Digital Commons Network^™

Neuro-Immune Interactions And Immuno-Oncology, Narmina Khanmammadova, Shajedul Islam, Padmanee Sharma, Moran Amit

Faculty, Staff and Student Publications

The nervous system is an important component of the tumor microenvironment (TME), driving tumorigenesis and tumor progression. Neuronal cues (e.g., neurotransmitters and neuropeptides) in the TME cause phenotypic changes in immune cells, such as increased exhaustion and inhibition of effector cells, which promote immune evasion and cancer progression. Two types of immune regulation by tumor-associated nerves are discussed in this review: regulation via neuronal stimuli (i.e., by neural transmission) and checkpoint-mediated neuronal immune regulation. The latter occurs via the expression of immune checkpoints on the membranes of intratumoral nerves and glial cells. Here, we summarize novel findings regarding the neuroimmune …

Predicting Severity Of Radiation Induced Lymphopenia In Individual Proton Therapy Patients For Varying Dose Rate And Fractionation Using Dynamic 4-Dimensional Blood Flow Simulations, Lucas Mccullum, Jungwook Shin, Stella Xing, Chris Beekman, Jan Schuemann, Dan Duda, Steven H Lin, Camilo M Correa-Alfonso, Sean Domal, Julia Withrow, Wesley Bolch, Harald Paganetti, Clemens Grassberger

Faculty, Staff and Student Publications

PURPOSE: Radiation-induced lymphopenia has gained attention recently as the result of its correlation with survival in a range of indications, particularly when combining radiation therapy (RT) with immunotherapy. The purpose of this study is to use a dynamic blood circulation model combined with observed lymphocyte depletion in patients to derive the in vivo radiosensitivity of circulating lymphocytes and study the effect of RT delivery parameters.

METHODS AND MATERIALS: We assembled a cohort of 17 patients with hepatocellular carcinoma treated with proton RT alone in 15 fractions (fx) using conventional dose rates (beam-on time [BOT], 120 seconds) for whom weekly absolute …

A Spatiotemporal Notch Interaction Map From Plasma Membrane To Nucleus, Alexandre P Martin, Gary A Bradshaw, Robyn J Eisert, Emily D Egan, Lena Tveriakhina, Andrew N Dates, Jon C Aster, Tom Kirchhausen, Marian Kalocsay, Stephen C Blacklow

Faculty, Staff and Student Publications

Notch signaling relies on ligand-induced proteolysis of the transmembrane receptor Notch to liberate a nuclear effector that drives cell fate decisions. Upon ligand binding, sequential cleavage of Notch by the transmembrane protease ADAM10 and the intracellular protease γ-secretase releases the Notch intracellular domain (NICD), which translocates to the nucleus and forms a complex that induces target gene transcription. To map the location and timing of the individual steps required for the proteolysis and movement of Notch from the plasma membrane to the nucleus, we used proximity labeling with quantitative, multiplexed mass spectrometry to monitor the interaction partners of endogenous NOTCH2 …

Pitfalls In Machine Learning-Based Assessment Of Tumor-Infiltrating Lymphocytes In Breast Cancer: A Report Of The International Immuno-Oncology Biomarker Working Group On Breast Cancer, Jeppe Thagaard, Glenn Broeckx, David B Page, Chowdhury Arif Jahangir, Sara Verbandt, Rajarsi Gupta, Khalid Abduljabbar, Gabriela Acosta Haab, Balazs Acs, Guray Akturk, Jonas S Almeida, Isabel Alvarado-Cabrero, Mohamed Amgad, Farid Azmoudeh-Ardalan, Sunil Badve, Nurkhairul Bariyah Baharun, Eva Balslev, Enrique R Bellolio, Vydehi Bheemaraju, Kim Rm Blenman, Luciana Botinelly Mendonça Fujimoto, Najat Bouchmaa, Octavio Burgues, Alexandros Chardas, Maggie Chon U Cheang, Francesco Ciompi, Lee Ad Cooper, An Coosemans, Germán Corredor, Anders B Dahl, Flavio Luis Dantas Portela, Frederik Deman, Sandra Demaria, Johan Doré Hansen, Sarah N Dudgeon, Thomas Ebstrup, Mahmoud Elghazawy, Claudio Fernandez-Martín, Stephen B Fox, William M Gallagher, Jennifer M Giltnane, Sacha Gnjatic, Paula I Gonzalez-Ericsson, Anita Grigoriadis, Niels Halama, Matthew G Hanna, Aparna Harbhajanka, Steven N Hart, Johan Hartman, Søren Hauberg, Stephen Hewitt, Akira I Hida, Hugo M Horlings, Zaheed Husain, Evangelos Hytopoulos, Sheeba Irshad, Emiel Am Janssen, Mohamed Kahila, Tatsuki R Kataoka, Kosuke Kawaguchi, Durga Kharidehal, Andrey I Khramtsov, Umay Kiraz, Pawan Kirtani, Liudmila L Kodach, Konstanty Korski, Anikó Kovács, Anne-Vibeke Laenkholm, Corinna Lang-Schwarz, Denis Larsimont, Jochen K Lennerz, Marvin Lerousseau, Xiaoxian Li, Amy Ly, Anant Madabhushi, Sai K Maley, Vidya Manur Narasimhamurthy, Douglas K Marks, Elizabeth S Mcdonald, Ravi Mehrotra, Stefan Michiels, Fayyaz Ul Amir Afsar Minhas, Shachi Mittal, David A Moore, Shamim Mushtaq, Hussain Nighat, Thomas Papathomas, Frederique Penault-Llorca, Rashindrie D Perera, Christopher J Pinard, Juan Carlos Pinto-Cardenas, Giancarlo Pruneri, Lajos Pusztai, Arman Rahman, Nasir Mahmood Rajpoot, Bernardo Leon Rapoport, Tilman T Rau, Jorge S Reis-Filho, Joana M Ribeiro, David Rimm, Anne Roslind, Anne Vincent-Salomon, Manuel Salto-Tellez, Joel Saltz, Shahin Sayed, Ely Scott, Kalliopi P Siziopikou, Christos Sotiriou, Albrecht Stenzinger, Maher A Sughayer, Daniel Sur, Susan Fineberg, Fraser Symmans, Sunao Tanaka, Timothy Taxter, Sabine Tejpar, Jonas Teuwen, E Aubrey Thompson, Trine Tramm, William T Tran, Jeroen Van Der Laak, Paul J Van Diest, Gregory E Verghese, Giuseppe Viale, Michael Vieth, Noorul Wahab, Thomas Walter, Yannick Waumans, Hannah Y Wen, Wentao Yang, Yinyin Yuan, Reena Md Zin, Sylvia Adams, John Bartlett, Sibylle Loibl, Carsten Denkert, Peter Savas, Sherene Loi, Roberto Salgado, Elisabeth Specht Stovgaard

Faculty, Staff and Student Publications

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. …

Combining Voltage-Sensitive Dye, Carbon Fiber Array, And Extracellular Nerve Electrodes Using A 3-D Printed Recording Chamber And Manipulators, Curtis L Neveu, Yu Huan, Yuto Momohara, Paras R Patel, Hillel J Chiel, John H Byrne

Faculty, Staff and Student Publications

BACKGROUND: The analyses of neuronal circuits require high-throughput technologies for stimulating and recording many neurons simultaneously with single-neuron precision. Voltage-sensitive dyes (VSDs) have enabled the monitoring of membrane potentials of many (10-100 s) neurons simultaneously. Carbon fiber electrode (CFE) arrays allow for stimulation and recording of many neurons simultaneously, including intracellularly.

NEW METHOD: Combining CFE with VSD leverages the advantages of both technologies, allowing for stimulation of single neurons while recording the activity of the entire network. 3-D printing technology was used to develop a chamber to simultaneously perform VSD imaging, CFE array recording, and extracellular recording from individual glass …

Superficial Thrombophlebitis In Ipsilateral Breast After Covid-19 Vaccination, John A Breier, Meng Li, Gregory J Dick, Karthik Anantharaman

Faculty, Staff and Student Publications

In globally distributed deep-sea hydrothermal vent plumes, microbiomes are shaped by the redox energy landscapes created by reduced hydrothermal vent fluids mixing with oxidized seawater. Plumes can disperse over thousands of kilometers and their characteristics are determined by geochemical sources from vents, e.g., hydrothermal inputs, nutrients, and trace metals. However, the impacts of plume biogeochemistry on the oceans are poorly constrained due to a lack of integrated understanding of microbiomes, population genetics, and geochemistry. Here, we use microbial genomes to understand links between biogeography, evolution, and metabolic connectivity, and elucidate their impacts on biogeochemical cycling in the deep sea. Using …

Magnesium-Based Nanocomposites: A Review From Mechanical, Creep And Fatigue Properties, S. Abazari, A. Shamsipur, H. R. Bakhsheshi-Rad, J. W. Drelich, J. Goldman, S. Sharif, A. F. Ismail, M. Razzaghi

Michigan Tech Publications

The addition of nanoscale additions to magnesium (Mg) based alloys can boost mechanical characteristics without noticeably decreasing ductility. Since Mg is the lightest structural material, the Mg-based nanocomposites (NCs) with improved mechanical properties are appealing materials for lightweight structural applications. In contrast to conventional Mg-based composites, the incorporation of nano-sized reinforcing particles noticeably boosts the strength of Mg-based nanocomposites without significantly reducing the formability. The present article reviews Mg-based metal matrix nanocomposites (MMNCs) with metallic and ceramic additions, fabricated via both solid-based (sintering and powder metallurgy) and liquid-based (disintegrated melt deposition) technologies. It also reviews strengthening models and mechanisms that …

Investigation Of Pool Boiling Heat Transfer On Modified Copper And Aluminum Surfaces, Mariam Khaled Zaghloul

Theses

This thesis presents an investigation of pool-boiling heat transfer using the surfacemodification method. The studied boiling surfaces were smooth and rough pin-fins embedded on the copper and aluminum surfaces. This study visualizes bubble formation and departure diameter. Experimental testing aims to predict the heat transfer coefficients for different heat fluxes over smooth and rough pin-finned surfaces. The surface modification method allows changing the boiling surface characteristics, which can lead to enhanced heat transfer. Several factors were studied, including the heat-transfer coefficient, heat flux, bubble departure diameter, and formation time. Four surfaces were used for conducting the pool boiling experiment: a …

Survey On Sentiment Analysis: Evolution Of Research Methods And Topics, Jingfeng Cui, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates …

Decoding The Underlying Meaning Of Multimodal Hateful Memes, Ming Shan Hee, Wen Haw Chong, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme …

Transferable Curricula Through Difficulty Conditioned Generators, Sidney Tio, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Advancements in reinforcement learning (RL) have demonstrated superhuman performance in complex tasks such as Starcraft, Go, Chess etc. However, knowledge transfer from Artificial "Experts" to humans remain a significant challenge. A promising avenue for such transfer would be the use of curricula. Recent methods in curricula generation focuses on training RL agents efficiently, yet such methods rely on surrogate measures to track student progress, and are not suited for training robots in the real world (or more ambitiously humans). In this paper, we introduce a method named Parameterized Environment Response Model (PERM) that shows promising results in training RL agents …

Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge …

Camper: An Effective Framework For Privacy-Aware Deep Entity Resolution, Yuxiang Guo, Lu Chen, Zhengjie Zhou, Baihua Zheng, Ziquan Fang, Zhikun Zhang, Yuren Mao, Yunjun Gao

Research Collection School Of Computing and Information Systems

Entity Resolution (ER) is a fundamental problem in data preparation. Standard deep ER methods have achieved state-of-the-art efectiveness, assuming that relations from diferent organizations are centrally stored. However, due to privacy concerns, it can be difcult to centralize data in practice, rendering standard deep ER solutions inapplicable. Despite eforts to develop rule-based privacy-preserving ER methods, they often neglect subtle matching mechanisms and have poor efectiveness as a result. To bridge efectiveness and privacy, in this paper, we propose CampER, an efective framework for privacy-aware deep entity resolution. Specifcally, we frst design a training pair self-generation strategy to overcome the absence …

Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to …

Decompiling X86 Deep Neural Network Executables, Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

Research Collection School Of Computing and Information Systems

Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators. We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process …

Document-Level Relation Extraction Via Separate Relation Representation And Logical Reasoning, Heyan Huang, Changsen Yuan, Qian Liu, Yixin Cao

Research Collection School Of Computing and Information Systems

Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model, SRLR, using Separate Relation Representation and Logical Reasoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct …

Hyperbolic Graph Topic Modeling Network With Continuously Updated Topic Tree, Ce Zhang, Rex Ying, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Connectivity across documents often exhibits a hierarchical network structure. Hyperbolic Graph Neural Networks (HGNNs) have shown promise in preserving network hierarchy. However, they do not model the notion of topics, thus document representations lack semantic interpretability. On the other hand, a corpus of documents usually has high variability in degrees of topic specificity. For example, some documents contain general content (e.g., sports), while others focus on specific themes (e.g., basketball and swimming). Topic models indeed model latent topics for semantic interpretability, but most assume a flat topic structure and ignore such semantic hierarchy. Given these two challenges, we propose a …

Semantically Constitutive Entities In Knowledge Graphs, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Knowledge graphs are repositories of facts about a world. In this work, we seek to distill the set of entities or nodes in a knowledge graph into a specified number of constitutive nodes, whose embeddings would be retained. Intuitively, the remaining accessory nodes could have their original embeddings “forgotten”, and yet reconstitutable from those of the retained constitutive nodes. The constitutive nodes thus represent the semantically constitutive entities, which retain the core semantics of the knowledge graph. We propose a formulation as well as algorithmic solutions to minimize the reconstitution errors. The derived constitutive nodes are validated empirically both in …

The Heterogeneous Vehicle Routing Problem With Multiple Time Windows For The E-Waste Collection Problem, Aldy Gunawan, Minh P.K Nguyen, Vincent F. Yu, Dang Viet Anh Nguyen

Research Collection School Of Computing and Information Systems

Waste from electrical and electronic equipment (WEEE) or e-waste describes end-of-life electronic products that are discarded. Due to their toxic and negative impacts to humans' health, many publications have been proposed to handle, however, studies related to e-waste collection and transportation to waste disposal sites are not widely studied so far. This study proposes a mixed integer linear programming (MILP) model to solve the e-waste collecting problem by formulating it as the heterogeneous vehicle routing problem with multiple time windows (HVRPMTW). The model is validated with newly developed benchmark instances that are solved by commercial software, CPLEX. The model is …

An Adaptive Large Neighborhood Search For Heterogeneous Vehicle Routing Problem With Time Windows, Minh Pham Kien Nguyen, Aldy Gunawan, Vincent F. Yu, Mustafa Misir

Research Collection School Of Computing and Information Systems

The heterogeneous vehicle routing problem with time windows (HVRPTW) employs various vehicles with different capacities to serve upcoming pickup and delivery orders. We introduce a HVRPTW variant for reflecting the practical needs of crowd-shipping by considering the mass-rapid-transit stations, as the additional terminal points. A mixed integer linear programming model is formulated. An Adaptive Large Neighborhood Search based meta-heuristic is also developed by utilizing a basic probabilistic selection strategy, i.e. roulette wheel, and Simulated Annealing. The proposed approach is empirically evaluated on a new set of benchmark instances. The computational results revealed that ALNS shows its clear advantage on the …

Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. Nguyen, Pieter. Vansteenwegen

Research Collection School Of Computing and Information Systems

The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …

The 4th International Workshop On Talent And Management Computing (Tmc'2023): Editorial, Hengshu Zhu, Hui Xiong, Yong Ge, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To …

Investigation Of The Protein Components In A Periplasmic Mechanism Regulating Bacterial Morphology, Alexandra Pulliam

