Understanding The Challenges Of Cryptography-Related Cybercrime And Its Investigation, Sinyong Choi, Katalin Parti

International Journal of Cybersecurity Intelligence & Cybercrime

Cryptography has been applied to a range of modern technologies which criminals also exploit to gain criminal rewards while hiding their identity. Although understanding of cybercrime involving this technique is necessary in devising effective preventive measures, little has been done to examine this area. Therefore, this paper provides an overview of the two articles, featured in the special issue of the International Journal of Cybersecurity Intelligence and Cybercrime, that will enhance our understanding of cryptography-related crime, ranging from cryptocurrency and darknet market to password-cracking. The articles were presented by the winners of the student paper competition at the 2022 International …

Dynamics Of Dark Web Financial Marketplaces: An Exploratory Study Of Underground Fraud And Scam Business, Bo Ra Jung, Kyung-Shick Choi, Claire Seungeun Lee

International Journal of Cybersecurity Intelligence & Cybercrime

The number of Dark Web financial marketplaces where Dark Web users and sellers actively trade illegal goods and services anonymously has been growing exponentially in recent years. The Dark Web has expanded illegal activities via selling various illicit products, from hacked credit cards to stolen crypto accounts. This study aims to delineate the characteristics of the Dark Web financial market and its scams. Data were derived from leading Dark Web financial websites, including Hidden Wiki, Onion List, and Dark Web Wiki, using Dark Web search engines. The study combines statistical analysis with thematic analysis of Dark Web content. Offering promotions …

Kerberoasting: Case Studies Of An Attack On A Cryptographic Authentication Technology, D Demers, Hannarae Lee

International Journal of Cybersecurity Intelligence & Cybercrime

Kerberoasting, an attack vector aimed at the Kerberos authentication protocol, can be used as part of an adversary’s attack arsenal. Kerberos is a type of network authentication protocol that allows a client and server to conduct a mutual verification before providing the requested resource to the client. A successful Kerberoasting attack allows an adversary to leverage the architectural limitations of Kerberos, providing access to user password hashes that can be subject to offline cracking. A cracked user password could give a bad actor the ability to maintain persistence, move laterally, or escalate privileges in a system. Persistence or movement within …

Generalization In Quantum Machine Learning From Few Training Data, Matthias C Caro, Hsin-Yuan Huang, M Cerezo, Kunal Sharma, Andrew Sornborger, Lukasz Cincio, Patrick J Coles

Faculty, Staff and Student Publications

Modern quantum machine learning (QML) methods involve variationally optimizing a parameterized quantum circuit on a training data set, and subsequently making predictions on a testing data set (i.e., generalizing). In this work, we provide a comprehensive study of generalization performance in QML after training on a limited number N of training data points. We show that the generalization error of a quantum machine learning model with T trainable gates scales at worst as [Formula: see text]. When only K ≪ T gates have undergone substantial change in the optimization process, we prove that the generalization error improves to [Formula: see …

Fdrl Approach For Association And Resource Allocation In Multi-Uav Air-To-Ground Iomt Network, Abegaz Mohammed, Aiman Erbad, Hayla Nahom, Abdullatif Albaseer, Mohammed Abdallah, Mohsen Guizani

Machine Learning Faculty Publications

In 6G networks, unmanned aerial vehicles (UAVs) can serve as aerial flying base stations (AFBS) with aerial mobile edge computing (AMEC) server capabilities. AFBS is an increasingly popular solution for delivering time-sensitive applications, extending network coverage, and assisting ground base stations in the healthcare systems for remote areas with limited infrastructure. Furthermore, the UAVs are deployed in the healthcare system to support the Internet of medical things (IoMT) devices in data collection, medical equipment distribution, and providing smart services. However, ensuring the privacy and security of patients’ data with the limited UAV resources is a major challenge. In this paper, …

Proceedings Of The Rust-Edu Workshop, Bart Massey

Rust-Edu Workshop

The 2022 Rust-Edu Workshop was an experiment. We wanted to gather together as many thought leaders we could attract in the area of Rust education, with an emphasis on academic-facing ideas. We hoped that productive discussions and future collaborations would result. Given the quick preparation and the difficulties of an international remote event, I am very happy to report a grand success. We had more than 27 participants from timezones around the globe. We had eight talks, four refereed papers and statements from 15 participants. Everyone seemed to have a good time, and I can say that I learned a …

