Computer Sciences Commons^™

Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver

Dissertations

Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between …

Performance Comparison Between A Distributed Particle Swarm Algorithm And A Centralised Algorithm, Ciarán O’Loughlin

Dissertations

Particle Swarm optimisation (PSO) is a particular form of swarm intelligence, which itself is an innovative intelligent paradigm for solving optimization problems. PSO is generally used to find a global optimum in a single optimisation function. This typically occurs on one node(machine) but there has been a significant body of research into creating distributed implementations of the PSO algorithm. Such research has often focused on the creation and performance of the distributed implementation in an isolated manner or compared to different distributed algorithms.

This research piece aims to bridge a gap in the existing literature, by testing a distributed implementation …

Efficientnet-Lite And Hybrid Cnn-Knn Implementation For Facial Expression Recognition On Raspberry Pi, Mohd Nadhir Ab Wahab, Anthony Tan Zhen Ren, Amril Nazir, Mohd Halim Mohd Noor, Muhammad Firdaus Akbar, Ahmad Sufril Azlan Mohamed

All Works

Facial expression recognition (FER) is the task of determining a person’s current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system’s accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER’s accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the …

Voice Impersonation For Thai Speech Using Cyclegan Over Prosody, Chatri Chuanngulueam

Chulalongkorn University Theses and Dissertations (Chula ETD)

No abstract provided.

Integrating The First Person View And The Third Person View Using A Connected Vr-Mr System For Pilot Training, Chang-Geun Oh, Kwanghee Lee, Myunghoon Oh

Journal of Aviation/Aerospace Education & Research

Virtual reality (VR)-based flight simulator provides pilots the enhanced reality from the first-person view. Mixed reality (MR) technology generates effective 3D graphics. The users who wear the MR headset can walk around the 3D graphics to see all its 360 degrees of vertical and horizontal aspects maintaining the consciousness of real space. A VR flight simulator and an MR application were connected to create the capability of both first-person view and third-person view for a comprehensive pilot training system. This system provided users the capability to monitor the aircraft progress along the planned path from the third-person view as well …

Law Library Blog (January 2021): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

Forensic Analysis Of Tor Browser On Windows 10 And Android 10 Operating Systems, Muhammad Raheel Arshad, Mehdi Hussain, Hasan Tahir, Sana Qadir, Faraz Iqbal Ahmed Memon, Yousra Javed

Faculty Publications – Technology

Smartphones and Internet have become prevalent in our society with various applications in businesses, education, healthcare, gaming, and research. One of the major issues with the Internet today is its lack of security since an eavesdropper can potentially intercept the communication. This has contributed towards an increased number of cyber-crime incidents, resulting in an increase in users’ consciousness about the security and privacy of their communication . One example is the shift towards using private browsers such as Tor. Tor is a well-recognized and widely used privacy browser based on The Onion Router network that provisions anonymity over the insecure …

Quality Of Sql Code Security On Stackoverflow And Methods Of Prevention, Robert Klock

Honors Papers

This paper explores the frequency at which SQL/PHP posts on the website Stackoverflow.com contain code susceptible to SQL Injection, a common database vulnerability. Specifically, we analyze whether other users give notice of the vulnerability or provide an answer that is secure. The majority of questions analyzed were vulnerable to SQL Injection and were not corrected in their answers or brought to the attention of the original poster. To mitigate this, we present a machine learning bot which analyzes the poster’s code and alerts them of potential injection vulnerabilities, if necessary.

