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Articles 1 - 26 of 26
Full-Text Articles in Physical Sciences and Mathematics
Unraveling The Versatility And Impact Of Multi-Objective Optimization: Algorithms, Applications, And Trends For Solving Complex Real-World Problems, Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih
Unraveling The Versatility And Impact Of Multi-Objective Optimization: Algorithms, Applications, And Trends For Solving Complex Real-World Problems, Noor A. Rashed, Yossra H. Ali, Tarik A. Rashid, A. Salih
Journal of Soft Computing and Computer Applications
Multi-Objective Optimization (MOO) techniques have become increasingly popular in recent years due to their potential for solving real-world problems in various fields, such as logistics, finance, environmental management, and engineering. These techniques offer comprehensive solutions that traditional single-objective approaches fail to provide. Due to the many innovative algorithms, it has been challenging for researchers to choose the optimal algorithms for solving their problems. This paper examines recently developed MOO-based algorithms. MOO is introduced along with Pareto optimality and trade-off analysis. In real-world case studies, MOO algorithms address complicated decision-making challenges. This paper examines algorithmic methods, applications, trends, and issues in …
Optimization Of Resources Allocation Using Evolutionary Deep Learning, Sanaa Ali Jabber, Soukaena H. Hashem, Shatha H. Jafer
Optimization Of Resources Allocation Using Evolutionary Deep Learning, Sanaa Ali Jabber, Soukaena H. Hashem, Shatha H. Jafer
Journal of Soft Computing and Computer Applications
The Bidirectional Long Short-Term Memory (Bi-LSTM) network structure enables data analysis, enhances decision-making processes, and optimizes resource allocation in cloud computing systems. However, achieving peak network performance relies heavily on choosing the hyperparameters for configuring the network. Enhancing resource allocation improves the Service Level Agreement (SLA) by ensuring efficient utilization and allocation of computational resources based on dynamic workload demands. This paper proposes an approach that integrates a Multi-Objective Evolutionary Algorithm (MOEA) with deep learning techniques to address this challenge. This approach combines the optimization capabilities of MOEA with the learning predictive models to establish a framework for resource allocation …
Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif
Face Mask Detection Based On Deep Learning: A Review, Shahad Fadhil Abbas, Shaimaa Hameed Shaker, Firas. A. Abdullatif
Journal of Soft Computing and Computer Applications
The coronavirus disease 2019 outbreak caused widespread disruption. The World Health Organization has recommended wearing face masks, along with other public health measures, such as social distancing, following medical guidelines, and thermal scanning, to reduce transmission, reduce the burden on healthcare systems, and protect population groups. However, wearing a mask, which acts as a barrier or shield to reduce transmission of infection from infected individuals, hides most facial features, such as the nose, mouth, and chin, on which face detection systems depend, which leads to the weakness of these systems. This paper aims to provide essential insights for researchers and …
Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali
Strangeness Detection From Crowded Video Scenes By Hand-Crafted And Deep Learning Features, Ali A. Hussan, Shaimaa H. Shaker, Akbas Ezaldeen Ali
Journal of Soft Computing and Computer Applications
Video anomaly detection is one of the trickiest issues in intelligent video surveillance because of the complexity of real data and the hazy definition of anomalies. Since abnormal occurrences typically seem different from normal events and move differently. The global optical flow was determined with the maximum accuracy and speed using the Farneback approach for calculating the magnitudes. Two approaches have been used in this study to detect strangeness in the video. These approaches are Deep Learning (DL) and manuality. The first method uses the activity map's development of entropy to detect the oddity in the video using a particular …
A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy
A Comprehensive Analysis Of Deep Learning And Swarm Intelligence Techniques To Enhance Vehicular Ad-Hoc Network Performance, Hussein K. Abdul Atheem, Israa T. Ali, Faiz A. Al Alawy
Journal of Soft Computing and Computer Applications
The primary elements of Intelligent Transportation Systems (ITSs) have become Vehicular Ad-hoc NETworks (VANETs), allowing communication between the infrastructure environment and vehicles. The large amount of data gathered by connected vehicles has simplified how Deep Learning (DL) techniques are applied in VANETs. DL is a subfield of artificial intelligence that provides improved learning algorithms able to analyzing and process complex and heterogeneous data. This study explains the power of DL in VANETs, considering applications like decision-making, vehicle localization, anomaly detection, traffic prediction and intelligent routing, various types of DL, including Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs) are …