Reconfigurable Intelligent Surfaces And Capacity Optimization: A Large System Analysis, Aris L. Moustakas, George C. Alexandropoulos, Mérouane Debbah

Machine Learning Faculty Publications

Reconfigurable Intelligent Surfaces (RISs), comprising large numbers of low-cost and almost passive metamaterials with tunable reflection properties, have been recently proposed as an enabling technology for programmable wireless propagation environments. In this paper, we present asymptotic closed-form expressions for the mean and variance of the mutual information metric for a multi-antenna transmitter-receiver pair in the presence of multiple RISs, using methods from statistical physics. While nominally valid in the large system limit, we show that the derived Gaussian approximation for the mutual information can be quite accurate, even for modest-sized antenna arrays and metasurfaces. The above results are particularly useful …

Did Usage Of Mental Health Apps Change During Covid-19? A Comparative Study Based On An Objective Recording Of Usage Data And Demographics, Maryam Aziz, Aiman Erbad, Mohamed Basel Almourad, Majid Altuwairiqi, John Mcalaney, Raian Ali

All Works

This paper aims to objectively compare the use of mental health apps between the pre-COVID-19 and during COVID-19 periods and to study differences amongst the users of these apps based on age and gender. The study utilizes a dataset collected through a smartphone app that objectively records the users' sessions. The dataset was analyzed to identify users of mental health apps (38 users of mental health apps pre-COVID-19 and 81 users during COVID-19) and to calculate the following usage metrics; the daily average use time, the average session time, the average number of launches, and the number of usage days. …

Artificial Neural Networks And Gradient Boosted Machines Used For Regression To Evaluate Gasification Processes: A Review, Owen Sedej, Eric Mbonimpa, Trevor Sleight, Jeremy M. Slagley

Faculty Publications

Waste-to-Energy technologies have the potential to dramatically improve both the natural and human environment. One type of waste-to-energy technology that has been successful is gasification. There are numerous types of gasification processes and in order to drive understanding and the optimization of these systems, traditional approaches like computational fluid dynamics software have been utilized to model these systems. The modern advent of machine learning models has allowed for accurate and computationally efficient predictions for gasification systems that are informed by numerous experimental and numerical solutions. Two types of machine learning models that have been widely used to solve for quantitative …

Computational Study On The Effectiveness Of Flavonoids From Marsilea Crenata C. Presl As Potent Sirt1 Activators And Nfκb Inhibitors, Sri Rahayu, Sasangka Prasetyawan, Sri Widyarti, Mochammad Fitri Atho’Illah, Gatot Ciptadi

Karbala International Journal of Modern Science

Ovarian aging is a natural process in females, and it occurs due to an elevated ROS-induced inflammation caused by oxidative stress. SIRT-1 is a metabolic sensor that tightly regulates oxidative and inflammatory responses. However, this regulative function is antagonized by NFκB. Therefore, the objective of this study was to explore the pathways involved in aging and identify the flavonoid compounds from Marsilea crenata that might be useful as SIRT1 activators and NFκB inhibitors. The screening began with exploring the protein-protein interaction in the experimental process using BioGrid, and the role of the flavonoid was evaluated using STITCH. The interaction between …

Molecular Characterization Of Esbls And Ampc Β-Lactamases In Bacteria Isolated From Currency Notes Circulating In Mosul City, Iraq, Mahmood Zeki Al-Hasso, Shakir Ghazi Gergees, Zahraa Khairialdeen Mohialdeen

Karbala International Journal of Modern Science

The Iraqi currency notes circulating in Mosul city were evaluated for the occurrence of ESBLs and AmpC b-lactamaseproducing bacteria. Four hundred and twenty-two Gram-positive and negative bacterial isolates with different antimicrobial resistance profiles were recovered from 250 samples collected during the period from April to July 2021, among which 150 isolates (35.5%) were multi-drug resistant (MDR). The study found that 16.4% and 14.8% of Gram negative isolates were positive for ESBLs and AmpC phenotypic detection tests, respectively. Interestingly, 6.6% of the isolates were simultaneously positive for both tests. Molecular characterization was carried out using PCR technique to determine the prevalent …

