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2021

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Articles 3151 - 3180 of 3475

Full-Text Articles in Computer Sciences

Infrequent Pattern Detection For Reliable Network Traffic Analysis Using Robust Evolutionary Computation, A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Al-Sakib K. Pathan Jan 2021

Infrequent Pattern Detection For Reliable Network Traffic Analysis Using Robust Evolutionary Computation, A. N. M. Bazlur Rashid, Mohiuddin Ahmed, Al-Sakib K. Pathan

Research outputs 2014 to 2021

While anomaly detection is very important in many domains, such as in cybersecurity, there are many rare anomalies or infrequent patterns in cybersecurity datasets. Detection of infrequent patterns is computationally expensive. Cybersecurity datasets consist of many features, mostly irrelevant, resulting in lower classification performance by machine learning algorithms. Hence, a feature selection (FS) approach, i.e., selecting relevant features only, is an essential preprocessing step in cybersecurity data analysis. Despite many FS approaches proposed in the literature, cooperative co-evolution (CC)-based FS approaches can be more suitable for cybersecurity data preprocessing considering the Big Data scenario. Accordingly, in this paper, we have …

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Detecting Incentivized Review Groups With Co-Review Graph, Yubao Zhang, Shuai Hao, Haining Wang Jan 2021

Detecting Incentivized Review Groups With Co-Review Graph, Yubao Zhang, Shuai Hao, Haining Wang

Computer Science Faculty Publications

Online reviews play a crucial role in the ecosystem of nowadays business (especially e-commerce platforms), and have become the primary source of consumer opinions. To manipulate consumers’ opinions, some sellers of e-commerce platforms outsource opinion spamming with incentives (e.g., free products) in exchange for incentivized reviews. As incentives, by nature, are likely to drive more biased reviews or even fake reviews. Despite e-commerce platforms such as Amazon have taken initiatives to squash the incentivized review practice, sellers turn to various social networking platforms (e.g., Facebook) to outsource the incentivized reviews. The aggregation of sellers who …

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Digital Forensic Readiness Intelligence Crime Repository, Victor R. Kebande, Nickson M. Karie, Kim-Kwang R. Choo, Sadi Alawadi Jan 2021

Digital Forensic Readiness Intelligence Crime Repository, Victor R. Kebande, Nickson M. Karie, Kim-Kwang R. Choo, Sadi Alawadi

Research outputs 2014 to 2021

It may not always be possible to conduct a digital (forensic) investigation post-event if there is no process in place to preserve potential digital evidence. This study posits the importance of digital forensic readiness, or forensic-by-design, and presents an approach that can be used to construct a Digital Forensic Readiness Intelligence Repository (DFRIR). Based on the concept of knowledge sharing, the authors leverage this premise to suggest an intelligence repository. Such a repository can be used to cross-reference potential digital evidence (PDE) sources that may help digital investigators during the process. This approach employs a technique of capturing PDE from …

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Digital Forensic Readiness In Operational Cloud Leveraging Iso/Iec 27043 Guidelines On Security Monitoring, Sheunesu Makura, H. S. Venter, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan, Sadi Alawadi Jan 2021

Digital Forensic Readiness In Operational Cloud Leveraging Iso/Iec 27043 Guidelines On Security Monitoring, Sheunesu Makura, H. S. Venter, Victor R. Kebande, Nickson M. Karie, Richard A. Ikuesan, Sadi Alawadi

Research outputs 2014 to 2021

An increase in the use of cloud computing technologies by organizations has led to cybercriminals targeting cloud environments to orchestrate malicious attacks. Conversely, this has led to the need for proactive approaches through the use of digital forensic readiness (DFR). Existing studies have attempted to develop proactive prototypes using diverse agent-based solutions that are capable of extracting a forensically sound potential digital evidence. As a way to address this limitation and further evaluate the degree of PDE relevance in an operational platform, this study sought to develop a prototype in an operational cloud environment to achieve DFR in the cloud. …

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Interactive Visual Self-Service Data Classification Approach To Democratize Machine Learning, Sridevi Narayana Wagle Jan 2021

Interactive Visual Self-Service Data Classification Approach To Democratize Machine Learning, Sridevi Narayana Wagle

All Master's Theses

Machine learning algorithms often produce models considered as complex black-box models by both end users and developers. Such algorithms fail to explain the model in terms of the domain they are designed for. The proposed Iterative Visual Logical Classifier (IVLC) is an interpretable machine learning algorithm that allows end users to design a model and classify data with more confidence and without having to compromise on the accuracy. Such technique is especially helpful when dealing with sensitive and crucial data like cancer data in the medical domain with high cost of errors. With the help of the proposed interactive and …

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Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen Jan 2021

Efficient Algorithms For Identifying Loop Formation And Computing Θ Value For Solving Minimum Cost Flow Network Problems, Timothy Michael Chávez, Duc Thai Nguyen

Computational Modeling & Simulation Engineering Faculty Publications

While the minimum cost flow (MCF) problems have been well documented in many publications, due to its broad applications, little or no effort have been devoted to explaining the algorithms for identifying loop formation and computing the value needed to solve MCF network problems. This paper proposes efficient algorithms, and MATLAB computer implementation, for solving MCF problems. Several academic and real-life network problems have been solved to validate the proposed algorithms; the numerical results obtained by the developed MCF code have been compared and matched with the built-in MATLAB function Linprog() (Simplex algorithm) for further validation.

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Characterizing Visual Programming Approaches For End-User Developers: A Systematic Review, Mohammad Amin Kuhail, Shahbano Farooq, Rawad Hammad, Mohammed Bahja Jan 2021

Characterizing Visual Programming Approaches For End-User Developers: A Systematic Review, Mohammad Amin Kuhail, Shahbano Farooq, Rawad Hammad, Mohammed Bahja

All Works

Recently many researches have explored the potential of visual programming in robotics, the Internet of Things (IoT), and education. However, there is a lack of studies that analyze the recent evidence-based visual programming approaches that are applied in several domains. This study presents a systematic review to understand, compare, and reflect on recent visual programming approaches using twelve dimensions: visual programming classification, interaction style, target users, domain, platform, empirical evaluation type, test participants' type, number of test participants, test participants' programming skills, evaluation methods, evaluation measures, and accessibility of visual programming tools. The results show that most of the selected …

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Files Cryptography Based On One-Time Pad Algorithm, Ahmad Mohamad Al-Smadi, Ahmad Al-Smadi, Roba Mahmoud Ali Aloglah, Nisrein Abu-Darwish, Ahed Abugabah Jan 2021

Files Cryptography Based On One-Time Pad Algorithm, Ahmad Mohamad Al-Smadi, Ahmad Al-Smadi, Roba Mahmoud Ali Aloglah, Nisrein Abu-Darwish, Ahed Abugabah

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The Vernam-cipher is known as a one-time pad of algorithm that is an unbreakable algorithm because it uses a typically random key equal to the length of data to be coded, and a component of the text is encrypted with an element of the encryption key. In this paper, we propose a novel technique to overcome the obstacles that hinder the use of the Vernam algorithm. First, the Vernam and advance encryption standard AES algorithms are used to encrypt the data as well as to hide the encryption key; Second, a password is placed on the file because of the …

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Convolutional Neural Network Based Vehicle Classification In Adverse Illuminous Conditions For Intelligent Transportation Systems, Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan Jan 2021

Convolutional Neural Network Based Vehicle Classification In Adverse Illuminous Conditions For Intelligent Transportation Systems, Muhammad Atif Butt, Asad Masood Khattak, Sarmad Shafique, Bashir Hayat, Saima Abid, Ki Il Kim, Muhammad Waqas Ayub, Ahthasham Sajid, Awais Adnan

All Works

© 2021 Muhammad Atif Butt et al. In step with rapid advancements in computer vision, vehicle classification demonstrates a considerable potential to reshape intelligent transportation systems. In the last couple of decades, image processing and pattern recognition-based vehicle classification systems have been used to improve the effectiveness of automated highway toll collection and traffic monitoring systems. However, these methods are trained on limited handcrafted features extracted from small datasets, which do not cater the real-time road traffic conditions. Deep learning-based classification systems have been proposed to incorporate the above-mentioned issues in traditional methods. However, convolutional neural networks require piles of …

