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Influence Maximization Based On A Non-Dominated Sorting Genetic Algorithm, Elaf Adel Abbas, Huda Naji Nawaf 2021 College of Information Technology, University of Babylon

Influence Maximization Based On A Non-Dominated Sorting Genetic Algorithm, Elaf Adel Abbas, Huda Naji Nawaf

Karbala International Journal of Modern Science

Influence Maximization (IM) is a problem represented by a set of users who are specified in advance and are usually called the seed. The latter can influence their friends, who can in turn influence others and so on until it reaches the largest number of users within the network. This issue is of ultimate importance in a variety of fields. In the current study, a Non-dominated Sorting Genetic Algorithm II (NSGA-II) has been adopted in influence maximization to produce the so-called NSGAII based IM algorithm (NSGAII-IM). Principally, the population should be represented with individuals of variable lengths as the seed ...


Editorial Board, 2021 Karbala International Journal of Modern Science

Editorial Board

Karbala International Journal of Modern Science

No abstract provided.


Understanding Ransomware Trajectory To Create An Informed Prediction, Jacob D. Klusnick 2021 Portland State University

Understanding Ransomware Trajectory To Create An Informed Prediction, Jacob D. Klusnick

University Honors Theses

Ransomware is a form of extortion in which digital files are rendered inaccessible until a ransom payment is made. Modern ransomware emerged in 2006 and its destructive influence has been expanding ever since. In recent years cybercriminals have evolved who they target, what computer systems they target, and how they infect those systems. Meanwhile, cybersecurity experts have modelled ransomware methods allowing them to innovate their defense techniques across three paradigms: recovery, detection, and prevention. Ultimately either ransomware attackers or ransomware defenders will dominate this ongoing conflict. A review of the literature indicates that the ransomware crime wave will likely be ...


Rebalancing Shared Mobility Systems By User Incentive Scheme Via Enforcement Learning, Matthew Brian Schofield 2021 Rowan University

Rebalancing Shared Mobility Systems By User Incentive Scheme Via Enforcement Learning, Matthew Brian Schofield

Theses and Dissertations

Shared mobility systems regularly suffer from an imbalance of vehicle supply within the system, leading to users being unable to receive service. If such imbalance problems are not mitigated some users will not be serviced. There is an increasing interest in the use of reinforcement learning (RL) techniques for improving the resource supply balance and service level of systems. The goal of these techniques is to produce an effective user incentivization policy scheme to encourage users of a shared mobility system to slightly alter their travel behavior in exchange for a small monetary incentive. These slight changes in user behavior ...


Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin 2021 Independent Researcher

Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin

Journal of Digital Forensics, Security and Law

The security of a computer system depends on OS kernel protection. It is crucial to reveal and inspect new attacks on kernel data, as these are used by hackers. The purpose of this paper is to continue research into attacks on dynamically allocated data in the Windows OS kernel and demonstrate the capacity of MemoryRanger to prevent these attacks. This paper discusses three new hijacking attacks on kernel data, which are based on bypassing OS security mechanisms. The first two hijacking attacks result in illegal access to files open in exclusive access. The third attack escalates process privileges, without applying ...


Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan 2021 Peking University, China

Algebraic Graph-Assisted Bidirectional Transformers For Molecular Property Prediction, Dong Chen, Kaifu Gao, Duc Duy Nguyen, Xin Chen, Yi Jiang, Guo-Wei Wei, Feng Pan

Mathematics Faculty Publications

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information. Algebraic graph, specifically, element-specific multiscale weighted colored algebraic graph, embeds complementary 3D molecular information into graph invariants. We propose an algebraic graph-assisted bidirectional transformer (AGBT) framework by fusing representations generated by algebraic graph and bidirectional transformer, as well as ...


Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz 2021 University of Louisville

Early Assessment Of Lung Function In Coronavirus Patients Using Invariant Markers From Chest X-Rays Images, Mohamed Elsharkawy, Ahmed Sharafeldeen, Fatma Taher, Ahmed Shalaby, Ahmed Soliman, Ali Mahmoud, Mohammed Ghazal, Ashraf Khalil, Norah Saleh Alghamdi, Ahmed Abdel Khalek Abdel Razek, Eman Alnaghy, Moumen T. El-Melegy, Harpal Singh Sandhu, Guruprasad A. Giridharan, Ayman El-Baz

All Works

The primary goal of this manuscript is to develop a computer assisted diagnostic (CAD) system to assess pulmonary function and risk of mortality in patients with coronavirus disease 2019 (COVID-19). The CAD system processes chest X-ray data and provides accurate, objective imaging markers to assist in the determination of patients with a higher risk of death and thus are more likely to require mechanical ventilation and/or more intensive clinical care.To obtain an accurate stochastic model that has the ability to detect the severity of lung infection, we develop a second-order Markov-Gibbs random field (MGRF) invariant under rigid transformation ...


