Open Access. Powered by Scholars. Published by Universities.®

Computer Sciences Commons™

Open Access. Powered by Scholars. Published by Universities.®

2021

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 421 - 450 of 3475

Full-Text Articles in Computer Sciences

Managing Incomplete Data In The Patient Discharge Summary To Support Correct Hospital Reimbursements, Fadi Naser Eddin Nov 2021

Managing Incomplete Data In The Patient Discharge Summary To Support Correct Hospital Reimbursements, Fadi Naser Eddin

USF Tampa Graduate Theses and Dissertations

The patient discharge summary is a document that conveys the patient's story to other healthcare practitioners, external users, and, most importantly from a financial perspective, health insurers. A defect or incompleteness in the patient's discharge summary will result in delays in the collection process through denial of the entire or partial reimbursement claim or, in the best-case scenario, delay until the discharge summary issue is resolved. The purpose of this project is to address the issue of the incompleteness of discharge summary from the perspective of healthcare providers, with the goal of understanding, diagnosing, and intervening in the research problem. …

Go to article

Learning From Mistakes - A Framework For Neural Architecture Search, Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric P. Xing, Pengtao Xie Nov 2021

Learning From Mistakes - A Framework For Neural Architecture Search, Bhanu Garg, Li Zhang, Pradyumna Sridhara, Ramtin Hosseini, Eric P. Xing, Pengtao Xie

Machine Learning Faculty Publications

Learning from one's mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In this paper, we investigate if this human learning strategy can be applied in machine learning. We propose a novel machine learning method called Learning From Mistakes (LFM), wherein the learner improves its ability to learn by focusing more on the mistakes during revision. We formulate LFM as a three-stage optimization problem: 1) learner learns; 2) learner re-learns focusing on the mistakes, and; 3) learner validates its learning. We develop an efficient …

Go to article

The Maritime Domain Awareness Center– A Human-Centered Design Approach, Gary Gomez Nov 2021

The Maritime Domain Awareness Center– A Human-Centered Design Approach, Gary Gomez

Political Science & Geography Faculty Publications

This paper contends that Maritime Domain Awareness Center (MDAC) design should be a holistic approach integrating established knowledge about human factors, decision making, cognitive tasks, complexity science, and human information interaction. The design effort should not be primarily a technology effort that focuses on computer screens, information feeds, display technologies, or user interfaces. The existence of a room with access to vast amounts of information and wall-to-wall video screens of ships, aircraft, weather data, and other regional information does not necessarily correlate to possessing situation awareness. Fundamental principles of human-centered information design should guide MDAC design and technology selection, and …

Go to article

Fighting Mass Diffusion Of Fake News On Social Media, Abdallah Musmar Nov 2021

Fighting Mass Diffusion Of Fake News On Social Media, Abdallah Musmar

USF Tampa Graduate Theses and Dissertations

Fake news has been considered one of the most challenging problems in the last few years. The effects of spreading fake news over social media platforms are widely observed across the globe as the depth and velocity of fake news reach far more than real news (Vosoughi et al., 2018). The plan for the following dissertation is to investigate the mass spread of fake news across social media and propose a framework to fight the spread of fake news by mixing preventive methods that could hinder the overall percentage of fake news sharing. We plan to create a study on …

Go to article

Machine Learning In Apache Spark Environment For Diagnosis Of Diabetes, Farshid Bagheri Saravi Nov 2021

Machine Learning In Apache Spark Environment For Diagnosis Of Diabetes, Farshid Bagheri Saravi

Student Scholarship

Disease-related data and information collected by physicians, patients, and researchers seem insignificant at first glance. Still, the same unorganized data contain valuable information that is often hidden. The task of data mining techniques is to extract patterns to classify the data accurately. One of the various Data mining and its methods have been used often to diagnose various diseases. In this study, a machine learning (ML) technique based on distributed computing in the Apache Spark computing space is used to diagnose diabetics or hidden pattern of the illness to detect the disease using a large dataset in real-time. Implementation results …

Go to article

System Design And Optimization For Efficient Flash-Based Caching In Data Centers, Jian Liu Nov 2021

System Design And Optimization For Efficient Flash-Based Caching In Data Centers, Jian Liu

