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2023

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Preventing Inferences Through Data Dependencies On Sensitive Data, Primal Pappachan, Shufan Zhang, Xi He, Sharad Mehrotra Dec 2023

Preventing Inferences Through Data Dependencies On Sensitive Data, Primal Pappachan, Shufan Zhang, Xi He, Sharad Mehrotra

Computer Science Faculty Publications and Presentations

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Inference control from data dependencies has been studied in the prior work. However, existing solutions either detect and deny queries which may lead to leakage – resulting in poor utility, or only protects against exact reconstruction of the sensitive data – resulting in poor security. In this paper, we present a novel security model called full deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for …

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The Effect Of Export-Related Measures On Indonesian Export Of Palm, Cpo, And Its Derivative Products, Edy Krismanto Sipayung, Christina Ruth Elisabeth Dec 2023

The Effect Of Export-Related Measures On Indonesian Export Of Palm, Cpo, And Its Derivative Products, Edy Krismanto Sipayung, Christina Ruth Elisabeth

Economics and Finance in Indonesia

This study investigated the effect of Export-Related Measures (ERMs) imposed by the Government of Indonesia on the export volume and value of palm, Crude Palm Oil (CPO), and its derivatives products utilizing the export data of Indonesia to 194 destination countries in the 2011–2020 period. This study combined the dataset of ERMs from the United Nations Conference on Trade and Development (UNCTAD) database and relevant Indonesian regulations. The results reveal that ERMs have a positive and significant effect on both the export volume and value of Indonesian palm, CPO, and its derivatives products. The price-raising effect or costs of ERMs …

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Implementation Of Hierarchical And K-Means Clustering Techniques On The Trend And Seasonality Components Of Temperature Profile Data, Emmanuel Ogedegbe Dec 2023

Implementation Of Hierarchical And K-Means Clustering Techniques On The Trend And Seasonality Components Of Temperature Profile Data, Emmanuel Ogedegbe

Electronic Theses and Dissertations

In this study, time series decomposition techniques are used in conjunction with Kmeans clustering and Hierarchical clustering, two well-known clustering algorithms, to climate data. Their implementation and comparisons are then examined. The main objective is to identify similar climate trends and group geographical areas with similar environmental conditions. Climate data from specific places are collected and analyzed as part of the project. The time series is then split into trend, seasonality, and residual components. In order to categorize growing regions according to their climatic inclinations, the deconstructed time series are then submitted to K-means clustering and Hierarchical clustering with dynamic …

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Computational Quantum Chemistry Studies Of The Stabilities Of Radical Adducts Formed During The Oxidation Of Melatonin Derivatives, James Horne Dec 2023

Computational Quantum Chemistry Studies Of The Stabilities Of Radical Adducts Formed During The Oxidation Of Melatonin Derivatives, James Horne

Electronic Theses and Dissertations

Melatonin is a natural antioxidant that has been investigated for properties as a potential spin trap to identify short-lived free radicals. Computational quantum chemistry studies have been performed for the oxidation of melatonin to N1-acetyl-N2-formyl-5-methoxykynuramine. This research focused on modification of melatonin into derivatives and analyzing the change in total molecular energy from melatonin to its oxidation product, as well as the corresponding derivatives. Each of the molecular geometries were optimized at the DFT/B3LYP/6-31G(d), DFT/B3LYP/cc-pVXZ (X = D, T), HF/6-31G(d), HF/cc-PVXZ (X = D, T), MP2/6-31G(d), and MP2/cc-PVXZ (X = D, T) levels of theory. …

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Early Pliocene Mice And Rats From The Gray Fossil Site Of Eastern Tennessee: Implications For The Evolution Of Cricetidae And Understanding Of The Past Ecosystem, Ziqi Xu Dec 2023

Early Pliocene Mice And Rats From The Gray Fossil Site Of Eastern Tennessee: Implications For The Evolution Of Cricetidae And Understanding Of The Past Ecosystem, Ziqi Xu

Electronic Theses and Dissertations

Cricetidae ranks as the second-most species-rich and abundant mammalian family, with limited studies on eastern North American records prior to the Pleistocene. While cricetids has been previously noted at the early Pliocene Gray Fossil Site (GFS), this study provides a detailed description of eight taxa: Postcopemys (two species), Symmetrodontomys, Oryzomyini, Peromyscus, Neotoma, Neotomodon, and Xenomys. Postcopemys is the most common cricetid taxon at GFS, followed by Peromyscus and Neotoma. These records expand the stratigraphic and geographic range of multiple genera. Distinctive morphological features of GFS taxa suggest presence of several new species. The …

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Thermodynamics And Kinetics Analyses Of Clc-Ec1 Dimerization Reaction In Lipid Bilayers, Taeho Lee Dec 2023

Thermodynamics And Kinetics Analyses Of Clc-Ec1 Dimerization Reaction In Lipid Bilayers, Taeho Lee

Arts & Sciences Graduate Student Theses and Dissertations

The efforts of this thesis are directed towards understanding the thermodynamics and kinetics underlying the dimerization reaction of a chloride/proton antiporter membrane protein, CLC-ec1, in lipid membranes. Due to the inherent challenges of working with membrane proteins, thermodynamic and kinetic information for the protein association reactions in membranes has been historically limited. The research presented in this thesis demonstrates the technological advancements for these measurements in a model membrane protein system, thereby setting an important milestone for the field. Since CLC-ec1 dimerizes via a greasy, membrane embedded binding interface, the physical driving forces involved in this reaction were unknown. It …

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Experimental And Theoretical Investigations Of Novel 2d Magnet Heterostructures And Magnetic Thin Films, Stephen Hofer Dec 2023

Experimental And Theoretical Investigations Of Novel 2d Magnet Heterostructures And Magnetic Thin Films, Stephen Hofer

Dissertations

Since the first usage of magnets as navigation tools, magnetism has been an integral part of human technology. As such, with increased demand for more sophisticated technologies, the need to understand magnetism and its applications has also increased. In modern times, the focus of new technologies is on scalability, or how things behave on the smallest scales. To this end it is necessary to understand the fundamental and practical behaviors of magnetic materials at this limit. Recent discoveries in the field of true 2D magnets and the field of topological matter demands investigation of the intersection between these two fields. …

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A New Approach To Synthetic Image Evaluation, Majid Memari Dec 2023

A New Approach To Synthetic Image Evaluation, Majid Memari

Dissertations

This study is dedicated to enhancing the effectiveness of Optical Character Recognition (OCR) systems, with a special emphasis on Arabic handwritten digit recognition. The choice to focus on Arabic handwritten digits is twofold: first, there has been relatively less research conducted in this area compared to its English counterparts; second, the recognition of Arabic handwritten digits presents more challenges due to the inherent similarities between different Arabic digits.OCR systems, engineered to decipher both printed and handwritten text, often face difficulties in accurately identifying low-quality or distorted handwritten text. The quality of the input image and the complexity of the text …

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Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl Dec 2023

Enhanced Privacy-Enabled Face Recognition Using Κ-Identity Optimization, Ryan Karl

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Facial recognition is becoming more and more prevalent in the daily lives of the common person. Law enforcement utilizes facial recognition to find and track suspects. The newest smartphones have the ability to unlock using the user's face. Some door locks utilize facial recognition to allow correct users to enter restricted spaces. The list of applications that use facial recognition will only increase as hardware becomes more cost-effective and more computationally powerful. As this technology becomes more prevalent in our lives, it is important to understand and protect the data provided to these companies. Any data transmitted should be encrypted …

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An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks Dec 2023

An Investigation Of Match For Lossless Video Compression, Brittany Sullivan-Reicks

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

A new lossless video compression technique, Match, is investigated. Match uses the similarity between the frames of a video or the slices of medical images to find a prediction for the current pixel. A portion of the previous frame is searched to find a matching context, which is the pixels surrounding the current pixel, within some distance centered on the current location. The best distance to use for each dataset is found experimentally. The matching context refers to the neighborhood of w, nw, n, and ne, where the pixel in the previous frame with the closest matching context becomes the …

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Post-Wildfire Export Regimes Of Solutes Along The Gallinas-Pecos River-Santa Rosa Fluvial Network, Asmita Kaphle Dec 2023

Post-Wildfire Export Regimes Of Solutes Along The Gallinas-Pecos River-Santa Rosa Fluvial Network, Asmita Kaphle

Civil Engineering ETDs

Wildfires significantly contribute to water pollution when ash and sediments, including nutrients and metals, are released into fluvial systems, impacting aquatic ecosystems and surrounding communities. This study focuses on the impacts of the Hermits Peak-Calf Canyon Fire on the Gallinas and Pecos watersheds. It employs five monitoring stations along a 190km fluvial network to assess solute export regimes post-fire, where grab samples were collected for nutrient and metal analyses for a period of more than a year. The metrics used for characterizing mobilization, chemostatic, or dilution patterns indicated mobilization for most solutes at all sites upstream of Santa Rosa Lake. …

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Godel, Escherian Staircase And Possibility Of Quantum Wormhole With Liquid Crystalline Phase Of Iced-Water - Part I: Theoretical Underpinning, Victor Christianto, T. Daniel Chandra, Florentin Smarandache Dec 2023

Godel, Escherian Staircase And Possibility Of Quantum Wormhole With Liquid Crystalline Phase Of Iced-Water - Part I: Theoretical Underpinning, Victor Christianto, T. Daniel Chandra, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

As a senior physicist colleague and our friend, Robert N. Boyd, wrote in a journal (JCFA, Vol. 1,. 2, 2022), Our universe is but one page in a large book [4]. For example, things and Beings can travel between Universes, intentionally or unintentionally. In this short remark, we revisit and offer short remark to Neil’s ideas and trying to connect them with geometrization of musical chords as presented by D. Tymoczko and others, then to Escher staircase and then to Jacob’s ladder which seems to point to possibility to interpret Jacob’s vision as described in the ancient book of Genesis …

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Godel, Escherian Staircase And Possibility Of Quantum Wormhole With Liquid Crystalline Phase Of Iced-Water - Part Ii: Experiment Description, Victor Christianto, T. Daniel Chandra, Florentin Smarandache Dec 2023

Godel, Escherian Staircase And Possibility Of Quantum Wormhole With Liquid Crystalline Phase Of Iced-Water - Part Ii: Experiment Description, Victor Christianto, T. Daniel Chandra, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

The present article was partly inspired by G. Pollack’s book, and also Dadoloff, Saxena & Jensen (2010). As a senior physicist colleague and our friend, Robert N. Boyd, wrote in a journal (JCFA, Vol. 1, No. 2, 2022), for example, things and Beings can travel between Universes, intentionally or unintentionally [4]. In this short remark, we revisit and offer short remark to Neil Boyd’s ideas and trying to connect them with geometry of musical chords as presented by D. Tymoczko and others, then to Escherian staircase and then to Jacob’s ladder which seems to pointto possibility to interpret Jacob’s vision …

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Extremum Seeking Control Algorithms For Extremely High Frequency Antenna System Pointing, Scott Shore Dec 2023

Extremum Seeking Control Algorithms For Extremely High Frequency Antenna System Pointing, Scott Shore

Mechanical Engineering ETDs

The research examines the application of extremum seeking control (ESC) algorithms to ground station antenna pointing for extremely high frequency (EHF) communication systems. ESC algorithms search for local maxima or minima by locating where an objective function gradient goes to zero. With wireless communication expanding into higher frequencies, the ground station pointing requirement is increasing. ESC presents a method which utilizes available equipment and information to perform ground station pointing. Additionally, ESC algorithms do not rely on assumptions or approximations needed for other techniques. The dissertation demonstrates ESC algorithm feasibility for the ground station pointing problem, benchmarks the ESC algorithms …

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Software Composition Analysis For Vulnerability Detection: An Empirical Study On Java Projects, Lida Zhao, Sen Chen, Zhengzi Xu, Lyuye Zhang, Jiahui Wu, Jun Sun, Yang Liu Dec 2023

Software Composition Analysis For Vulnerability Detection: An Empirical Study On Java Projects, Lida Zhao, Sen Chen, Zhengzi Xu, Lyuye Zhang, Jiahui Wu, Jun Sun, Yang Liu

Research Collection School Of Computing and Information Systems

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities introduced by open-source software (OSS) imported as third-party libraries (TPL). With the increasing complexity of software functionality, SCA tools may encounter various scenarios during the dependency resolution process, such as diverse formats of artifacts, diverse dependency imports, and diverse dependency specifications. However, there still lacks a comprehensive evaluation of SCA tools for Java that takes into account the above scenarios. This could lead to a confined interpretation of comparisons, improper use of tools, and hinder further improvements of the tools. To fill this gap, we proposed an Evaluation Model …

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The Value Of Official Website Information In The Credit Risk Evaluation Of Smes, Cuiqing Jiang, Chang Yin, Qian Tang, Zhao Wang Dec 2023

The Value Of Official Website Information In The Credit Risk Evaluation Of Smes, Cuiqing Jiang, Chang Yin, Qian Tang, Zhao Wang

Research Collection School Of Computing and Information Systems

The official websites of small and medium-sized enterprises (SMEs) not only reflect the willingness of an enterprise to disclose information voluntarily, but also can provide information related to the enterprises’ historical operations and performance. This research investigates the value of official website information in the credit risk evaluation of SMEs. To study the effect of different kinds of website information on credit risk evaluation, we propose a framework to mine effective features from two kinds of information disclosed on the official website of a SME—design-based information and content-based information—in predicting its credit risk. We select the SMEs in the software …

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Llm-Adapters: An Adapter Family For Parameter-Efficient Fine-Tuning Of Large Language Models, Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Ka-Wei Lee Dec 2023

Llm-Adapters: An Adapter Family For Parameter-Efficient Fine-Tuning Of Large Language Models, Zhiqiang Hu, Lei Wang, Yihuai Lan, Wanyu Xu, Ee-Peng Lim, Lidong Bing, Xing Xu, Soujanya Poria, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

The success of large language models (LLMs), like GPT-4 and ChatGPT, has led to the development of numerous cost-effective and accessible alternatives that are created by finetuning open-access LLMs with task-specific data (e.g., ChatDoctor) or instruction data (e.g., Alpaca). Among the various fine-tuning methods, adapter-based parameter-efficient fine-tuning (PEFT) is undoubtedly one of the most attractive topics, as it only requires fine-tuning a few external parameters instead of the entire LLMs while achieving comparable or even better performance. To enable further research on PEFT methods of LLMs, this paper presents LLMAdapters, an easy-to-use framework that integrates various adapters into LLMs and …

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Graph Contrastive Learning With Stable And Scalable Spectral Encoding, Deyu Bo, Yuan Fang, Yang Liu, Chuan Shi Dec 2023

Graph Contrastive Learning With Stable And Scalable Spectral Encoding, Deyu Bo, Yuan Fang, Yang Liu, Chuan Shi

Research Collection School Of Computing and Information Systems

Graph contrastive learning (GCL) aims to learn representations by capturing the agreements between different graph views. Traditional GCL methods generate views in the spatial domain, but it has been recently discovered that the spectral domain also plays a vital role in complementing spatial views. However, existing spectral-based graph views either ignore the eigenvectors that encode valuable positional information, or suffer from high complexity when trying to address the instability of spectral features. To tackle these challenges, we first design an informative, stable, and scalable spectral encoder, termed EigenMLP, to learn effective representations from the spectral features. Theoretically, EigenMLP is invariant …

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Rome: Evaluating Pre-Trained Vision-Language Models On Reasoning Beyond Visual Common Sense, Kankan Zhou, Eason Lai, Au Wei Bin Yeong, Kyriakos Mouratidis, Jing Jiang Dec 2023

Rome: Evaluating Pre-Trained Vision-Language Models On Reasoning Beyond Visual Common Sense, Kankan Zhou, Eason Lai, Au Wei Bin Yeong, Kyriakos Mouratidis, Jing Jiang

Research Collection School Of Computing and Information Systems

Humans possess a strong capability for reasoning beyond common sense. For example, given an unconventional image of a goldfish laying on the table next to an empty fishbowl, a human would effortlessly determine that the fish is not inside the fishbowl. The case, however, may be different for a vision-language model, whose reasoning could gravitate towards the common scenario that the fish is inside the bowl, despite the visual input. In this paper, we introduce a novel probing dataset named ROME (reasoning beyond commonsense knowledge) to evaluate whether the state-of-the-art pre-trained vision-language models have the reasoning capability to correctly interpret …

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Examining The Inter-Consistency Of Large Language Models: An In-Depth Analysis Via Debate, Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin Dec 2023

Examining The Inter-Consistency Of Large Language Models: An In-Depth Analysis Via Debate, Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have shown impressive capabilities in various applications, but they still face various inconsistency issues. Existing works primarily focus on the inconsistency issues within a single LLM, while we complementarily explore the inter-consistency among multiple LLMs for collaboration. To examine whether LLMs can collaborate effectively to achieve a consensus for a shared goal, we focus on commonsense reasoning, and introduce a formal debate framework (FORD) to conduct a three-stage debate among LLMs with real-world scenarios alignment: fair debate, mismatched debate, and roundtable debate. Through extensive experiments on various datasets, LLMs can effectively collaborate to reach a consensus …

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Learning To Search Feasible And Infeasible Regions Of Routing Problems With Flexible Neural K-Opt, Yining Ma, Zhiguang Cao, Yew Meng Chee Dec 2023

Learning To Search Feasible And Infeasible Regions Of Routing Problems With Flexible Neural K-Opt, Yining Ma, Zhiguang Cao, Yew Meng Chee

Research Collection School Of Computing and Information Systems

In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for routing problems. It learns to perform flexible k-opt exchanges based on a tailored action factorization method and a customized recurrent dual-stream decoder. As a pioneering work to circumvent the pure feasibility masking scheme and enable the autonomous exploration of both feasible and infeasible regions, we then propose the Guided Infeasible Region Exploration (GIRE) scheme, which supplements the NeuOpt policy network with feasibility-related features and leverages reward shaping to steer reinforcement learning more effectively. Besides, we further equip NeuOpt with dynamic data augmentations during inference for more …

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Exgen: Ready-To-Use Exercise Generation In Introductory Programming Courses, Nguyen Binh Duong Ta, Hua Gia Phuc Nguyen, Gottipati Swapna Dec 2023

Exgen: Ready-To-Use Exercise Generation In Introductory Programming Courses, Nguyen Binh Duong Ta, Hua Gia Phuc Nguyen, Gottipati Swapna

Research Collection School Of Computing and Information Systems

In introductory programming courses, students as novice programmers would benefit from doing frequent practices set at a difficulty level and concept suitable for their skills and knowledge. However, setting many good programming exercises for individual learners is very time-consuming for instructors. In this work, we propose an automated exercise generation system, named ExGen, which leverages recent advances in pre-trained large language models (LLMs) to automatically create customized and ready-to-use programming exercises for individual students ondemand. The system integrates seamlessly with Visual Studio Code, a popular development environment for computing students and software engineers. ExGen effectively does the following: 1) maintaining …

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Spatial-Temporal Episodic Memory Modeling For Adls: Encoding, Retrieval, And Prediction, Xinjing Song, Di Wang, Chai Quek, Ah-Hwee Tan, Yanjiang Wang Dec 2023

Spatial-Temporal Episodic Memory Modeling For Adls: Encoding, Retrieval, And Prediction, Xinjing Song, Di Wang, Chai Quek, Ah-Hwee Tan, Yanjiang Wang

Research Collection School Of Computing and Information Systems

Activities of daily living (ADLs) relate to people’s daily self-care activities, which reflect their living habits and lifestyle. A prior study presented a neural network model called STADLART for ADL routine learning. In this paper, we propose a cognitive model named Spatial-Temporal Episodic Memory for ADL (STEM-ADL), which extends STADLART to encode event sequences in the form of distributed episodic memory patterns. Specifically, STEM-ADL encodes each ADL and its associated contextual information as an event pattern and encodes all events in a day as an episode pattern. By explicitly encoding the temporal characteristics of events as activity gradient patterns, STEM-ADL …

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Generalized Logit Adjustment: Calibrating Fine-Tuned Models By Removing Label Bias In Foundation Models, Beier Zhu, Kaihua Tang, Qianru Sun, Hanwang Zhang Dec 2023

Generalized Logit Adjustment: Calibrating Fine-Tuned Models By Removing Label Bias In Foundation Models, Beier Zhu, Kaihua Tang, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Foundation models like CLIP allow zero-shot transfer on various tasks without additional training data. Yet, the zero-shot performance is less competitive than a fully supervised one. Thus, to enhance the performance, fine-tuning and ensembling are also commonly adopted to better fit the downstream tasks. However, we argue that such prior work has overlooked the inherent biases in foundation models. Due to the highly imbalanced Web-scale training set, these foundation models are inevitably skewed toward frequent semantics, and thus the subsequent fine-tuning or ensembling is still biased. In this study, we systematically examine the biases in foundation models and demonstrate the …

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Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra Dec 2023

Mrim: Lightweight Saliency-Based Mixed-Resolution Imaging For Low-Power Pervasive Vision, Jiyan Wu, Vithurson Subasharan, Minh Anh Tuan Tran, Kasun Pramuditha Gamlath, Archan Misra

Research Collection School Of Computing and Information Systems

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes …

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Transformer-Based Multi-Task Learning For Crisis Actionability Extraction, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint Dec 2023

Transformer-Based Multi-Task Learning For Crisis Actionability Extraction, Yuhao Zhang, Siaw Ling Lo, Phyo Yi Win Myint

Research Collection School Of Computing and Information Systems

Social media has become a valuable information source for crisis informatics. While various methods were proposed to extract relevant information during a crisis, their adoption by field practitioners remains low. In recent fieldwork, actionable information was identified as the primary information need for crisis responders and a key component in bridging the significant gap in existing crisis management tools. In this paper, we proposed a Crisis Actionability Extraction System for filtering, classification, phrase extraction, severity estimation, localization, and aggregation of actionable information altogether. We examined the effectiveness of transformer-based LSTM-CRF architecture in Twitter-related sequence tagging tasks and simultaneously extracted actionable …

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Distxplore: Distribution-Guided Testing For Evaluating And Enhancing Deep Learning Systems, Longtian Wang, Xiaofei Xie, Xiaoning Du, Meng Tian, Qing Guo, Zheng Yang, Chao Shen Dec 2023

Distxplore: Distribution-Guided Testing For Evaluating And Enhancing Deep Learning Systems, Longtian Wang, Xiaofei Xie, Xiaoning Du, Meng Tian, Qing Guo, Zheng Yang, Chao Shen

Research Collection School Of Computing and Information Systems

Deep learning (DL) models are trained on sampled data, where the distribution of training data differs from that of real-world data (i.e., the distribution shift), which reduces the model's robustness. Various testing techniques have been proposed, including distribution-unaware and distribution-aware methods. However, distribution-unaware testing lacks effectiveness by not explicitly considering the distribution of test cases and may generate redundant errors (within same distribution). Distribution-aware testing techniques primarily focus on generating test cases that follow the training distribution, missing out-of-distribution data that may also be valid and should be considered in the testing process. In this paper, we propose a novel …

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Flowpg: Action-Constrained Policy Gradient With Normalizing Flows, Brahmanage Janaka Chathuranga Thilakarathna, Jiajing Ling, Akshat Kumar Dec 2023

Flowpg: Action-Constrained Policy Gradient With Normalizing Flows, Brahmanage Janaka Chathuranga Thilakarathna, Jiajing Ling, Akshat Kumar

Research Collection School Of Computing and Information Systems

Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-critical and resource-allocation related decision making problems. A major challenge in ACRL is to ensure agent taking a valid action satisfying constraints in each RL step. Commonly used approach of using a projection layer on top of the policy network requires solving an optimization program which can result in longer training time, slow convergence, and zero gradient problem. To address this, first we use a normalizing flow model to learn an invertible, differentiable mapping between the feasible action space and the support of a simple distribution on a latent variable, …

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On The Usage Of Continual Learning For Out-Of-Distribution Generalization In Pre-Trained Language Models Of Code, Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, Houari A. Sahraoui Dec 2023

On The Usage Of Continual Learning For Out-Of-Distribution Generalization In Pre-Trained Language Models Of Code, Martin Weyssow, Xin Zhou, Kisub Kim, David Lo, Houari A. Sahraoui

Research Collection School Of Computing and Information Systems

Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the effectiveness and robustness of PLMs. In particular, world-realistic scenarios potentially lead to significant differences between the distribution of the pre-training and test data, i.e., distribution shift, resulting in a degradation of the PLM's performance on downstream tasks. In this paper, we stress the need for adapting PLMs of code to software data whose …

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Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin Dec 2023

Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in this work, we propose a learning-based approach namely SnapVuln, which first utilizes multiple vulnerability-specific inter-procedural slicing algorithms to capture vulnerability semantics of various types and then employs a Gated Graph Neural Network (GGNN) with an attention mechanism to learn vulnerability semantics. We compare SnapVuln with state-of-the-art learning-based approaches on two public datasets, and confirm that SnapVuln outperforms them. We further perform an ablation study …

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