Examining Dimensions Of Patient Satisfaction With Telemedicine, Robert Garcia 2021 DePaul University

Examining Dimensions Of Patient Satisfaction With Telemedicine, Robert Garcia

College of Computing and Digital Media Dissertations

During the outbreak of the novel coronavirus (COVID-19) medical institutions and practitioners have drastically increased their adoption of telemedicine. The proliferation of telemedicine systems has sparked renewed interest among IS researchers in evaluating its usage. One of the main indicators used to measure the success of telemedicine services is patient satisfaction. Yet several problems exist with current methods used to evaluate telemedicine satisfaction. Patient satisfaction with telemedicine is frequently evaluated using either single question items or handmade instruments that are seldom assessed for validity. While telemedicine satisfaction is typically evaluated through single measures, satisfaction is considered a complex and multidimensional ...


Identifying Optimal Course Structures Using Topic Models, Tehut Tesfaye Biru 2021 Dartmouth College

Identifying Optimal Course Structures Using Topic Models, Tehut Tesfaye Biru

Dartmouth College Undergraduate Theses

This research project investigates whether there exists an optimal way to structure topics in educational course content that results in higher levels of engagement among students. It is implemented by fitting topic models to transcripts of educational videos contained in the Khan Academy platform. The fitted models were used to extract topic trajectories across time for each video and subsequently clustered based on whether they have similar “shapes”. The differences in mean engagement metrics per cluster suggest that some course shapes are more palatable to students regardless of subject matter. Additionally, the topic trajectories suggest a constant progression of topics ...


Facing Truths: Facial Recognition Software In Digital Archives, Rebecca Bakker, Kelley Flannery Rowan 2021 Florida International University

Facing Truths: Facial Recognition Software In Digital Archives, Rebecca Bakker, Kelley Flannery Rowan

Works of the FIU Libraries

This presentation discusses research conducted on various facial recognition software and was funded by a LYRASIS Catalyst Fund grant. The goal of the research was to determine whether facial recognition software could be adapted to work with older, often faded or discolored historical photos and still accurately identify faces in photographs. Such software capabilities would be highly beneficial for librarians and archivists in creating quality metadata by identifying unknown people in photos. It would also assist archivists in finding the photos patrons and partners are seeking. The research brought to light the many ethical controversies associated with facial recognition technology ...


Line Sampling In Participating Media, Hsu Cheng 2021 Dartmouth College

Line Sampling In Participating Media, Hsu Cheng

Dartmouth College Master’s Theses and Essays

Participating media, such as fog, fire, dust, and smoke, surrounds us in our daily life. Rendering participating media efficiently has always been a challenging task in physically based rendering. Line sampling has been derived to be an alternative method in direct lighting recently. Since line sampling takes visibility into account, it could reduce variance in the same render time compared to point sampling. We leverage the benefits of line sampling in the context of evaluating direct lighting in participating media. We express the direct lighting as a three-dimensional integral and perform line sampling in any one of them. We show ...


Reimagining The Archive For Computational Analysis At Scale, Jamie Rogers 2021 Florida International University

Reimagining The Archive For Computational Analysis At Scale, Jamie Rogers

Works of the FIU Libraries

This presentation was part of a three-segment panel discussion sponsored by IS&T, the Society for Imaging Science and Technology, titled "OCR and Text Recognition: Workflows, Trends, and New Applications." This segment covers ways in which we have re-conceptualized archive materials as computationally useful data as well as the value of utilizing data at scale to impact research possibilities. We have been able to accomplish this through an ongoing project "dLOC as Data: A Thematic Approach to Caribbean Newspapers," a collaborative initiative between the Digital Library of the Caribbean, University of Florida, and Florida International University.


Advancing The Ability To Predict Cognitive Decline And Alzheimer’S Disease Based On Genetic Variants Beyond Amyloid-Beta And Tau, Naveen Rawat 2021 San Jose State University

Advancing The Ability To Predict Cognitive Decline And Alzheimer’S Disease Based On Genetic Variants Beyond Amyloid-Beta And Tau, Naveen Rawat

Master's Projects

A growing amount of neurodegenerative R&D is focused on identifying genomic- based explanations of AD that are beyond Amyloid-b and Tau. The proposed effort involves identifying some of the genomic variations, such as single nucleotide polymorphisms (SNPs), allele , chromosome, epigenetic contributors to MCI and AD that are beyond Aβ and Tau.

The project involves building a prediction model based on a support vector machine (SVM) classifier that takes into account the genomic variations and epigenetic factors to predict the early stage of mild cognitive impairment (MCI) and Alzheimer disease (AD). To achieve this, picking up important feature sets which ...


Witness For Two-Site Enabled Coordination, Sriram Priyatham Siram 2021 San Jose State University

Witness For Two-Site Enabled Coordination, Sriram Priyatham Siram

Master's Projects

Many replicated data services utilize majority quorums to safely replicate data changes in the presence of server failures. Majority quorum-based services require a simple majority of the servers to be operational for the service to stay available. A key limitation of the majority quorum is that if a service is composed of just two servers, progress cannot be made even if a single server fails because the majority quorum size is also two. This is called the Two-Server problem. A problem similar to the Two-Server problem occurs when a service’s servers are spread across only two failure domains. Servers ...