Increasing The Value Of Information During Planning In Uncertain Environments, Gaurab Pokharel

Honors Papers

Prior studies have demonstrated that for many real-world problems, POMDPs can be solved through online algorithms both quickly and with near optimality [10, 8, 6]. However, on an important set of problems where there is a large time delay between when the agent can gather information and when it needs to use that information, these solutions fail to adequately consider the value of information. As a result, information gathering actions, even when they are critical in the optimal policy, will be ignored by existing solutions, leading to sub-optimal decisions by the agent. In this research, we develop a novel solution …

The Mathematics Of Mutual Aid: Robust Welfare Guarantees For Decentralized Financial Organizations, Christian Ikeokwu

Honors Papers

Mutual aid groups often serve as informal financial organizations that don’t rely on any central authority or legal framework to resolve disputes. Rotating savings and credit associations (roscas) are informal financial organizations common in settings where communities have reduced access to formal financial institutions. In a Rosca, a fixed group of participants regularly contribute small sums of money to a pool. This pool is then allocated periodically typically using lotteries or auction mechanisms. Roscas are empirically well-studied in the development economics literature. Due to their dynamic nature, however, roscas have proven challenging to examine theoretically. Theoretical analyses within economics have …

Uncovering Object Categories In Infant Views, Naiti S. Bhatt

Scripps Senior Theses

While adults recognize objects in a near-instant, infants must learn how to categorize the objects in their visual environments. Recent work has shown that egocentric head-mounted camera videos contain rich data that illuminate the infant experience (Clerkin et al., 2017; Franchak et al., 2011; Yoshida & Smith, 2008). While past work has focused on the social information in view, in this work, we aim to characterize the objects in infants’ at-home visual environments by modifying modern computer vision models for the infant view. To do so, we collected manual annotations of objects that infants seemed to be interacting within a …

An Open-Publishing Response To The Covid-19 Infodemic, Halie M. Rando, Simina M. Boca, Lucy D.Agostino Mcgowan, Daniel S. Himmelstein, Michael P. Robson, Vincent Rubinetti, Ryan Velazquez, Casey S. Greene, Anthony Gitter

Computer Science: Faculty Publications

The COVID-19 pandemic catalyzed the rapid dissemination of papers and preprints investigating the disease and its associated virus, SARS-CoV-2. The multifaceted nature of COVID-19 demands a multidisciplinary approach, but the urgency of the crisis combined with the need for social distancing measures present unique challenges to collaborative science. We applied a massive online open publishing approach to this problem using Manubot. Through GitHub, collaborators summarized and critiqued COVID-19 literature, creating a review manuscript. Manubot automatically compiled citation information for referenced preprints, journal publications, websites, and clinical trials. Continuous integration workflows retrieved up-to-date data from online sources nightly, regenerating some of …

Adoption Of Artificial Intelligence (Ai) In Local Governments: An Exploratory Study On The Attitudes And Perceptions Of Officials In A Municipal Government In The Philippines, Charmaine B. Distor, Odkhuu Khaltar, M. Jae Moon

Journal of Public Affairs and Development

Emerging technologies like artificial intelligence (AI) have been instrumental in transforming governments in recent years, which is why several agencies worldwide have integrated them into their governance strategies. One of the countries that have paid attention to the potential of AI is the Philippines, which launched its national AI roadmap in 2021. This study investigated the perceived acceptance and adoption of AI in the Municipality of Carmona located in the Province of Cavite. Following the combined constructs from the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), perception data were gathered from among Carmona’s …

Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi

Theses and Dissertations

Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily fine-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the first place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be …

Perceptually Improved Medical Image Translations Using Conditional Generative Adversarial Networks, Anurag Vaidya

Honors Theses

Magnetic resonance imaging (MRI) can help visualize various brain regions. Typical MRI sequences consist of T1-weighted sequence (favorable for observing large brain structures), T2-weighted sequence (useful for pathology), and T2-FLAIR scan (useful for pathology with suppression of signal from water). While these different scans provide complementary information, acquiring them leads to acquisition times of ~1 hour and an average cost of $2,600, presenting significant barriers. To reduce these costs associated with brain MRIs, we present pTransGAN, a generative adversarial network capable of translating both healthy and unhealthy T1 scans into T2 scans. We show that the addition of non-adversarial …

Pb-Acr: Node Payload Balanced Ant Colony Optimal Cooperative Routing For Multi-Hop Underwater Acoustic Sensor Networks, Yougan Chen, Yuying Tang, Xing Fang, Lei Wan, Yi Tao, Xiaomei Xu