Electronic Theses and Dissertations

Salmonella is a leading bacterial cause of foodborne illness worldwide. During a previous study investigating the enzymes responsible for regulating cyclic-di-GMP concentrations, a mutant in the cyclic-di-GMP-specific phosphodiesterase STM3615 was identified that displayed a phenotype characterized by decreased survival on agar plates and a shorter bacterium length. I was able to determine that the periplasmic domain of STM3615 was responsible for this phenotype, not the enzymatic phosphodiesterase domain. Based upon a bioinformatic analysis of the protein, I then hypothesized that the periplasmic domain of STM3615 was interacting with a periplasmic protein to give rise to this phenotype. To identify this …

Extraction, Purification, And Characterization Of Potential Bioactive Compounds Produced By Janthinobacterium Lividum Tajx1901, Andy Elorm Agbakpo

Electronic Theses and Dissertations

Underexplored environments such as soil samples continue to be an untapped source of bacterial strains with great potential to produce secondary metabolites for medicinal applications. As a result, these microorganisms represent a broad and yet unknown reservoir of new strains capable of producing these novel compounds. The current research primarily seeks to perform the isolation, purification, and characterization of secondary metabolites from a soil bacterium (Janthinobacterium lividum TAJX1901). The isolated soil bacterium was successfully cultured on rich media agar plates, followed by extraction using methanol and chloroform. The purification methods utilized include flash column chromatography, preparative thin-layer chromatography, and …

Deep Weakly-Supervised Anomaly Detection, Guansong Pang, Chunhua Shen, Huidong Jin, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Recent semi-supervised anomaly detection methods that are trained using small labeled anomaly examples and large unlabeled data (mostly normal data) have shown largely improved performance over unsupervised methods. However, these methods often focus on fitting abnormalities illustrated by the given anomaly examples only (i.e., seen anomalies), and consequently they fail to generalize to those that are not, i.e., new types/classes of anomaly unseen during training. To detect both seen and unseen anomalies, we introduce a novel deep weakly-supervised approach, namely Pairwise Relation prediction Network (PReNet), that learns pairwise relation features and anomaly scores by predicting the relation of any two …

Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao

Research Collection School Of Computing and Information Systems

Nowadays, using AI-based detectors to keep pace with the fast iterating of malware has attracted a great attention. However, most AI-based malware detectors use features with vast sparse subspaces to characterize applications, which brings significant vulnerabilities to the model. To exploit this sparsityrelated vulnerability, we propose a clean-label backdoor attack consisting of a dissimilarity metric-based candidate selection and a variation ratio-based trigger construction. The proposed backdoor is verified on different datasets, including a Windows PE dataset, an Android dataset with numerical and boolean feature values, and a PDF dataset. The experimental results show that the attack can slash the accuracy …

Single-View View Synthesis With Self-Rectified Pseudo-Stereo, Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous …

Balancing Utility And Fairness In Submodular Maximization, Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang

Research Collection School Of Computing and Information Systems

Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications – including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the utility and fairness objectives are both desirable, they might contradict each other, and, to the best of our knowledge, …

Ai For Connectivism Learning: Undergraduate Students’ Experiences Of Chatgpt In Advanced Programming Courses, Swapna Gottipati, Kyong Jin Shim, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Advanced programming skills are required for computing courses on merging topics, and students often struggle to develop these skills to solve complex problems. To address this challenge, faculty members provide additional lectures, practice sessions, and educational technology tools. This paper discusses the challenges faced by computer science students in developing advanced programming skills and explores the use of AI chatbots, specifically ChatGPT, as a support tool for learning. We study the engagement and effectiveness of ChatGPT in helping students learn advanced programming skills using two engagement learning frameworks (CIE and MELT) for evaluation. The study involves designing a computing lab …

Multi-Granularity Detector For Vulnerability Fixes, Truong Giang Nguyen, Cong, Thanh Le, Hong Jin Kang, Ratnadira Widyasari, Chengran Yang, Zhipeng Zhao, Bowen Xu, Jiayuan Zhou, Xin Xia, Ahmed E. Hassan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and …