A Configurable Social Network For Running Irb-Approved Experiments, Mihovil Mandic 2021 Dartmouth College

A Configurable Social Network For Running Irb-Approved Experiments, Mihovil Mandic

Dartmouth College Undergraduate Theses

Our world has never been more connected, and the size of the social media landscape draws a great deal of attention from academia. However, social networks are also a growing challenge for the Institutional Review Boards concerned with the subjects’ privacy. These networks contain a monumental variety of personal information of almost 4 billion people, allow for precise social profiling, and serve as a primary news source for many users. They are perfect environments for influence operations that are becoming difficult to defend against. Motivated to study online social influence via IRB-approved experiments, we designed and implemented a flexible, scalable ...


Learn Biologically Meaningful Representation With Transfer Learning, Di He 2021 City University of New York (CUNY)

Learn Biologically Meaningful Representation With Transfer Learning, Di He

Dissertations, Theses, and Capstone Projects

Machine learning has made significant contributions to bioinformatics and computational biol­ogy. In particular, supervised learning approaches have been widely used in solving problems such as bio­marker identification, drug response prediction, and so on. However, because of the limited availability of comprehensively labeled and clean data, constructing predictive models in super­ vised settings is not always desirable or possible, especially when using data­hunger, red­hot learning paradigms such as deep learning methods. Hence, there are urgent needs to develop new approaches that could leverage more readily available unlabeled data in driving successful machine learning ap­ plications in this ...


The “Knapsack Problem” Workbook: An Exploration Of Topics In Computer Science, Steven Cosares 2021 CUNY La Guardia Community College

The “Knapsack Problem” Workbook: An Exploration Of Topics In Computer Science, Steven Cosares

Open Educational Resources

This workbook provides discussions, programming assignments, projects, and class exercises revolving around the “Knapsack Problem” (KP), which is widely a recognized model that is taught within a typical Computer Science curriculum. Throughout these discussions, we use KP to introduce or review topics found in courses covering topics in Discrete Mathematics, Mathematical Programming, Data Structures, Algorithms, Computational Complexity, etc. Because of the broad range of subjects discussed, this workbook and the accompanying spreadsheet files might be used as part of some CS capstone experience. Otherwise, we recommend that individual sections be used, as needed, for exercises relevant to a course in ...


A Novel Color Image Encryption Scheme Based On Arnold’S Cat Map And 16-Byte S-Box, Tariq Shah, Ayesha Qureshi, Muhammad Usman 2021 Quaid-i-Azam University

A Novel Color Image Encryption Scheme Based On Arnold’S Cat Map And 16-Byte S-Box, Tariq Shah, Ayesha Qureshi, Muhammad Usman

Applications and Applied Mathematics: An International Journal (AAM)

The presented work sets out to subsidize to the general body of knowledge in the field of cryptography application by evolving color image encryption and decryption scheme based on the amalgamation of pixel shuffling and efficient substitution. Arnold’s cat map is applied to snap off the correlation in pixels of image and the shuffled image is encrypted by 16-byte S-box substitution. Computer simulations with a standard test image and the outcome is presented to scrutinize the competence of the projected system. Several image-quality measures and security analyses have been made out for the encrypted image to estimate the statistical ...


Towards Automated Software Evolution Of Data-Intensive Applications, Yiming Tang 2021 The Graduate Center, City University of New York

Towards Automated Software Evolution Of Data-Intensive Applications, Yiming Tang

Dissertations, Theses, and Capstone Projects

Recent years have witnessed an explosion of work on Big Data. Data-intensive applications analyze and produce large volumes of data typically terabyte and petabyte in size. Many techniques for facilitating data processing are integrated into data-intensive applications. API is a software interface that allows two applications to communicate with each other. Streaming APIs are widely used in today's Object-Oriented programming development that can support parallel processing. In this dissertation, an approach that automatically suggests stream code run in parallel or sequentially is proposed. However, using streams efficiently and properly needs many subtle considerations. The use and misuse patterns for ...


Exploring The Relationship Between Intrinsic Motivation And Receptivity To Mhealth Interventions, Sarah Hong 2021 Dartmouth College

Exploring The Relationship Between Intrinsic Motivation And Receptivity To Mhealth Interventions, Sarah Hong

Dartmouth College Undergraduate Theses

Recent research in mHealth has shown the promise of Just-in-Time Adaptive Interventions (JITAIs). JITAIs aim to deliver the right type and amount of support at the right time. Choosing the right delivery time involves determining a user's state of receptivity, that is, the degree to which a user is willing to accept, process, and use the intervention provided.

Although past work on generic phone notifications has found evidence that users are more likely to respond to notifications with content they view as useful, there is no existing research on whether users' intrinsic motivation for the underlying topic of mHealth ...


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