Molecular Docking And Dynamics Simulation Studies To Predict Multiple Medicinal Plants’ Bioactive Compounds Interaction And Its Behavior On The Surface Of Denv-2 E Protein, Arief Hidayatullah, Wira Eka Putra, Muhaimin Rifa’I, Sustiprijatno Sustiprijatno, Diana Widiastuti, Muhammad Fikri Heikal, Hendra Susanto, Wa Ode Salma, Hilal Mulyadi

Karbala International Journal of Modern Science

The envelope protein (E) is a fusion class II protein that is essential for DENV fusion. We use two active compounds derived from commonly used plants in Indonesia: galangin and kaempferide. We ran a docking and 1000 ps molecular dynamic analysis with normal physiological parameters. During the simulation, galangin and kaempferide binding sites fluctuated. But chloroquine has lesser ligand mobility, hence keeping contact with fusion loops, whereas both drugs lose contact with hydrophobic pockets. However, the two active compounds have a more stable ligand configuration. Less than 2 Å alterations were seen in the RMSF simulation of the protein E …

Effective Immersive Analytics For Everyday Use, Benjamin D. Weidner

Theses and Dissertations

Data visualization is an important field of work that takes in uncountable amounts of indexes to create an easy-to-read interpretation of what was previously unreadable. Immersive analytics is the new field that brings 3D data visualization to virtual reality, immersing users directly into the data. Focusing on bringing humans and computers closer together through natural function can benefit the world of data science. In order to accurately utilize this field to benefit this world, principles must be laid out and observed to see which techniques and methods are best fit for an everyday immersive analytics platform. Our findings show that, …

Transformnet: Self-Supervised Representation Learning Through Predicting Geometric Transformations, Muhammad Ali, Sayed Hashim

Computer Vision Faculty Publications

Deep neural networks need a big amount of training data, while in the real world there is a scarcity of data available for training purposes. To resolve this issue unsupervised methods are used for training with limited data. In this report, we describe the unsupervised semantic feature learning approach for recognition of the geometric transformation applied to the input data. The basic concept of our approach is that if someone is unaware of the objects in the images, he/she would not be able to quantitatively predict the geometric transformation that was applied to them. This self supervised scheme is based …

An Analysis Of Android Malware Detection Using Tree Learning Techniques, Kyler D. Dickey

Student Theses and Dissertations

Android malware is a growing threat, coinciding with the increasing adoption of the Android platform. Malware detection methods used to maintain user privacy and system integrity are increasingly becoming the subject of research. Many new methods studied employ learning algorithms to detect malicious programs. This study investigates the use of byte and opcode frequency features as inputs for tree-based machine learning methods. The algorithm is optimized to reduce overfitting given input hyperparameter combinations and is tuned using cross-validation procedures. Lastly, the study deliberates on possible avenues for future research to gather more concrete evidence for the efficacy and cost-effectiveness of …

Image Dehazing Network Based On Densely Connected Residual Block And Channel Pixel Attention, Weidong Jin, Shuli Zhang, Peng Tang, Man Zhang

Journal of System Simulation

Abstract: Abstruct: A lot of research achievements have been made in image dehazing based on neural network,but there aiming at the fog residue, even the color distortion and texture loss, in complex outdoor image dehazing, an image dehazing network based on densely connected residual block and channel pixel attention is proposed. Densely connected residual blocks are used to extract and fuse the features of foggy images,and the repair module with channel pixel attention mechanism is used to repair the color and texture of the feature maps. The experimental results show that, compared with the existing methods, the proposed method and …

Research On Motion Recognition And Tracking For Space Survey And Launch Tasks, Bin Ren, Xiaoyu Wang

Journal of System Simulation

Abstract: Space survey and launch task has high precision and long cycle, and needs to be exposed to direct sunlight for a long time, so that the non-contact action calibration and comparison in a virtual working environment is an efficient way to improve the mission completion success rate. Aiming at the real-time motion tracking of aerospace personnel, a keyframe optimization algorithm for the action recognition is proposed. According to the bone data in the depth image, the bone features are extracted, and the keyframes are extracted by the feature threshold. The characteristic data of the keyframe is input into bi-directional …

Sensorless Control Of Pmsm Based On An Anfis Optimized Flux Sliding Mode Observer, Huilin Zhang, Yujie Jin, Haima Yang

Journal of System Simulation

Abstract: Aiming at the low estimation accuracy of rotor speed and position and the system chattering in sensorless control of permanent magnet synchronous motor (PMSM), an adaptive neuro-fuzzy inference system (ANFIS) is proposed to optimize the flux sliding mode observer(FSMO). Compared with the traditional sliding mode observer, the FSMO improves the estimation accuracy of the rotor flux. The FSMO optimized by ANFIS realizes the on-line adjustment of the observer gain and reduces the system chattering. The improved PLL improves the estimation accuracy of the rotor speed and position. A simulation platform is established to verify the results which show …

Simulation Of Unmanned Tank Clusters Cooperative Combat Based On Military Rules, Chunyan Wang, Hao Ren, Minchi Kuang, Danfeng Wu, Xiangshu Cao, Heng Shi

Journal of System Simulation

Abstract: Modern warfare is developing towards the unmanned, informatized, and intelligent form. Being the important combat equipment in the future land warfare, unmanned tanks have greater advantages of mobility, safety, and economy. Single unmanned tank can not fulfill the complex tasks of large-scale battles as the cooperation of unmanned tank clusters can do. Focuses on the collaborative applications of unmanned tank clusters, the single unmanned tank system model is established, including dynamic control, decision-making, and weapon armor. A collaborative perception model of unmanned tank clusters is designed considering the unmanned tank's own state and the fusion situation. Based on the …

Modeling Of Traffic Flow Velocity Control Strategy For Human-Machine Mixed Driving At Signalized Intersections, Jianxu Zhang, Shuai Hu, Hongyi Jin

Journal of System Simulation

Abstract: In order to analyze the influence of speed control strategy of autonomous vehicle on the operation characteristics of traffic flow, a deterministic decision-making model for intersections with artificially driven vehicles considering the driver's influence on the acquisition of driving information is constructed. An automatic driving speed control strategy considering the influence of the speed of preceding vehicle is proposed, and the continuous Cellular Automata update rules for signalized intersections are constructed respectively. By introducing the different penetration rates of automatic driving, road saturation and control area length parameters, the influence of CAV speed control strategy on the traffic …

Research On Passenger Ship Evacuation Simulation Based On Social Force Model, Qimiao Xie, Shuaishuai Guo

Journal of System Simulation

Abstract: Aiming at the influence of group behavior and different evacuation methods on the passenger ship evacuation process. Three evacuation methods of passengers arriving at the assembly stations with and without group behavior are provided, and the passenger assembly time, congestion area, congestion timing and duration are analyzed. The simulation results show that the group behavior has a significant effect on the passenger assembly time and increases the variation range of the passenger assembly time, and the congestion area with and without group behavior remains the same. The influences of group behavior on the congestion timing and duration are complicated, …

Layout Planning Of Metro-Based Underground Logistics System Network Considering Fuzzy Uncertainties, Wanjie Hu, Jianjun Dong, Rui Ren, Zhilong Chen

Journal of System Simulation

Abstract: Aming at the network design and optimization of metro-based urban underground logistics under uncertainties, the facility components of two-tier metro-based underground logistics system (M-ULS) are proposed. Focus on the comprehensive costs and system utilization rate, a M-ULS network flow assignment model is established based on the expectation of environmental benefits of underground freight transport. a M-ULS network location-allocation-routing fuzzy random programming model is established, and a crisp linearization method is presented. A solution portfolio combining discrete binary chaos particle swarm optimization-genetic algorithm and exact algorithms is designed for combinatorial optimization. Effectiveness of the presented models and algorithms is verified …