LSU Doctoral Dissertations

Modern data centers are the backbone of today’s Internet-based services and applications. With the explosive growth of the Internet data and a wider range of data-intensive applications being deployed, it is increasingly challenging for data centers to satisfy the ever-increasing demand for high-quality data services. To relieve the heavy burden on data center systems and accelerate data processing, a popular cost-efficient solution is to deploy high-speed, large-capacity flash-based cache systems. However, we are facing multiple critical challenges from device hardware, systems, to application workloads. In this dissertation, we focus on designing highly efficient caching solutions to cope with the explosive …

Go to article

Don't Bite The Bait: Phishing Attack For Internet Banking (E-Banking), Ilker Kara Nov 2021

Don't Bite The Bait: Phishing Attack For Internet Banking (E-Banking), Ilker Kara

Journal of Digital Forensics, Security and Law

Phishing attacks are based on obtaining desired information from users quickly and easily with the help of misdirecting, panicking, curiosity, or excitement. Most of the phishing web sites are designed on internet banking(e-banking) and the attackers can acquire financial information of misled users with the tactics and discourses they develop. Despite the increase of prevention techniques against phishing attacks day by day, an effective solution could not be found for this issue due to the human factor. Because of this reason, real phishing attack studies are essential to study and analyze the attackers’ attack techniques and strategies. This study focused …

Go to article

Misconfiguration In Firewalls And Network Access Controls: Literature Review, Michael Alicea, Izzat Alsmadi Nov 2021

Misconfiguration In Firewalls And Network Access Controls: Literature Review, Michael Alicea, Izzat Alsmadi

Computer Information Systems Faculty Publications (Archived)

Firewalls and network access controls play important roles in security control and protection. Those firewalls may create an incorrect sense or state of protection if they are improperly configured. One of the major configuration problems in firewalls is related to misconfiguration in the access control roles added to the firewall that will control network traffic. In this paper, we evaluated recent research trends and open challenges related to firewalls and access controls in general and misconfiguration problems in particular. With the recent advances in next-generation (NG) firewalls, firewall roles can be auto-generated based on networks and threats. Nonetheless, and due …

Go to article

Towards A Framework For Comparing Functionalities Of Multimorbidity Clinical Decision Support: A Literature-Based Feature Set And Benchmark Cases., Dympna O'Sullivan, William Van Woensel, Szymon Wilk, Samson Tu, Wojtek Michalowski, Samina Abidi, Marc Carrier, Ruth Edry, Irit Hochberg, Stephen Kingwell, Alexandra Kogan, Martin Michalowski, Hugh O'Sullivan, Mor Peleg Nov 2021

Towards A Framework For Comparing Functionalities Of Multimorbidity Clinical Decision Support: A Literature-Based Feature Set And Benchmark Cases., Dympna O'Sullivan, William Van Woensel, Szymon Wilk, Samson Tu, Wojtek Michalowski, Samina Abidi, Marc Carrier, Ruth Edry, Irit Hochberg, Stephen Kingwell, Alexandra Kogan, Martin Michalowski, Hugh O'Sullivan, Mor Peleg

Articles

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present …

Go to article

Human-Centric Cybersecurity Research: From Trapping The Bad Guys To Helping The Good Ones, Armin Ziaie Tabari Nov 2021

Human-Centric Cybersecurity Research: From Trapping The Bad Guys To Helping The Good Ones, Armin Ziaie Tabari

USF Tampa Graduate Theses and Dissertations

The issue of cybersecurity has become much more prevalent over the last few years, with a number of widely publicised incidents, hacking attempts, and data breaches reaching the news. There is no sign of an abatement in the number of cyber incidents, and it would be wise to reconsider the way cybersecurity is viewed and whether a mindset shift is necessary. Cybersecurity, in general, can be seen as primarily a human problem, and it is for this reason that it requires human solutions and tradeoffs. In order to study this problem, using two perspectives; that of the adversaries and that …

Go to article

Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen Nov 2021

Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen

Mathematics, Physics, and Computer Science Faculty Articles and Research

During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky …

Go to article

Machine Learning For Species Habitat Analysis, Abigail Lavallin Nov 2021

Machine Learning For Species Habitat Analysis, Abigail Lavallin

USF Tampa Graduate Theses and Dissertations

Management and conservation initiatives will always be controlled by finite resources, whether financialor temporal. Understanding a species’ spatial ecology, and how its requirements vary across habitats and locations is key to a successful species management plan. During recent decades, it has been noted how many species populations have declined, despite conservation practices working to increase their numbers. The most prevalent impacts affecting fauna populations have come from anthropogenic change in the form of habitat loss and destruction, along with fragmentation, and global climate change. There is a clear need for management practices to now operate on an entire landscape instead …

