Digital Commons Network^™

The Effects Of Deepfakes And Synthetic Media On Communication Professionals, Axel Ebermann

Student Theses and Dissertations

The spread of misinformation and disinformation is regarded as one of the most salient challenges for communication professionals around the world.

Now, a new technology called "DeepFakes" or “Synthetic Media” has the potential to further uproot the infosphere. Artificial Intelligence (AI) will enable the mass creation of synthetic audio-visual content that resembles real videos, in which people will appear to say or do things they never actually said or did.

The existing literature predominantly focuses on three aspects of DeepFakes and Synthetic Media: the technical background, coverage of preexisting DeepFakes and possible solutions to combat the malicious use of the …

Robotic Olfactory-Based Navigation With Mobile Robots, Lingxiao Wang

Doctoral Dissertations and Master's Theses

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods.

A. …

Predictive Computational Materials Modeling With Machine Learning: Creating The Next Generation Of Atomistic Potential Using Neural Networks, Mashroor Shafat Nitol

Theses and Dissertations

Machine learning techniques using artificial neural networks (ANNs) have proven to be effective tools to rapidly mimic first principles calculations. These tools are capable of sub meV/atom accuracy while operating with linear scaling with respect to the system size. Here novel interatomic potentials are constructed based on the rapid artificial neural network (RANN) formalism. This approach generates precise force fields for various metals that have historically been difficult to describe at the atomic scale. These force fields can be utilized in molecular dynamics simulations to provide new physical insights. The RANN formalism, which is incorporated into a LAMMPS molecular dynamics …

"A Novel And Controversial Technology." Artificial Face Recognition, Privacy Protection, And Algorithm Bias In Europe, Andrea Pin

William & Mary Bill of Rights Journal

R (Bridges) v. Chief Constable of South Wales Police's Court of Appeals ruling... showcases the variety and the thickness of the legal, ethical, and political considerations that lie underneath the deployment of [Artificial Face Recognition]-based police tools and its ramification within Europe and beyond. More broadly, the topic of "[f]acial recognition technologies provide[s] a useful case study of the complex and unpredictable ways that norms of procedural fairness, equality, and privacy interact when the state deploys machine-learning tools to draw inferences from otherwise unilluminating data." This Article uses Bridges as a proxy to sketch out the main legal issues …

Docket Selection And Judicial Responsiveness: The Use Of Ai In The Colombian Constitutional Court, Pablo Rueda Saiz

William & Mary Bill of Rights Journal

This Article addresses some of the limitations of AI as a tool to preselect a long or shortlist of cases for a court at the apex of the judicial system to review. It focuses on the Colombian Constitutional Court, as an example of a court at the apex of the judicial system that has been historically responsive to claims for fundamental rights. Docket selection is an example of a classification problem using supervised learning, in which a machine groups data according to preestablished characteristics.

This Article draws from two different bodies of literature to analyze the consequences of using AI …

Modeling Driver Distraction Mechanism And Its Safety Impact In Automated Vehicle Environment., Song Wang

Electronic Theses and Dissertations

Automated Vehicle (AV) technology expects to enhance driving safety by eliminating human errors. However, driver distraction still exists under automated driving. The Society of Automotive Engineers (SAE) has defined six levels of driving automation from Level 0~5. Until achieving Level 5, human drivers are still needed. Therefore, the Human-Vehicle Interaction (HVI) necessarily diverts a driver’s attention away from driving. Existing research mainly focused on quantifying distraction in human-operated vehicles rather than in the AV environment. It causes a lack of knowledge on how AV distraction can be detected, quantified, and understood. Moreover, existing research in exploring AV distraction has mainly …

Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.

How To Train Your Algo: Investigating The Enablers Of Bias In Algorithmic Development, Marta Stelmaszak Rosa

Business Faculty Publications and Presentations

Literature on algorithmic bias identifies its source in either biased data or statistical methods, more rarely in the development of algorithmic solutions as a potential factor. Because of the prior unknowability of algorithms, data scientists developing such solutions have to take various design decisions. Drawing from the flow-oriented approach, we study algorithmic unknowability and how data scientists respond to it in 35 public data science Jupyter notebooks containing algorithmic solutions to predict customer churn in a credit card dataset on a data science platform Kaggle.com. We offer a more thorough understanding of the unknowability in algorithmic development that can enable …

Using Big Data To Manage Social Inclusion Programs, Esther Raya Diez, Manuel Trujillo Carmona, Domingo Carbonero Muñoz

The Journal of Sociology & Social Welfare

Technological developments based on Artificial Intelligence (AI) and empirical science in all areas of society are opening new opportunities for social work and social inclusion programs. AI relies on Big Data management systems, which in turn provide opportunities for descriptive inference and preventative measures, as well as data-informed decision making.

This article outlines the characteristics of Big Data and describes the process of designing a tool for diagnosing social exclusion, the SiSo scale. The tool consists of a scale that uses 25 variables to assess situations of social difficulty on the inclusion-exclusion spectrum. It is currently being used in the …

Application Of Using Fault Detection Techniques In Different Components In Power Systems, Mohamed Yahia Abd-Alkader, Ahmed M. Ebid Dr., Ibrahim Mahdi, Ibrahim Abdelrashed Nosseir

Future Engineering Journal

Throughout the years, Fault detection has been one of the most frequently discussed topics in the scientific community at present. Consequently, it has been extensively studied and focused on in recent years. This scientific paper presents some of this research, what they have reached, and the methods used for their studies. The literature review provides that these problems are not covered yet. It needs more works to develop fault detection techniques for better performance.

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 …

Exploratory Data Mining Techniques (Decision Tree Models) For Examining The Impact Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Machine Learning Approach, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah

School of Mathematical & Statistical Sciences Faculty Publications

Background: There is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment …

The Ratio Method: Addressing Complex Tort Liability In The Fourth Industrial Revolution, Harrison C. Margolin, Grant H. Frazier

St. Mary's Law Journal

Emerging technologies of the Fourth Industrial Revolution show fundamental promise for improving productivity and quality of life, though their misuse may also cause significant social disruption. For example, while artificial intelligence will be used to accelerate society’s processes, it may also displace millions of workers and arm cybercriminals with increasingly powerful hacking capabilities. Similarly, human gene editing shows promise for curing numerous diseases, but also raises significant concerns about adverse health consequences related to the corruption of human and pathogenic genomes.

In most instances, only specialists understand the growing intricacies of these novel technologies. As the complexity and speed of …

Scientific, Legal, And Ethical Concerns About Ai-Based Personnel Selection Tools: A Call To Action, Nancy T. Tippins, Frederick L. Oswald, S. Morton Mcphail

Personnel Assessment and Decisions

Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms of their …

Check Your Tech - The Ethics Of Deepfakes In A Political Context, Dympna O'Sullivan, Damian Gordon, Ioannis Stavrakakis, Michael Collins

Conference papers

No abstract provided.

Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University

Perspectives@SMU

Artificial intelligence presents significant opportunities for business – as well as not insignificant threats to humanity – and governance frameworks are urgently needed to create a fair and equitable future under AI

Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe

Research Collection School Of Computing and Information Systems

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance …

Deep Fakes: The Algorithms That Create And Detect Them And The National Security Risks They Pose, Nick Dunard

James Madison Undergraduate Research Journal (JMURJ)

The dissemination of deep fakes for nefarious purposes poses significant national security risks to the United States, requiring an urgent development of technologies to detect their use and strategies to mitigate their effects. Deep fakes are images and videos created by or with the assistance of AI algorithms in which a person’s likeness, actions, or words have been replaced by someone else’s to deceive an audience. Often created with the help of generative adversarial networks, deep fakes can be used to blackmail, harass, exploit, and intimidate individuals and businesses; in large-scale disinformation campaigns, they can incite political tensions around the …

How ‘Human’ Should Robots Be?, Singapore Management University

Perspectives@SMU

Hotel guests like interaction with devices that look and sound like them, but they can spark displeasure after service failures, new CUHK study shows

Impossibility Results In Ai: A Survey, Mario Brcic, Roman Yampolskiy

Faculty and Staff Scholarship

An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. In this paper, we have categorized impossibility theorems applicable to the domain of AI into five categories: deduction, indistinguishability, induction, …

The Objective Function: Science And Society In The Age Of Machine Intelligence, Emanuel D. Moss

Dissertations, Theses, and Capstone Projects

Machine intelligence, or the use of complex computational and statistical practices to make predictions and classifications based on data representations of phenomena, has been applied to domains as disparate as criminal justice, commerce, medicine, media and the arts, mechanical engineering, among others. How has machine intelligence become able to glide so freely across, and to make such waves for, these domains? In this dissertation, I take up that question by ethnographically engaging with how the authority of machine learning has been constructed such that it can influence so many domains, and I investigate what the consequences are of it being …

Artificial Intelligence And Work: Two Perspectives, Steven Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

One of the most important issues in contemporary societies is the impact of intelligent technologies on human work. For an empirical perspective on the issue, we recently completed 30 case studies of people collaborating with AI-enabled smart machines. Twenty-four were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. We compare some of our observations to one of the broadest academic examinations of the issue. In particular, we focus on our case study observations with regard to key findings from the MIT Task Force on the Work of the Future report.

Characterizing Convolutional Neural Network Early-Learning And Accelerating Non-Adaptive, First-Order Methods With Localized Lagrangian Restricted Memory Level Bundling, Benjamin O. Morris

Theses and Dissertations

This dissertation studies the underlying optimization problem encountered during the early-learning stages of convolutional neural networks and introduces a training algorithm competitive with existing state-of-the-art methods. First, a Design of Experiments method is introduced to systematically measure empirical second-order Lipschitz upper bound and region size estimates for local regions of convolutional neural network loss surfaces experienced during the early-learning stages. This method demonstrates that architecture choices can significantly impact the local loss surfaces traversed during training. Next, a Design of Experiments method is used to study the effects convolutional neural network architecture hyperparameters have on different optimization routines' abilities to …

Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi

Dissertations

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions.

First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule …

Predicting Human Behavior In Repeated Games With Attitude Vectors, Brian L. James

Theses and Dissertations

As Artificial Intelligence systems are used by human users at an increasing frequency, the need for such systems to understand and predict human behavior likewise increases. In my work, I have considered how to predict human behavior in repeated games. These repeated games can be applied as a foundation to many situations where a person may interact with an AI, In an attempt to create such a foundation, I have built a system using Attitude Vectors used in automata to predict actions based on prior actions and communications. These Attitude Vector Automata (AVA) can transform information from actions in one …

Innovative Computational Methods For Pharmaceutical Problem Solving A Review Part I: The Drug Development Process, Heather R. Campbell, Robert A. Lodder

Pharmaceutical Sciences Faculty Publications

Computational methods have provided pharmaceutical scientists and engineers a means to go beyond what's possible with experimental testing alone. Providing a means to study active pharmaceutical ingredients (API), excipients, and drug interactions at or near-atomic levels. This paper provides a review of this and other innovative computational methods used for solving pharmaceutical problems throughout the drug development process. Part one of two this paper will emphasize the role of computational methods and game theory in solving pharmaceutical challenges.

Application Of Artificial Intelligence And Machine Learning In Libraries: A Systematic Review, Rajesh Kumar Das, Mohammad Sharif Ul Islam

Library Philosophy and Practice (e-journal)

As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area. The objective of this systematic literature review is to provide a synthesis of empirical studies exploring application of artificial intelligence and machine learning in libraries. To achieve the objectives of the study, a systematic literature review was conducted based on the original guidelines proposed by Kitchenham et al. (2009). Data was collected from Web of Science, Scopus, LISA and LISTA databases. Following the rigorous/ established selection process, a total of thirty-two articles were …

Deep Reinforcement Learning With Accelerated Reward Function Technique For Robotics Task Planning, Shifabanu Mohammed Rafiq Shaikh

Master's Theses

The objectives of this research are to investigate, design, develop, and implement a DRL-based reward function enhanced robotics technique to achieve ”human-like” operations for a six DoF robot. Technical challenges for robotics applications arise nowadays with a need to achieve human-like intelligence. DRL can formulate human-likeintelligence by maximizing a long-term reward function. However, the design of states, actions, policies, and reward functions having continuous values turns out to be quite tricky. Lastly, to reach a target position, control motion behavior without calculating inverse kinematics, and update state space for actions and policy optimizations are also challenging. Inspired by Google Deepmind’s …

Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Harris, Martin Zwick

Complex Systems Faculty Publications and Presentations

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and …

Dealing With Classification Irregularities In Real-World Scenarios., Payel Sadhukhan Dr.

Doctoral Theses

Data processing by the human sensory system comes naturally. This processing, commonly denoted as pattern recognition and analysis are carried out spontaneously by humans. In day to day life, in most cases, decision making by humans come without any conscious effort. From the middle of the past century, humans have shown interest to render their abstraction capabilities (pattern recognition and analysis) to the machine. The abstraction capability of the machine is ’machine intelligence’ or ’machine learning’ [87].The primary goal of machine learning methods is to extract some meaningful information from the ’data’. Data refers to the information or attributes that …