Faculty Publications - Information Technology

For a given source-destination pair in multi-hop underwater acoustic sensor networks (UASNs), an optimal route is the one with the lowest energy consumptions that usually consists of the same relay nodes even under different transmission tasks. However, this will lead to the unbalanced payload of the relay nodes in the multi-hop UASNs and accelerate the loss of the working ability for the entire system. In this paper, we propose a node payload balanced ant colony optimal cooperative routing (PB-ACR) protocol for multi-hop UASNs, through combining the ant colony algorithm and cooperative transmission. The proposed PB-ACR protocol is a relay node …

Towards Enabling Explanation In Safety-Critical Artificial Intelligence Systems, Andy Michel

Electronic Theses and Dissertations, 2020-2023

With the advancement of accelerated hardware in recent years, there has been a surge in the development and application of intelligent systems. Deep learning systems, in particular, have shown exciting results in a wide range of tasks: classification, detection, and recognition. Despite these remarkable achievements, there remains an active research area that aims to increase the robustness of those systems in critical domains. Deep learning algorithms have proven to be brittle against adversarial attacks. That is, carefully crafted adversarial inputs can consistently trigger an erroneous prediction from a network model. Hence the motivation of this dissertation, we study prominent adversarial …

การพยากรณ์ปริมาณและความเข้มข้นสารฟลอกคูแลต์ในกระบวนการพักใสสำหรับอุตสาหกรรมการผลิตน้ำตาลจากอ้อย, สิงหดิศร์ จันทรักษ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

กระบวนการพักใสเป็นกระบวนการที่สำคัญในกระบวนการผลิตน้ำตาล ซึ่งกระบวนการมีการทำงานเพื่อแยกระหว่างตะกอนกับน้ำอ้อยออกจากกันโดยใช้สารฟลอกคูแลนต์ โดยในการใส่ปริมาณและความเข้มข้นสารฟลอกคูแลนต์ลงไปในน้ำอ้อยทำให้ส่งผลกระทบโดยตรงต่อความเร็วการตกตะกอนและค่าความขุ่นของน้ำอ้อย วิทยานิพนธ์เล่มนี้เสนอวิธีการพยากรณ์ปริมาณและความเข้มข้นสารฟลอกคูแลต์ในกระบวนการพักใสสำหรับอุตสาหกรรมการผลิตน้ำตาลจากอ้อย โดยใช้โครงข่ายประสาทเทียมแบบวนซ้ำชนิดพิเศษ Long Short-Term Memory โดยข้อมูลที่นำมาใช้เป็นข้อมูลขาเข้าสำหรับการสร้างโมเดลได้แก่ ปริมาณอ้อยสด, ปริมาณอ้อยเผา, ความขุ่นของน้ำอ้อย และปริมาณน้ำฝน และข้อมูลขาออกได้แก่ ปริมาณและความเข้มข้นของสารฟลอกคูแลนต์ ทั้งนี้ข้อมูลที่ได้นำมาจากโรงงานผลิตน้ำตาลแห่งหนึ่งในประเทศไทย ผลการทดลองแสดงให้เห็นถึงประสิทธิภาพของโมเดลที่ได้นำเสนอ LSTM โดยการเปรียบเทียบกับโมเดลอื่นๆ ได้แก่ Autoregressive Integrated Moving Average (ARIMA), Recurrent Neural Network (RNN) และ Gated Recurrent Unit (GRU) โดยใช้ตัวแปร RMSE และ MAPE เป็นตัววัดประสิทธิภาพของโมเดล พบว่าโมเดลที่นำเสนอมีประสิทธิภาพที่สุดในการพยากรณ์ปริมาณและความเข้มข้นของสารฟลอกคูแลนต์

การจำแนกปัญหาของเทคโนโลยีฐานข้อมูลในชุมชนถามตอบออนไลน์, ณัฐนัย สุวรรณชูชิต

Chulalongkorn University Theses and Dissertations (Chula ETD)

วิทยานิพนธ์นี้นำเสนอแนวทางการสร้างเครื่องมือการทำงานอัตโนมัติเพื่อจำแนกคำถามบนเว็บไซต์สแต็กโอเวอร์โฟลว์ โดยเฉพาะที่เกี่ยวกับชนิดของผลิตภัณฑ์ฐานข้อมูล ซึ่งถือเป็นข้อมูลที่มีค่าสำหรับเจ้าของผลิตภัณฑ์ฐานข้อมูลในการนำไปปรับปรุงผลิตภัณฑ์ หมวดหมู่ของคำถามกำหนดไว้เป็นสองระดับได้แก่ ระดับปัญหา และ ปัญหาย่อย โดยที่ระดับปัญหาประกอบด้วย การพัฒนา การติดตั้ง และ การปรับปรุงประสิทธิภาพ ในขณะที่ ปัญหาย่อย ประกอบด้วย การออกแบบ ข้อจำกัด และการอภิปรายปัญหา ด้วยการรวมทั้งสองระดับเข้าด้วยกัน คำถามจะถูกจำแนกออกเป็นเก้าหมวดของปัญหา-ปัญหาย่อย การประมวลผลภาษาธรรมชาติและการจำแนกข้อความถูกนำมาใช้ โดยใช้อัลกอริทึมการเรียนรู้ของเครื่องที่หลากหลาย โมเดลการจำแนกประเภทที่มีประสิทธิภาพดีที่สุดจะถูกนำมาใช้ในเว็บแอปพลิเคชัน เพื่อจำแนกแต่ละคำถามโดยใช้แท็กปัญหา-ปัญหาย่อย นอกจากนี้คำถามที่ถูกจำแนกออกตามหมวดแล้ว สามารถนำมาวิเคราะห์เพิ่มเติมโดยใช้อัลกอริทึมการสร้างแบบจำลองหัวข้อ เพื่อให้ทราบว่าคำถามในแต่ละหมวดนั้นกล่าวถึงหัวข้อใดบ้าง ซึ่งจะเป็นข้อมูลเพิ่มเติมให้กับเจ้าของผลิตภัณฑ์ฐานข้อมูลในการทำความเข้าใจถึงปัญหาของผลิตภัณฑ์เพื่อจะได้ทำการปรับปรุงต่อไป

Robustness Against Attacks And Uncertainties In Smart Cyber-Physical Systems, Prithwiraj Roy

Doctoral Dissertations

Cyber-Physical Systems (CPS) are sensing, processing, and communicating platforms, embedded with physical devices that provide real-time monitoring and control. Security challenges in CPS necessitate solutions that are robust against attacks and uncertainties and provide a seamless operation, especially when used in real-time applications to monitor and secure critical infrastructures. CPS mainly consists of a physical component for sensing or monitoring and a cyber component for processing and communicating. The quality of interactions between physical and cyber systems has direct impacts on the system’s performance and reliability.

CPS plays a major role in smart services and applications within a smart living …

Biochemical Assay Invariant Attestation For The Security Of Cyber-Physical Digital Microfluidic Biochips, Fredrick Eugene Love Ii

Masters Theses

“Due to the devastating global impact that infectious diseases have had, especially in developing countries, the demand for access to adequate resources to combat sickness continues to be a heavy burden. Reliable and affordable diagnostics is a vital first line of defense in fighting outbreaks and providing accurate treatment. Digital microfluidics biochips capable of running multiple diagnostic tests on a single platform are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. Although these systems offer many benefits, processing errors are inherent. Therefore, cyber-physical digital biochips are being investigated that …

Values Of Trust In Ai In Autonomous Driving Vehicles, Ru Lian

Masters Theses

“Automation with artificial intelligence technology is an emerging field and is widely used in various industries. With the increasing autonomy, learning, and adaptability of intelligent machines such as self-driving cars, it is difficult to regard them as simple tools in human hands. At the same time, a series of problems and challenges such as predictability, interpretability, and causality arise. Trust in self-driving technology will impact the adoption and utilization of autonomous driving technology. A qualitative research methodology, Value-Focused Thinking, is used to identify the values of trust in autonomous driving vehicles and analyze the relationship between these values”--Abstract, page iii.