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Adversarial Reconstruction Loss For Domain Generalization, Bekkouch Imad Eddine Ibrahim, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov Jan 2021

Adversarial Reconstruction Loss For Domain Generalization, Bekkouch Imad Eddine Ibrahim, Dragos Constantin Nicolae, Adil Khan, S. M. Ahsan Kazmi, Asad Masood Khattak, Bulat Ibragimov

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The biggest fear when deploying machine learning models to the real world is their ability to handle the new data. This problem is significant especially in medicine, where models trained on rich high-quality data extracted from large hospitals do not scale to small regional hospitals. One of the clinical challenges addressed in this work is magnetic resonance image generalization for improved visualization and diagnosis of hip abnormalities such as femoroacetabular impingement and dysplasia. Domain Generalization (DG) is a field in machine learning that tries to solve the model’s dependency on the training data by leveraging many related but different data …

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A Parallelized Database Damage Assessment Approach After Cyberattack For Healthcare Systems, Sanaa Kaddoura, Ramzi A. Haraty, Karam Al Kontar, Omar Alfandi Jan 2021

A Parallelized Database Damage Assessment Approach After Cyberattack For Healthcare Systems, Sanaa Kaddoura, Ramzi A. Haraty, Karam Al Kontar, Omar Alfandi

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In the current Internet of things era, all companies shifted from paper-based data to the electronic format. Although this shift increased the efficiency of data processing, it has security drawbacks. Healthcare databases are a precious target for attackers because they facilitate identity theft and cybercrime. This paper presents an approach for database damage assessment for healthcare systems. Inspired by the current behavior of COVID-19 infections, our approach views the damage assessment problem the same way. The malicious transactions will be viewed as if they are COVID-19 viruses, taken from infection onward. The challenge of this research is to discover the …

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Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet Jan 2021

Data-Fusion For Epidemiological Analysis Of Covid-19 Variants In Uae, Anoud Bani-Hani, Anaïs Lavorel, Newel Bessadet

All Works

Since December 2019, a new pandemic has appeared causing a considerable negative global impact. The SARS-CoV-2 first emerged from China and transformed to a global pandemic within a short time. The virus was further observed to be spreading rapidly and mutating at a fast pace, with over 5,775 distinct variations of the virus observed globally (at the time of submitting this paper). Extensive research has been ongoing worldwide in order to get a better understanding of its behaviour, influence and more importantly, ways for reducing its impact. Data analytics has been playing a pivotal role in this research to obtain …

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Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim Jan 2021

Q-Learning Based Routing Protocol For Congestion Avoidance, Daniel Godfrey, Beom Su Kim, Haoran Miao, Babar Shah, Bashir Hayat, Imran Khan, Tae Eung Sung, Ki Il Kim

All Works

The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol, …

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A Smart Dynamic Crowd Evacuation System For Exhibition Centers, Faouzi Kamoun, May El Barachi, Fatna Belqasmi, Abderrazak Hachani Jan 2021

A Smart Dynamic Crowd Evacuation System For Exhibition Centers, Faouzi Kamoun, May El Barachi, Fatna Belqasmi, Abderrazak Hachani

All Works

In this paper, we consider the problem of finding the safest evacuation route in a multi-exit exhibition center while the fire hazard spreads. We first propose a system composed of sensor nodes to collect pertinent safety data. We present a real-time dynamic evacuation system that considers the changing conditions in the risks associated with each hallway segment in terms of walking distance, heat, two major asphyxiant fire gases and congestion. Our system activates smart panels placed at major junctions of the hallways to guide evacuees towards the appropriate exit by displaying the proper escape direction. This work can pave the …

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Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi Jan 2021

Early Detection Of Lung Cancer - A Challenge, Fatma Taher, Neema Prakash, Ashraf Alzaabi

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Lung cancer or lung carcinoma, is a common and serious type of cancer caused by rapid cell growth in tissues of the lung. Lung cancer detection at its earlier stage is very difficult because of the structure of the cell alignment which makes it very challenging. Computed tomography (CT) scan is used to detect the presence of cancer and its spread. Visual analysis of CT scan can lead to late treatment of cancer; therefore, different steps of image processing can be used to solve this issue. A comprehensive framework is used for the classification of pulmonary nodules by combining appearance …

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Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman Jan 2021

Variational Autoencoders And Wasserstein Generative Adversarial Networks For Improving The Anti-Money Laundering Process, Zhiyuan Chen, Waleed Soliman, Amril Nazir, Mohammad Shorfuzzaman

All Works

There has been much recent work on fraud and Anti Money Laundering (AML) detection using machine learning techniques. However, most algorithms are based on supervised techniques. Studies show that supervised techniques often have the limitation of not adapting well to new irregular fraud patterns when the dataset is highly imbalanced. Instead, unsupervised learning can have a better capability to find anomalous and irregular patterns in new transaction. Despite this, unsupervised techniques also have the disadvantage of not being able to give state-of-the-art detection results. We propose a suite of unsupervised and deep learning techniques to implement an anti-money laundering and …

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Deceptive Opinions Detection Using New Proposed Arabic Semantic Features, Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, Nassira Chekkai, Nawel Zemmal, Marwa Hadj Salah Jan 2021

Deceptive Opinions Detection Using New Proposed Arabic Semantic Features, Amel Ziani, Nabiha Azizi, Didier Schwab, Djamel Zenakhra, Monther Aldwairi, Nassira Chekkai, Nawel Zemmal, Marwa Hadj Salah

All Works

Some users try to post false reviews to promote or to devalue other’s products and services. This action is known as deceptive opinions spam, where spammers try to gain or to profit from posting untruthful reviews. Therefore, we conducted this work to develop and to implement new semantic features to improve the Arabic deception detection. These features were inspired from the study of discourse parse and the rhetoric relations in Arabic. Looking to the importance of the phrase unit in the Arabic language and the grammatical studies, we have analyzed and selected the most used unit markers and relations to …

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Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz Jan 2021

Opposition-Based Quantum Bat Algorithm To Eliminate Lower-Order Harmonics Of Multilevel Inverters, Jahedul Islam, Sheikh Tanzim Meraj, Ammar Masaoud, Md Apel Mahmud, Amril Nazir, Muhammad Ashad Kabir, Md Moinul Hossain, Farhan Mumtaz

All Works

Selective harmonic elimination (SHE) technique is used in power inverters to eliminate specific lower-order harmonics by determining optimum switching angles that are used to generate Pulse Width Modulation (PWM) signals for multilevel inverter (MLI) switches. Various optimization algorithms have been developed to determine the optimum switching angles. However, these techniques are still trapped in local optima. This study proposes an opposition-based quantum bat algorithm (OQBA) to determine these optimum switching angles. This algorithm is formulated by utilizing habitual characteristics of bats. It has advanced learning ability that can effectively remove lower-order harmonics from the output voltage of MLI. It can …

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Real-Time Privacy Preserving Framework For Covid-19 Contact Tracing, Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah Jan 2021

Real-Time Privacy Preserving Framework For Covid-19 Contact Tracing, Akashdeep Bhardwaj, Ahmed A. Mohamed, Manoj Kumar, Mohammed Alshehri, Ahed Abugabah

All Works

The recent unprecedented threat from COVID-19 and past epidemics, such as SARS, AIDS, and Ebola, has affected millions of people in multiple countries. Countries have shut their borders, and their nationals have been advised to self-quarantine. The variety of responses to the pandemic has given rise to data privacy concerns. Infection prevention and control strategies as well as disease control measures, especially real-time contact tracing for COVID-19, require the identification of people exposed to COVID-19. Such tracing frameworks use mobile apps and geolocations to trace individuals. However, while the motive may be well intended, the limitations and security issues associated …

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Synergygrids: Blockchain-Supported Distributed Microgrid Energy Trading, Moayad Aloqaily, Ouns Bouachir, Öznur Özkasap, Faizan Safdar Ali Jan 2021

Synergygrids: Blockchain-Supported Distributed Microgrid Energy Trading, Moayad Aloqaily, Ouns Bouachir, Öznur Özkasap, Faizan Safdar Ali

All Works

Growing intelligent cities is witnessing an increasing amount of local energy generation through renewable energy resources. Energy trade among the local energy generators (aka prosumers) and consumers can reduce the energy consumption cost and also reduce the dependency on conventional energy resources, not to mention the environmental, economic, and societal benefits. However, these local energy sources might not be enough to fulfill energy consumption demands. A hybrid approach, where consumers can buy energy from both prosumers (that generate energy) and also from prosumer of other locations, is essential. A centralized system can be used to manage this energy trading that …

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Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri Jan 2021

Active Learning Strategy For Covid-19 Annotated Dataset, Amril Nazir, Ricky Maulana Fajri

All Works

The efficient diagnosis of COVID-19 plays a key role in preventing its spread. Recently, many artificial intelligence techniques, such as the deep neural network approach, have been implemented to help efficient diagnosis of COVID-19. However, the accurate performance of deep learning depends on the tuning of many hyperparameters and a large amount of labeled data. This COVID-19 data bottleneck also leads to insufficient human resources for data labeling, which presents a challenging obstacle. In this paper, a novel discriminative batch-mode active learning (DS3) is proposed to allow faster and more effective COVID-19 data annotation. The framework specifically designed to suit …

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Automatic Fall Risk Detection Based On Imbalanced Data, Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung Jan 2021

Automatic Fall Risk Detection Based On Imbalanced Data, Yen-Hung Liu, Patrick C. K. Hung, Farkhund Iqbal, Benjamin C. M. Fung

All Works

In recent years, the declining birthrate and aging population have gradually brought countries into an ageing society. Regarding accidents that occur amongst the elderly, falls are an essential problem that quickly causes indirect physical loss. In this paper, we propose a pose estimation-based fall detection algorithm to detect fall risks. We use body ratio, acceleration and deflection as key features instead of using the body keypoints coordinates. Since fall data is rare in real-world situations, we train and evaluate our approach in a highly imbalanced data setting. We assess not only different imbalanced data handling methods but also different machine …

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Multi-Level Resource Sharing Framework Using Collaborative Fog Environment For Smart Cities, Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi Jan 2021

Multi-Level Resource Sharing Framework Using Collaborative Fog Environment For Smart Cities, Tariq Qayyum, Zouheir Trabelsi, Asad Waqar Malik, Kadhim Hayawi

All Works

No abstract provided.

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Pothole Detection Under Diverse Conditions Using Object Detection Model, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever Jan 2021

Pothole Detection Under Diverse Conditions Using Object Detection Model, Ibrahim Hassan Syed, Dympna O'Sullivan, Susan Mckeever

Datasets

One of the most important tasks in road maintenance is the detection of potholes. This process is usually done through manual visual inspection, where certified engineers assess recorded images of pavements acquired using cameras or professional road assessment vehicles. Machine learning techniques are now being applied to this problem, with models trained to automatically identify road conditions. However, approaching this real-world problem with machine learning techniques presents the classic problem of how to produce generalizable models. Images and videos may be captured in different illumination conditions, with different camera types, camera angles and resolutions. In this paper we present our …

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Rfid Adaption In Healthcare Organizations: An Integrative Framework, Ahed Abugabah, Louis Sanzogni, Luke Houghton, Ahmad Ali Alzubi, Alaa Abuqabbeh Jan 2021

Rfid Adaption In Healthcare Organizations: An Integrative Framework, Ahed Abugabah, Louis Sanzogni, Luke Houghton, Ahmad Ali Alzubi, Alaa Abuqabbeh

All Works

Radio frequency identification (RFID), also known as electronic label technology, is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals. Medical equipment information management is an important part of the construction of a modern hospital, as it is linked to the degree of diagnosis and care, as well as the hospital's benefits and growth. The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare, as well as to conduct an empirical review of the …

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Gene Selection For Cancer Classification: A New Hybrid Filter-C5.0 Approach For Breast Cancer Risk Prediction, Mohammed Hamim, Ismail El Moudden, Hicham Moutachaouik, Mustapha Hain Jan 2021

Gene Selection For Cancer Classification: A New Hybrid Filter-C5.0 Approach For Breast Cancer Risk Prediction, Mohammed Hamim, Ismail El Moudden, Hicham Moutachaouik, Mustapha Hain

Department of Medicine Faculty Publications

Despite the significant progress made in data mining technologies in recent years, breast cancer risk prediction and diagnosis at an early stage using DNA microarray technology still a real challenging task. This challenge comes especially from the high-dimensionality in gene expression data, i.e., an enormous number of genes versus a few tens of subjects (samples). To overcome this problem of data imbalance, a gene selection phase becomes a crucial step for gene expression data analysis. This study proposes a new Decision Tree model-based attributes (genes) selection strategy, which incorporates two stages: fisher-score-based filter technique and the gene selection ability of …

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Matters Of Biocybersecurity With Consideration To Propaganda Outlets And Biological Agents, Xavier-Lewis Palmer, Ernestine Powell, Lucas Potter, Thaddeus Eze (Ed.), Lee Speakman (Ed.), Cyril Onwubiko (Ed.) Jan 2021

Matters Of Biocybersecurity With Consideration To Propaganda Outlets And Biological Agents, Xavier-Lewis Palmer, Ernestine Powell, Lucas Potter, Thaddeus Eze (Ed.), Lee Speakman (Ed.), Cyril Onwubiko (Ed.)

Electrical & Computer Engineering Faculty Publications

The modern era holds vast modalities in human data utilization. Within Biocybersecurity (BCS), categories of biological information, especially medical information transmitted online, can be viewed as pathways to destabilize organizations. Therefore, analysis of how the public, along with medical providers, process such data, and the methods by which false information, particularly propaganda, can be used to upset the flow of verified information to populations of medical professionals, is important for maintenance of public health. Herein, we discuss some interplay of BCS within the scope of propaganda and considerations for navigating the field.

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Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li Jan 2021

Converting Optical Videos To Infrared Videos Using Attention Gan And Its Impact On Target Detection And Classification Performance, Mohammad Shahab Uddin, Reshad Hoque, Kazi Aminul Islam, Chiman Kwan, David Gribben, Jiang Li

Electrical & Computer Engineering Faculty Publications

To apply powerful deep-learning-based algorithms for object detection and classification in infrared videos, it is necessary to have more training data in order to build high-performance models. However, in many surveillance applications, one can have a lot more optical videos than infrared videos. This lack of IR video datasets can be mitigated if optical-to-infrared video conversion is possible. In this paper, we present a new approach for converting optical videos to infrared videos using deep learning. The basic idea is to focus on target areas using attention generative adversarial network (attention GAN), which will preserve the fidelity of target areas. …

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Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding Jan 2021

Continuity Of Chen-Fliess Series For Applications In System Identification And Machine Learning, Rafael Dahmen, W. Steven Gray, Alexander Schmeding

Electrical & Computer Engineering Faculty Publications

Model continuity plays an important role in applications like system identification, adaptive control, and machine learning. This paper provides sufficient conditions under which input-output systems represented by locally convergent Chen-Fliess series are jointly continuous with respect to their generating series and as operators mapping a ball in an Lp-space to a ball in an Lq-space, where p and q are conjugate exponents. The starting point is to introduce a class of topological vector spaces known as Silva spaces to frame the problem and then to employ the concept of a direct limit to describe convergence. The proof of the main …

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Performance Evaluation Of Snmpv1/2c/3 Using Different Security Models On Raspberry Pi, Eric Gamess, Sergio Hernandez Jan 2021

Performance Evaluation Of Snmpv1/2c/3 Using Different Security Models On Raspberry Pi, Eric Gamess, Sergio Hernandez

Research, Publications & Creative Work

The Simple Network Management Protocol (SNMP) is one of the dominant protocols for network monitoring and configuration. The first two versions of SNMP (v1 and v2c) use the Community-based Security Model (CSM), where the community is transferred in clear text, resulting in a low level of security. With the release of SNMPv3, the User-based Security Model (USM) and Transport Security Model (TSM) were proposed, with strong authentication and privacy at different levels. The Raspberry Pi family of Single-Board Computers (SBCs) is widely used for many applications. To help their integration into network management systems, it is essential to study the …

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