Go to article

Multi-Task Learning Of Order-Consistent Causal Graphs, Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song Nov 2021

Multi-Task Learning Of Order-Consistent Causal Graphs, Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song

Machine Learning Faculty Publications

We consider the problem of discovering K related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports. Under the multi-task learning setting, we propose a l1/l2regularized maximum likelihood estimator (MLE) for learning K linear structural equation models. We theoretically show that the joint estimator, by leveraging data across related tasks, can achieve a better sample complexity for recovering the causal order (or topological order) than separate estimations. Moreover, the joint estimator is able to recover non-identifiable DAGs, by estimating them together with some identifiable DAGs. Lastly, our analysis also …

Go to article

A Quantitative Evaluation Of Global, Rule-Based Explanations Of Post-Hoc, Model Agnostic Methods, Giulia Vilone, Luca Longo Nov 2021

A Quantitative Evaluation Of Global, Rule-Based Explanations Of Post-Hoc, Model Agnostic Methods, Giulia Vilone, Luca Longo

Articles

Understanding the inferences of data-driven, machine-learned models can be seen as a process that discloses the relationships between their input and output. These relationships consist and can be represented as a set of inference rules. However, the models usually do not explicit these rules to their end-users who, subsequently, perceive them as black-boxes and might not trust their predictions. Therefore, scholars have proposed several methods for extracting rules from data-driven machine-learned models to explain their logic. However, limited work exists on the evaluation and comparison of these methods. This study proposes a novel comparative approach to evaluate and compare the …

Go to article

Predicting The Adoption Of Password Managers: A Tale Of Two Samples, Shelia Kennison, D. Eric Chan-Tin Nov 2021

Predicting The Adoption Of Password Managers: A Tale Of Two Samples, Shelia Kennison, D. Eric Chan-Tin

Computer Science: Faculty Publications and Other Works

Using weak passwords and re-using passwords can make one vulnerable to cybersecurity breaches. Cybersecurity experts recommend the adoption of password managers (PMs), as they generate and store strong passwords for all accounts. Prior research has shown that few people adopt PMs. Our research examined PM adoption in a sample of 221 undergraduates from psychology courses and a sample of 278 MTurk workers. We hypothesized that PM adoption could be predicted using a small set of user characteristics (i.e., gender, age, Big Five personality traits, number of devices used, frequency of using social media, and cybersecurity knowledge). The results showed that …

Go to article

From Mdp To Alphazero, David Robert Sewell Nov 2021

From Mdp To Alphazero, David Robert Sewell

Dissertations and Theses

In this paper I will explain the AlphaGo family of algorithms starting from first principles and requiring little previous knowledge from the reader. The focus will be upon one of the more recent versions AlphaZero but I hope to explain the core principles that allowed these algorithms to be so successful. I will generally refer to AlphaZero as theses [sic] core set of principles and will make it clear when I am referring to a specific algorithm of the AlphaGo family. AlphaZero in short combines Monte Carlo Tree Search (MCTS) with Deep learning and self-play. We will see how these …

Go to article

Improving Accurate Candidates For Missing Data Using Benefit Performance Of (Ml-Som), Abeer Abdullah Al-Mohdar, Mohamed Abdullah Bamatraf Nov 2021

Improving Accurate Candidates For Missing Data Using Benefit Performance Of (Ml-Som), Abeer Abdullah Al-Mohdar, Mohamed Abdullah Bamatraf

Hadhramout University Journal of Natural & Applied Sciences

Missing data is one of the major challenges in extracting and analyzing knowledge from datasets. The performance of training quality was affected by the appearance of missing data in a dataset. For this reason, there is a need for a quick and reliable method to find possible solutions in order to provide an accurate system. Therefore, the previous studies provided robust ability of Self Organizing Map (SOM) algorithm to deal with the missing values [6, 20]. However, it has a drawback such as an error rate(ERR) in the missing values that increase huge dataset. This study is mainly based on …

Go to article

Why Moments (And Generalized Moments) Are Used In Statistics And Why Expected Utility Is Used In Decision Making: A Possible Explanation, R. Noah Padilla, Vladik Kreinovich Nov 2021

Why Moments (And Generalized Moments) Are Used In Statistics And Why Expected Utility Is Used In Decision Making: A Possible Explanation, R. Noah Padilla, Vladik Kreinovich

Departmental Technical Reports (CS)

Among the most efficient characteristics of a probability distribution are its moments and, more generally, generalized moments. One of the most adequate numerical characteristics describing human behavior is expected utility. In both cases, the corresponding characteristic is the sum of results of applying appropriate nonlinear functions applied to individual inputs. In this paper, we provide a possible theoretical explanation of why such functions are efficient.

