Computer Sciences Commons^™

Test-Time Augmentation For 3d Point Cloud Classification And Segmentation, Tuan-Anh Vu, Srinjay Sarkar, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Data augmentation is a powerful technique to enhance the performance of a deep learning task but has received less attention in 3D deep learning. It is well known that when 3D shapes are sparsely represented with low point density, the performance of the downstream tasks drops significantly. This work explores test-time augmentation (TTA) for 3D point clouds. We are inspired by the recent revolution of learning implicit representation and point cloud upsampling, which can produce high-quality 3D surface reconstruction and proximity-to-surface, respectively. Our idea is to leverage the implicit field reconstruction or point cloud upsampling techniques as a systematic way …

The Effect Of Internet Firms’ Data Analytics Capability On Their Innovation Speed And Innovation Quality: A Dynamic Capability Perspective, Yeyu Hua

Dissertations and Theses Collection (Open Access)

With the advent of big data era, data plays a pivotal role in sustainingfirms’ competitive advantages. Although a few studies have shown that data analytics capability contributes to firms’ innovative performance, these studies either focus on general innovative performance or specific types of innovation, such as incremental innovation, radical innovation, and supply chaininnovation. In this thesis, I enrich this stream of literature by conducting twostudies to further examine the relationship between data analytics capabilityand innovation speed as well as innovation quality. This thesis consists of twostudies. Study 1 is a survey study, in which I investigate the relationshipbetween data analytics …

U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan

Theses and Dissertations

The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.

A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike

Theses and Dissertations

This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …

A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae

Theses and Dissertations

A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …

Design Considerations For The Use Of The Julia Programming Language In Future Quantum Networking Simulation Software, Takashi Joubert

Theses and Dissertations

Given the prevalence of Python-based packages in the existing quantum network simulation ecosystem, we attempt to assess what might be realistically gained by switching to Julia. We focus our experimental activities on three areas: 1) surveying the characteristics of Julia as they tie into robust framework development, 2) presenting benchmarks that compare Julia and Python with respect to elements of possible simulation workloads, and 3) producing a tangible lightweight Julia architecture for modeling components in a manner similar to SeQUeNCe. Our analysis suggests that while Julia does o.er performance advantages over Python over certain workloads, knowing the reasons for why …

Enhancing Sequence With Quantum Key Distribution Protocols And An Intuitive User Interface, Blake Perkins

Theses and Dissertations

The rapidly growing domain of quantum networks necessitates advancements in associated software packages. This master’s thesis will detail, in part, new protocols added to extend the usefulness of SeQUeNCe. Notably, these added protocols were implemented to ensure compatibility and efficiency with the existing codebase. To complement this expansion in capability, the graphical user interface (GUI) was restructured. Updates to the GUI now allow users to initiate and operate these newly integrated protocols with ease, thereby expanding the accessibility of SeQUeNCe to a wider audience. By prioritizing the incorporation of these new protocols and refining the user interface, this research significantly …

Group Convolutional Decoders For Toric Codes, Jim Wang

Theses and Dissertations

Quantum Error Correction (QEC) enables both industrial and defense applications of quantum computing. Toric codes and other quantum Low-Density Parity-Check (LDPC) codes are promising and well-researched methods of QEC. However, their decoding cost increases exponentially with a computer’s qubit count. Neural Network (NN) decoders have been shown to decode a code’s error syndrome both accurately and fast enough for a real-time error correcting scheme. Recent key developments introduced Convolutional Neural Network (CNN) to implement a translationally equivariant decoder for a toric code. These CNN decoders both outperform NN decoders and require less training data. This research applies a Group Convolutional …

Quantum Circuit Reduction Using Three Layer Transposition, Christian L. Grauberger

Theses and Dissertations

The potential of quantum computing to revolutionize critical military applications has led the US Department of Defense to recognize it as a keen interest. However, the practical implementation of these theoretical applications on physical quantum devices is currently limited by inherent reliability and accuracy issues in quantum hardware. To mitigate errors stemming from these limitations, the incorporation of software-based solutions is imperative. Quantum circuit optimization stands out as a primary method of increasing the accuracy of quantum computations. One of the key components of this approach is circuit reduction, whereby circuits are condensed to realize the same computation using fewer …

Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O

Theses and Dissertations

This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …

A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington

Theses and Dissertations

In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …

Possible Role Of Correlation Coefficients And Network Analysis Of Multiple Intracellular Proteins In Blood Cells Of Patients With Bipolar Disorder In Studying The Mechanism Of Lithium Responsiveness: A Proof-Concept Study, Keming Gao, Marzieh Ayati, Nicholas M. Kaye, Mehmet Koyutürk, Joseph R. Calabrese, Eric Christian, Hillard M. Lazarus, David Kaplan

Computer Science Faculty Publications

Background: The mechanism of lithium treatment responsiveness in bipolar disorder (BD) remains unclear. The aim of this study was to explore the utility of correlation coefficients and protein-to-protein interaction (PPI) network analyses of intracellular proteins in monocytes and CD4+ lymphocytes of patients with BD in studying the potential mechanism of lithium treatment responsiveness. Methods: Patients with bipolar I or II disorder who were diagnosed with the MINI for DSM-5 and at any phase of the illness with at least mild symptom severity and received lithium (serum level ≥ 0.6 mEq/L) for 16 weeks were divided into two groups, responders (≥50% …

Sigmadiff: Semantics-Aware Deep Graph Matching For Pseudocode Diffing, Lian Gao, Yu Qu, Sheng Yu, Yue Duan, Heng Yin

Research Collection School Of Computing and Information Systems

Pseudocode diffing precisely locates similar parts and captures differences between the decompiled pseudocode of two given binaries. It is particularly useful in many security scenarios such as code plagiarism detection, lineage analysis, patch, vulnerability analysis, etc. However, existing pseudocode diffing and binary diffing tools suffer from low accuracy and poor scalability, since they either rely on manually-designed heuristics (e.g., Diaphora) or heavy computations like matrix factorization (e.g., DeepBinDiff). To address the limitations, in this paper, we propose a semantics-aware, deep neural network-based model called SIGMADIFF. SIGMADIFF first constructs IR (Intermediate Representation) level interprocedural program dependency graphs (IPDGs). Then it uses …

Fixing Your Own Smells: Adding A Mistake-Based Familiarization Step When Teaching Code Refactoring, Ivan Wei Han Tan, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

Programming problems can be solved in a multitude of functionally correct ways, but the quality of these solutions (e.g. readability, maintainability) can vary immensely. When code quality is poor, symptoms emerge in the form of 'code smells', which are specific negative characteristics (e.g. duplicate code) that can be resolved by applying refactoring patterns. Many undergraduate computing curricula train students on this software engineering practice, often doing so via exercises on unfamiliar instructor-provided code. Our observation, however, is that this makes it harder for novices to internalise refactoring as part of their own development practices. In this paper, we propose a …

Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang

Research Collection School Of Computing and Information Systems

Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability. Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated …

Ur2m: Uncertainty And Resource-Aware Event Detection On Microcontrollers, Hong Jia, Young D. Kwon, Dong Ma, Nhat Pham, Lorena Qendro, Tam Vu, Cecilia Mascolo

Research Collection School Of Computing and Information Systems

Traditional machine learning techniques are prone to generating inaccurate predictions when confronted with shifts in the distribution of data between the training and testing phases. This vulnerability can lead to severe consequences, especially in applications such as mobile healthcare. Uncertainty estimation has the potential to mitigate this issue by assessing the reliability of a model's output. However, existing uncertainty estimation techniques often require substantial computational resources and memory, making them impractical for implementation on microcontrollers (MCUs). This limitation hinders the feasibility of many important on-device wearable event detection (WED) applications, such as heart attack detection. In this paper, we present …

Towards Understanding Convergence And Generalization Of Adamw, Pan Zhou, Xingyu Xie, Zhouchen Lin, Shuicheng Yan