A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi
A Novel Approach To Generate Dynamic S-Box For Lightweight Cryptography Based On The 3d Hindmarsh Rose Model, Ala'a Talib Khudhair, Abeer Tariq Maolood, Ekhlas Khalaf Gbashi
Journal of Soft Computing and Computer Applications
In lightweight cryptography, the absence of an S-Box in some algorithms like speck, Tiny Encryption Algorithm, or the presence of a fixed S-Box in others like Advanced Encryption Standard can make them more vulnerable to attacks. This study introduces an innovative method for creating a dynamic 6-bit S-Box (8×8) in octal format. The generating process of S-Box passes through two phases. The first is the number initialization phase. This phase involves generating sequence numbers 1, sequence numbers 2, and sequence numbers 3 depending on Xi, Yi, and Zi values generated using the 3D Hindmarsh …
The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana
The Robust Digital Video Watermarking Methods: A Comparative Study, Ebtehal Talib, Abeer Salim Jamil, Nidaa Flaih Hassan, Muhammad Ehsan Rana
Journal of Soft Computing and Computer Applications
Digital data such as images, audio, and video have become widely available since the invention of the Internet. Due to the ease of access to this multimedia, challenges such as content authentication, security, copyright protection, and ownership determination arose. In this paper, an explanation of watermark techniques, embedding, and extraction methods are provided. It further discusses the utilization of artificial intelligence methods and conversion of host media from the spatial domain to the frequency domain; these methods aim to improve the quality of watermarks. This paper also included a classification of the basic characteristics of the digital watermark and the …
Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal
Foxann: A Method For Boosting Neural Network Performance, Mahmood A. Jumaah, Yossra H. Ali, Tarik A. Rashid, S. Vimal
Journal of Soft Computing and Computer Applications
Artificial neural networks play a crucial role in machine learning and there is a need to improve their performance. This paper presents FOXANN, a novel classification model that combines the recently developed Fox optimizer with ANN to solve ML problems. Fox optimizer replaces the backpropagation algorithm in ANN; optimizes synaptic weights; and achieves high classification accuracy with a minimum loss, improved model generalization, and interpretability. The performance of FOXANN is evaluated on three standard datasets: Iris Flower, Breast Cancer Wisconsin, and Wine. The results presented in this paper are derived from 100 epochs using 10-fold cross-validation, ensuring that all dataset …
Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy
Surveying Machine Learning In Cyberattack Datasets: A Comprehensive Analysis, Azhar F. Al-Zubidi, Alaa Kadhim Farhan, El-Sayed M. El-Kenawy
Journal of Soft Computing and Computer Applications
Cyberattacks have become one of the most significant security threats that have emerged in the last couple of years. It is imperative to comprehend such attacks; thus, analyzing various kinds of cyberattack datasets assists in constructing the precise intrusion detection models. This paper tries to analyze many of the available cyberattack datasets and compare them with many of the fields that are used to detect and predict cyberattack, like the Internet of Things (IoT) traffic-based, network traffic-based, cyber-physical system, and web traffic-based. In the present paper, an overview of each of them is provided, as well as the course of …
Network-Based Representations And Dynamic Discrete Choice Models For Multiple Discrete Choice Analysis, Huy Hung Tran, Tien Mai
Network-Based Representations And Dynamic Discrete Choice Models For Multiple Discrete Choice Analysis, Huy Hung Tran, Tien Mai
Research Collection School Of Computing and Information Systems
In many choice modeling applications, consumer demand is frequently characterized as multiple discrete, which means that consumer choose multiple items simultaneously. The analysis and prediction of consumer behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application …
An In-Network Approach For Pmu Missing Data Recovery With Data Plane Programmability, Jack Norris
An In-Network Approach For Pmu Missing Data Recovery With Data Plane Programmability, Jack Norris
Computer Science and Computer Engineering Undergraduate Honors Theses
Phasor measurement unit (PMU) systems often experience unavoidable missing and erroneous measurements, which undermine power system observability and operational effectiveness. Traditional solutions for recovering missing PMU data employ a centralized approach at the control center, resulting in lengthy recovery times due to data transmission and aggregation. In this work, we leverage P4-based programmable networks to expedite missing data recovery. Our approach utilizes the data plane programmability offered by P4 to present an in-network solution for PMU data recovery. We establish a data-plane pipeline on P4 switches, featuring a customized PMU protocol parser, a missing data detection module, and an auto-regressive …
Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark
Side Channel Detection Of Pc Rootkits Using Nonlinear Phase Space, Rebecca Clark
Honors Theses
Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, …
Creating Interpretable Deep Learning Models To Identify Species Using Environmental Dna Sequences, Samuel Waggoner
Creating Interpretable Deep Learning Models To Identify Species Using Environmental Dna Sequences, Samuel Waggoner
Honors College
This research aims to develop an interpretable and fast machine learning (ML) model for identifying species using environmental DNA (eDNA). eDNA is a technique used to detect the presence or absence of species in an ecosystem by analyzing the DNA that animals naturally leave behind in water or soil. However, there can be millions of sequences to classify and the reference databases are sizeable, so traditional methods such as BLAST are slow. Convolutional neural networks (CNNs) have been shown to be 150 times faster at classifying sequences. In this work, we create a CNN that achieves 92.5% accuracy, surpassing the …
Ghost Connect-Net: A Connectivity-Based Companion Network To Enhance Pruning Methods, Mary Isabelle Wisell
Ghost Connect-Net: A Connectivity-Based Companion Network To Enhance Pruning Methods, Mary Isabelle Wisell
Honors College
Deep neural network (DNN) approaches excel in various real-world applications like robotics and computer vision, yet their computational demands and memory requirements hinder usability on advanced devices. Also, larger models heighten overparameterization risks, making networks more vulnerable to input disturbances. Recent studies aim to boost DNN efficiency by trimming redundant neurons or filters based on task relevance. Instead of introducing a new pruning method, this project aims to enhance existing techniques by introducing a companion network, Ghost Connect-Net (GC-Net), to monitor the connections in the original network. The initial weights of GC- Net are equal to the connectivity measurements of …
A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka
A Design Science Approach To Investigating Decentralized Identity Technology, Janelle Krupicka
Cybersecurity Undergraduate Research Showcase
The internet needs secure forms of identity authentication to function properly, but identity authentication is not a core part of the internet’s architecture. Instead, approaches to identity verification vary, often using centralized stores of identity information that are targets of cyber attacks. Decentralized identity is a secure way to manage identity online that puts users’ identities in their own hands and that has the potential to become a core part of cybersecurity. However, decentralized identity technology is new and continually evolving, which makes implementing this technology in an organizational setting challenging. This paper suggests that, in the future, decentralized identity …
Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian Gao, Yu Qu, Sheng Yu, Yue Duan, Heng Yin
Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian Gao, Yu Qu, Sheng Yu, Yue Duan, Heng Yin
Research Collection School Of Computing and Information Systems
Pseudocode diffing precisely locates similar parts and captures differences between the decompiled pseudocode of two given binaries. It is particularly useful in many security scenarios such as code plagiarism detection, lineage analysis, patch, vulnerability analysis, etc. However, existing pseudocode diffing and binary diffing tools suffer from low accuracy and poor scalability, since they either rely on manually-designed heuristics (e.g., Diaphora) or heavy computations like matrix factorization (e.g., DeepBinDiff). To address the limitations, in this paper, we propose a semantics-aware, deep neural network-based model called SIGMADIFF. SIGMADIFF first constructs IR (Intermediate Representation) level interprocedural program dependency graphs (IPDGs). Then it uses …
Temporal Implicit Multimodal Networks For Investment And Risk Management, Meng Kiat Gary Ang, Ee-Peng Lim
Temporal Implicit Multimodal Networks For Investment And Risk Management, Meng Kiat Gary Ang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Many deep learning works on financial time-series forecasting focus on predicting future prices/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a …
Introducing Flexible Assessment Into A Computer Networks Course: A Case Study, Joe Meehean
Introducing Flexible Assessment Into A Computer Networks Course: A Case Study, Joe Meehean
Journal of Mathematics and Science: Collaborative Explorations
With overall positive results and limited drawbacks, I have adapted modern pedagogical techniques to address a common difficulty encountered when teaching a computer networks course. Due to the tiered nature of the skills taught in the course, students often fail unnecessarily. Using mastery learning, competency-based education, and specifications grading as a foundation, I have developed a course that allows students with varied skills and abilities to pass. The heart of this approach is the flexible assessment of programming assignments which eliminates due dates and allows students to have their work graded and regraded without penalty. Flexible assessment also defines an …
Age Of Sensing Empowered Holographic Isac Framework For Nextg Wireless Networks: A Vae And Drl Approach, Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Monishanker Halder, Choong Seon Hong
Age Of Sensing Empowered Holographic Isac Framework For Nextg Wireless Networks: A Vae And Drl Approach, Apurba Adhikary, Avi Deb Raha, Yu Qiao, Md. Shirajum Munir, Monishanker Halder, Choong Seon Hong
School of Cybersecurity Faculty Publications
This paper proposes an artificial intelligence (AI) framework that leverages integrated sensing and communication (ISAC), aided by the age of sensing (AoS) to ensure the timely location updates of the users for a holographic MIMO (HMIMO)- enabled wireless network. The AI-driven framework guarantees optimal power allocation for efficient beamforming by activating the minimal number of grids from the HMIMO base station. An optimization problem is formulated to maximize the sensing utility function, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received signal, beam-pattern gains to improve the sensing SINR of reflected echo signals and maximizing the evidence lower bound …
A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi
A Systemic Mapping Study On Intrusion Response Systems, Adel Rezapour, Mohammad Ghasemigol, Daniel Takabi
School of Cybersecurity Faculty Publications
With the increasing frequency and sophistication of network attacks, network administrators are facing tremendous challenges in making fast and optimum decisions during critical situations. The ability to effectively respond to intrusions requires solving a multi-objective decision-making problem. While several research studies have been conducted to address this issue, the development of a reliable and automated Intrusion Response System (IRS) remains unattainable. This paper provides a Systematic Mapping Study (SMS) for IRS, aiming to investigate the existing studies, their limitations, and future directions in this field. A novel semi-automated research methodology is developed to identify and summarize related works. The innovative …
Mitigating Cyber Espionage: A Network Security Strategy Using Notifications, Claire Headland
Mitigating Cyber Espionage: A Network Security Strategy Using Notifications, Claire Headland
Williams Honors College, Honors Research Projects
Network security and its mitigation of cyber espionage is paramount to the confidentiality, integrity, and availability of data within the intelligence field. With the advancing efficacy of social engineering to execute cyber espionage attacks, further measures and fail-safe mechanisms have become necessary. If a malicious actor successfully penetrates the network, suspending confidential data transmissions over the compromised network becomes crucial. However, connected users need a platform to receive security notifications and, therefore, need to know that their continued network use compromises more data. This project eliminates this by achieving two primary objectives: designing a multi- layered, hardened, and segmented network …
Privacy And Security Of The Windows Registry, Edward L. Amoruso
Privacy And Security Of The Windows Registry, Edward L. Amoruso
Graduate Thesis and Dissertation 2023-2024
The Windows registry serves as a valuable resource for both digital forensics experts and security researchers. This information is invaluable for reconstructing a user's activity timeline, aiding forensic investigations, and revealing other sensitive information. Furthermore, this data abundance in the Windows registry can be effortlessly tapped into and compiled to form a comprehensive digital profile of the user. Within this dissertation, we've developed specialized applications to streamline the retrieval and presentation of user activities, culminating in the creation of their digital profile. The first application, named "SeeShells," using the Windows registry shellbags, offers investigators an accessible tool for scrutinizing and …
Towards Explainable Neural Network Fairness, Mengdi Zhang
Towards Explainable Neural Network Fairness, Mengdi Zhang
Dissertations and Theses Collection (Open Access)
Neural networks are widely applied in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug, non-transparent and subject to fairness issues. Discrimination has been observed in various machine learning models, including Large Language Models (LLMs), which calls for systematic fairness evaluation (i.e., testing, verification or even certification) before their deployment in ethic-relevant domains. If a model is found to be discriminating, we must apply systematic measure to improve its fairness. In the literature, multiple categories of fairness improving methods have been discussed, including pre-processing, in-processing and post-processing.
In this dissertation, …
Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen
Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen
Research Collection School Of Computing and Information Systems
Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this …
Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang
Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang
Research Collection School Of Computing and Information Systems
Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static …
Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang
Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang
Research Collection School Of Computing and Information Systems
Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static …