Findings Of The Nlp4if-2021 Shared Tasks On Fighting The Covid-19 Infodemic And Censorship Detection, Shaden Shaar, Firoj Alam, Giovanni Da San Martino, Alex Nikolov, Wajdi Zaghouani, Preslav Nakov, Anna Feldman

Department of Computer Science Faculty Scholarship and Creative Works

We present the results and the main findings of the NLP4IF-2021 shared tasks. Task 1 focused on fighting the COVID-19 infodemic in social media, and it was offered in Arabic, Bulgarian, and English. Given a tweet, it asked to predict whether that tweet contains a verifiable claim, and if so, whether it is likely to be false, is of general interest, is likely to be harmful, and is worthy of manual fact-checking; also, whether it is harmful to society, and whether it requires the attention of policy makers. Task 2 focused on censorship detection, and was offered in Chinese. A …

Strategies To Sustain Small Construction Businesses Beyond The First 5 Years Of Operation, Catherine Nyasha Mukopfa

Walden Dissertations and Doctoral Studies

Small business owners employ over half the U.S. labor force, yet only 50% of small businesses survive beyond 5 years. When small business owners understand the factors that lead to their business failure, they can develop strategies to remain sustainable and profitable within the first 5 years, thus reducing the potential of business failure. Grounded in the resource-based theory, the purpose of this qualitative multiple case study was to explore strategies five small construction owners in central Georgia used to remain in business beyond 5 years. Data were collected from semi structured interviews, a review of organization income statements and …

Strategies Security Managers Used To Prevent Security Breaches In Scada Systems' Networks, Oladipo Ogunmesa

Walden Dissertations and Doctoral Studies

Supervisory Control and Data Acquisition (SCADA) systems monitor and control physical processes in critical infrastructure. The impact of successful attacks on the SCADA systems includes the system's downtime and delay in production, which may have a debilitating effect on the national economy and create critical human safety hazards. Grounded in the general systems theory, the purpose of this qualitative multiple case study was to explore strategies SCADA security managers in the Southwest region of the United States use to secure SCADA systems' networks. The participants comprised six SCADA security managers from three oil and gas organizations in the midstream sector …

Algorithmic Responsibility, Algorithmic Bias, Darla Jackson

Other Faculty Publications

No abstract provided.

Visualization For Solving Non-Image Problems And Saliency Mapping, Divya Chandrika Kalla

All Master's Theses

High-dimensional data play an important role in knowledge discovery and data science. Integration of visualization, visual analytics, machine learning (ML), and data mining (DM) are the key aspects of data science research for high-dimensional data. This thesis is to explore the efficiency of a new algorithm to convert non-images data into raster images by visualizing data using heatmap in the collocated paired coordinates (CPC). These images are called the CPC-R images and the algorithm that produces them is called the CPC-R algorithm. Powerful deep learning methods open an opportunity to solve non-image ML/DM problems by transforming non-image ML problems into …

Accurate Diagnosis Of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence, K. S. Wang, G. Yu, C. Xu, X. H. Meng, J. Zhou, W. Zhou, Et. Al.

Michigan Tech Publications, Part 1

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered …

Unsupervised Representation Learning By Predicting Random Distances, Hu Wang, Guansong Pang, Chunhua Shen, Congbo Ma

Research Collection School Of Computing and Information Systems

Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected …

Blockchain Technology Applications, Concerns And Recommendations For Public Sector, Mohanad Ghazi Yaseen, Mahadi Bahari, Omar A. Hammood

Mesopotamian Journal of Computer Science

Blockchain technology is being hailed as the next significant phenomenon, with potential near-term uses that might profoundly impact society and the economy. While the previous studies emphasized cryptocurrency research, our article focused more on the blockchain-based applications applied in public sectors. This review was conducted by collecting articles from different libraries, and the search strategy was applied Nvivo software was utilized to code the literature and extract the themes used to build the body of this research. Finally, the study identified several applications and concerns regarding adopting and implementing blockchain technology within these sectors and the recommendations offered by scholars …