Research Collection School Of Computing and Information Systems

AdamW modifies Adam by adding a decoupled weight decay to decay network weights per training iteration. For adaptive algorithms, this decoupled weight decay does not affect specific optimization steps, and differs from the widely used ℓ2-regularizer which changes optimization steps via changing the first- and second-order gradient moments. Despite its great practical success, for AdamW, its convergence behavior and generalization improvement over Adam and ℓ2-regularized Adam (ℓ2-Adam) remain absent yet. To solve this issue, we prove the convergence of AdamW and justify its generalization advantages over Adam and ℓ2-Adam. Specifically, AdamW provably converges but minimizes a dynamically regularized loss that …

Generative Ai In Finance: Risks And Potential Solutions, Nydia Remolina Leon

Research Collection Yong Pung How School Of Law

Generative Artificial Intelligence captured the attention of academics, policymakers, the private sector, and some regulators in 2022 after the launch of ChatGPT and its widespread adoption worldwide. Then, during the World Economic Forum session in 2023, Microsoft Chairman and Chief Executive Officer Satya Nadella said that the ‘golden age’ of AI is underway and generative AI is set to play a big role in it. Accordingly, Microsoft has invested billions of dollars into OpenAI, the company behind the launch of ChatGPT. Additionally, Google and Meta have also created their own generative AI models. Given the multiplicity of potential use cases …

Self-Admitted Technical Debts Identification: How Far Are We?, Hao Gu, Shichao Zhang, Qiao Huang, Zhifang Liao, Jiakun Liu, David Lo

Research Collection School Of Computing and Information Systems

Self-admitted technical debt (SATD) is a kind of technical debt that is already acknowledged by the developers and needs additional work or resources to address in the future. In recent years, though many methods have been proposed to detect SATDs, these methods have mainly focused on Java-type code comments published by Maldonado et al. It is unclear whether these methods trained on Maldonado's code comments dataset can find SATD in other programming languages or other software artifacts, such as issue trackers, pull requests, and commit messages effectively. In order to answer the above confusion and investigate how far our community …

Active Discovering New Slots For Task-Oriented Conversation, Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Research Collection School Of Computing and Information Systems

Existing task-oriented conversational systems heavily rely on domain ontologies with pre-defined slots and candidate values. In practical settings, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working towards detecting out-of-vocabulary values or discovering new slots under unsupervised or semi-supervised learning paradigms. However, overemphasizing on the conversation data patterns alone induces these methods to yield noisy and arbitrary slot results. To facilitate the pragmatic utility, real-world systems tend to provide a stringent amount of human labeling quota, which offers an authoritative way …

On The Effects Of Information Asymmetry In Digital Currency Trading, Kwansoo Kim, Robert John Kauffman

Research Collection School Of Computing and Information Systems

We report on two studies that examine how social sentiment influences information asymmetry in digital currency markets. We also assess whether cryptocurrency can be an investment vehicle, as opposed to only an instrument for asset speculation. Using a dataset on transactions from an exchange in South Korea and sentiment from Korean social media in 2018, we conducted a study of different trading behavior under two cryptocurrency trading market microstructures: a bid-ask spread dealer's market and a continuous trading buy-sell, immediate trade execution market. Our results highlight the impacts of positive and negative trader social sentiment valences on the effects of …

Application Of Collaborative Learning Paradigms Within Software Engineering Education: A Systematic Mapping Study, Rita Garcia, Christoph Treude, Andrew Valentine

Research Collection School Of Computing and Information Systems

Collaboration is used in Software Engineering (SE) to develop software. Industry seeks SE graduates with collaboration skills to contribute to productive software development. SE educators can use Collaborative Learning (CL) to help students develop collaboration skills. This paper uses a Systematic Mapping Study (SMS) to examine the application of the CL educational theory in SE Education. The SMS identified 14 papers published between 2011 and 2022. We used qualitative analysis to classify the papers into four CL paradigms: Conditions, Effect, Interactions, and Computer-Supported Collaborative Learning (CSCL). We found a high interest in CSCL, with a shift in student interaction research …