Go to article

Notmad: Estimating Bayesian Networks With Sample-Specific Structures And Parameters, Benjamin Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis Nov 2021

Notmad: Estimating Bayesian Networks With Sample-Specific Structures And Parameters, Benjamin Lengerich, Caleb Ellington, Bryon Aragam, Eric P. Xing, Manolis Kellis

Machine Learning Faculty Publications

Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity requirement makes it difficult to share information between context-specific estimators (e.g. with graph generator functions). For this reason, existing methods for inferring context-specific Bayesian networks have favored breaking datasets into subsamples, limiting statistical power and resolution, and preventing the use of multidimensional and latent contexts. To overcome this challenge, we propose NOTEARS-optimized Mixtures of Archetypal DAGs (NOTMAD). NOTMAD models context-specific Bayesian networks as the output of a function which learns to mix archetypal networks according to sample context. The archetypal …

Go to article

How Multi-View Techniques Can Help In Processing Uncertainty, Olga Kosheleva, Vladik Kreinovich Nov 2021

How Multi-View Techniques Can Help In Processing Uncertainty, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Multi-view techniques help us reconstruct a 3-D object and its properties from its 2-D (or even 1-D) projections. It turns out that similar techniques can be used in processing uncertainty -- where many problems can reduced to a similar task of reconstructing properties of a multi-D object from its 1-D projections. In this chapter, we provide an overview of these techniques.

Go to article

Why Do People Become Addicted: Towards A Theoretical Explanation For Eyal's Experiment-Based Hook Model, Christopher Reyes, Vladik Kreinovich Nov 2021

Why Do People Become Addicted: Towards A Theoretical Explanation For Eyal's Experiment-Based Hook Model, Christopher Reyes, Vladik Kreinovich

Departmental Technical Reports (CS)

Why do people become addicted, e.g., to gambling? Experiments have shown that simple lotteries, in which we can win a small prize with a certain probability, and not addictive. However, if we add a second possibility -- of having a large prize with a small probability -- the lottery becomes highly addictive to many participants. In this paper, we provide a possible theoretical explanation for this empirical phenomenon.

Go to article

Why Ovals In Eliciting Intervals?, Joshua Zamora, Vladik Kreinovich Nov 2021

Why Ovals In Eliciting Intervals?, Joshua Zamora, Vladik Kreinovich

Departmental Technical Reports (CS)

To elicit people's opinions, we usually ask them to mark their degree of satisfaction on a scale -- e.g., from 0 to 5 or from 0 to 10. Often, people are unsure about the exact degree: 7 or 8? To cover such situations, it is desirable to elicit not a single value but an interval of possible values. However, it turns out that most people are not comfortable with marking an interval. Empirically, it turned out that the best way to elicit an interval is to ask them to draw an oval whose intersection with the 0-to-10 line is the …

Go to article

Decision Making Under Uncertainty: Cases When We Only Know An Upper Bound Or A Lower Bound, Toshiki Kamio, Gavin Baechle, Vladik Kreinovich Nov 2021

Decision Making Under Uncertainty: Cases When We Only Know An Upper Bound Or A Lower Bound, Toshiki Kamio, Gavin Baechle, Vladik Kreinovich

Departmental Technical Reports (CS)

In situations when we have a perfect knowledge about the outcomes of several situations, a natural idea is to select the best of these situations. For example, among different investments, we should select the one with the largest gain. In practice, however, we rarely know the exact consequences of each action. In some cases, we know the lower and upper bounds on the corresponding gain. It has been proven that in such cases, an appropriate decision is to use Hurwicz optimism-pessimism criterion. In this paper, we extend the corresponding results to the cases when we only know an upper bound …

Go to article

Commonsense "And"-Operations, Javier Tellez, Wenbo Xie, Vladik Kreinovich Nov 2021

Commonsense "And"-Operations, Javier Tellez, Wenbo Xie, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical situations, we need to estimate our degree of belief in a statement "A and B" when the only thing we know are the degrees of belief a and b in combined statements A and B. An algorithm for this estimation is known as an "and"-operation, or, for historical reasons, a t-norm. Usually, "and"-operations are selected in such a way that if one of the statements A or B is false, our degree of belief in "A and B" is 0. However, in practice, this is sometimes not the case: for example, an ideal faculty candidate must satisfy …

Go to article

Fourier Transform And Other Quadratic Problems Under Interval Uncertainty, Oscar Galindo, Christopher Ibarra, Vladik Kreinovich Nov 2021

Fourier Transform And Other Quadratic Problems Under Interval Uncertainty, Oscar Galindo, Christopher Ibarra, Vladik Kreinovich

Departmental Technical Reports (CS)

In general, computing the range of a quadratic function on given intervals is NP-hard. Recently, a feasible algorithm was proposed for computing the range of a specific quadratic function -- square of the modulus of a Fourier coefficient. For this function, the rank of the quadratic form -- i.e., the number of nonzero eigenvalues -- is 2. In this paper, we show that this algorithm can be extended to all the cases when the rank of the quadratic form is bounded by a constant.

Go to article

Why Model Order Reduction, Salvador Robles, Martine Ceberio, Vladik Kreinovich Nov 2021

Why Model Order Reduction, Salvador Robles, Martine Ceberio, Vladik Kreinovich

Departmental Technical Reports (CS)

Reasonably recently, a new efficient method appeared for solving complex non-linear differential equations (and systems of differential equations). In this method -- known as Model Order Reduction (MOR) -- we select several solutions, and approximate a general solution by a linear combination of the selected solutions. In this paper, we use the known explanation for efficiency of neural networks to explain the efficiency of MOR techniques.

Go to article

Why Residual Neural Networks, Sofia Holguin, Vladik Kreinovich Nov 2021

Why Residual Neural Networks, Sofia Holguin, Vladik Kreinovich

Departmental Technical Reports (CS)

In the traditional neural networks, the outputs of each layer serve as inputs to the next layer. It is known that in many cases, it is beneficial to also allow outputs from pre-previous etc. layers as inputs. Such networks are known as residual. In this paper, we provide a possible theoretical explanation for the empirical success of residual neural networks.

Go to article

How To Gauge The Quality Of A Multi-Class Classification When Ground Truth Is Known With Uncertainty, Ricardo Mendez, Osagumwenro Osaretin, Vladik Kreinovich Nov 2021

How To Gauge The Quality Of A Multi-Class Classification When Ground Truth Is Known With Uncertainty, Ricardo Mendez, Osagumwenro Osaretin, Vladik Kreinovich

Departmental Technical Reports (CS)

The usual formulas for gauging the quality of a classification method assume that we know the ground truth, i.e., that for several objects, we know for sure to which class they belong. In practice, we often only know this with some degree of certainty. In this paper, we explain how to take this uncertainty into account when gauging the quality of a classification method.

Go to article

Kinematic Metric Spaces Under Interval Uncertainty: Towards An Adequate Definition, Vladik Kreinovich, Olga Kosheleva, Victor Selivanov Nov 2021

Kinematic Metric Spaces Under Interval Uncertainty: Towards An Adequate Definition, Vladik Kreinovich, Olga Kosheleva, Victor Selivanov

Departmental Technical Reports (CS)

In the physical space, we define distance between the two points as the length of the shortest path connecting these points. Similarly, in space-time, for every pair of events for which the event a can causally effect the event b, we can define the longest proper time t(a,b) over all causal trajectories leading from a to b. The resulting function is known as kinematic metric. In practice, our information about all physical quantities -- including time -- comes from measurement, and measurements are never absolutely precise: the measurement result V is, in general, different from the actual (unknown) value v …

Go to article

Fuzzy Logic Beyond Traditional "And"- And "Or"-Operations, Vladik Kreinovich, Olga Kosheleva Nov 2021

Fuzzy Logic Beyond Traditional "And"- And "Or"-Operations, Vladik Kreinovich, Olga Kosheleva

Departmental Technical Reports (CS)

In the traditional fuzzy logic, we can use "and"-operations (also known as t-norms) to estimate the expert's degree of confidence in a composite statement A&B based on his/her degrees of confidence d(A) and d(B) in the corresponding basic statements A and B. But what if we want to estimate the degree of confidence in A&B&C in situations when, in addition to the degrees of estimate d(A), d(B), and d(C) of the basic statements, we also know the expert's degrees of confidence in the pairs d(A&B), d(A&C), and d(B&C)? Traditional "and"-operations can provide such an estimate -- but only by ignoring …

Go to article

First Prev Next Last