Leveraging Multimodal Features And Item‑Level User Feedback For Bundle Construction, Yunshan Ma, Xiaohao Liu, Yinwei Wei, Zhulin Tao, Xiang Wang, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Automatic bundle construction is a crucial prerequisite step in various bundle-aware online services. Previous approaches are mostly designed to model the bundling strategy of existing bundles. However, it is hard to acquire large-scale well-curated bundle dataset, especially for those platforms that have not offered bundle services before. Even for platforms with mature bundle services, there are still many items that are included in few or even zero bundles, which give rise to sparsity and cold-start challenges in the bundle construction models. To tackle these issues, we target at leveraging multimodal features, item-level user feedback signals, and the bundle composition information, …

Decentralized Multimedia Data Sharing In Iov: A Learning-Based Equilibrium Of Supply And Demand, Jiani Fan, Minrui Xu, Jiale Guo, Lwin Khin Shar, Jiawen Kang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this paper, by modeling the …

Hearing Iterative And Recursive Behavior: Sonification Improves Student Understanding, Joel C. Adams, Hayworth Anderson

University Faculty Publications and Creative Works

Abstract topics such as recursion are challenging for many computer science students to understand. In this experience report, we explore function sonification-the addition of sound to a function to communicate information about the function's behavior in real-time as it runs-as a pedagogical approach for improving students' understanding of recursion. We present several example iterative and recursive function sonifications, plus spectrograms that illustrate their different sonic behaviors. We also present experimental evidence that using these sonifications significantly improved the understanding of recursion for students who used them, compared to students who used silent (i.e., traditional) versions of the same functions. Based …

Artificial Intelligence And/Or Machine Learning (Ai &| Ml), George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

These slides are from an invited panel presentation at my home institution, Loyola University Chicago, organized by the Loyola University Chicago Retiree Association (LUCRA). I was asked to give a broad historical overview of AI and ML and speak about its societal impacts.

"The Loyola University Chicago Retiree Association embraces the Vision of Loyola University Chicago and will assist students, faculty, and administrators as they strive to serve humanity. The group values freedom of inquiry, the pursuit of truth, and care of others and embraces a commitment to excellence, service that promotes social justice, values based leadership, and global awareness."

Non-Binary Evaluation Of Next-Basket Food Recommendation, Yue Liu, Palakorn Achananuparp, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Next-basket recommendation (NBR) is a recommendation task that predicts a basket or a set of items a user is likely to adopt next based on his/her history of basket adoption sequences. It enables a wide range of novel applications and services from predicting next basket of items for grocery shopping to recommending food items a user is likely to consume together in the next meal. Even though much progress has been made in the algorithmic NBR research over the years, little research has been done to broaden knowledge about the evaluation of NBR methods, which is largely based on the …

Transiam: Aggregating Multi-Modal Visual Features With Locality For Medical Image Segmentation, Xuejian Li, Shiqiang Ma, Junhai Xu, Jijun Tang, Shengfeng He, Fei Guo

Research Collection School Of Computing and Information Systems

Automatic segmentation of medical images plays an important role in the diagnosis of diseases. On single-modal data, convolutional neural networks have demonstrated satisfactory performance. However, multi-modal data encompasses a greater amount of information rather than single-modal data. Multi-modal data can be effectively used to improve the segmentation accuracy of regions of interest by analyzing both spatial and temporal information. In this study, we propose a dual-path segmentation model for multi-modal medical images, named TranSiam. Taking into account that there is a significant diversity between the different modalities, TranSiam employs two parallel CNNs to extract the features which are specific to …

Simulated Annealing With Reinforcement Learning For The Set Team Orienteering Problem With Time Windows, Vincent F. Yu, Nabila Y. Salsabila, Shih-W Lin, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research investigates the Set Team Orienteering Problem with Time Windows (STOPTW), a new variant of the well-known Team Orienteering Problem with Time Windows and Set Orienteering Problem. In the STOPTW, customers are grouped into clusters. Each cluster is associated with a profit attainable when a customer in the cluster is visited within the customer's time window. A Mixed Integer Linear Programming model is formulated for STOPTW to maximizing total profit while adhering to time window constraints. Since STOPTW is an NP-hard problem, a Simulated Annealing with Reinforcement Learning (SARL) algorithm is developed. The proposed SARL incorporates the core concepts …

Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink

Research Collection School Of Computing and Information Systems

Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …