Computer Sciences Commons^™

How To Resuscitate A Sick Vm In The Cloud, Xuhua Ding

Research Collection School Of Computing and Information Systems

A guest virtual machine in a cloud platform may fall “sick” when its kernel encounters a fatal low-level bug or is subverted by an adversary. The VM owner is hence likely to lose her control over it due to a kernel hang or being denied of remote accesses. While the VM can be rebooted with the assistance from the cloud server, the owner not only faces service disruption but also is left with no opportunity to make an in-depth diagnosis and forensics on the spot, not to mention a live rectification. Currently, the cloud service provider has neither incentive nor …

Semantic Scene Completion With Cleaner Self, Fengyun Wang, Dong Zhang, Hanwang Zhang, Jinhui Tang, Qianru Sun

Research Collection School Of Computing and Information Systems

Semantic Scene Completion (SSC) transforms an image of single-view depth and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a well-known ill-posed problem as the prediction model has to “imagine” what is behind the visible surface, which is usually represented by Truncated Signed Distance Function (TSDF). Due to the sensory imperfection of the depth camera, most existing methods based on the noisy TSDF estimated from depth values suffer from 1) incomplete volumetric predictions and 2) confused semantic labels. To this end, we use the ground-truth 3D voxels to generate a perfect visible surface, …

Unbiased Multiple Instance Learning For Weakly Supervised Video Anomaly Detection, Hui Lyu, Zhongqi Yue, Qianru Sun, Bin Luo, Zhen Cui, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level predictions. So, Multiple Instance Learning (MIL) is prevailing in WSVAD. However, MIL is notoriously known to suffer from many false alarms because the snippet-level detector is easily biased towards the abnormal snippets with simple context, confused by the normality with the same bias, and missing the anomaly with a different pattern. To this end, we propose a new MIL framework: Unbiased MIL (UMIL), to learn unbiased anomaly features that improve WSVAD. At each MIL training …

Livoauth: Liveness Detection In Voiceprint Authentication With Random Challenges And Detection Modes, Rui Zhang, Zheng Yan, Xueru Wang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Voiceprint authentication provides great convenience to users in many application scenarios. However, it easily suffers from spoofing attacks including speech synthesis, speech conversion, and speech replay. Liveness detection is an effective way to resist these attacks. But existing methods suffer from many disadvantages, such as extra deployment costs due to precise data collection, environmental disturbance, high computational overhead, and operational complexity. A uniform platform that can offer voiceprint authentication as a service (VAaS) over the cloud is also lacked. Hence, it is imperative to design an economic and effective method for liveness detection in voiceprint authentication. In this article, we …

Evading Deepfake Detectors Via Adversarial Statistical Consistency, Yang Hou, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Jianjun Zhao

Research Collection School Of Computing and Information Systems

In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds, more and more DeepFake detection techniques have been proposed. These methods typically rely on detecting statistical differences between natural (i.e., real) and DeepFake-generated images in both spatial and frequency domains. In this work, we propose to explicitly minimize the statistical differences to evade state-of-the-art DeepFake detectors. To this end, we propose a statistical consistency attack (StatAttack) against DeepFake detectors, which contains two main parts. First, we select several statistical-sensitive natural degradations (i.e., exposure, blur, and noise) and add them to the fake images …

The Bemi Stardust: A Structured Ensemble Of Binarized Neural Networks, Ambrogio Maria Bernardelli, Stefano Gualandi, Hoong Chuin Lau, Simone Milanesi

Research Collection School Of Computing and Information Systems

Binarized Neural Networks (BNNs) are receiving increasing attention due to their lightweight architecture and ability to run on low-power devices, given the fact that they can be implemented using Boolean operations. The state-of-the-art for training classification BNNs restricted to few-shot learning is based on a Mixed Integer Programming (MIP) approach. This paper proposes the BeMi ensemble, a structured architecture of classification-designed BNNs based on training a single BNN for each possible pair of classes and applying a majority voting scheme to predict the final output. The training of a single BNN discriminating between two classes is achieved by a MIP …

Knowledge Compilation For Constrained Combinatorial Action Spaces In Reinforcement Learning, Jiajing Ling, Moritz Lukas Schuler, Akshat Kumar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

Action-constrained reinforcement learning (ACRL), where any action taken in a state must satisfy given constraints, has several practical applications such as resource allocation in supply-demand matching, and path planning among others. A key challenge is to enforce constraints when the action space is discrete and combinatorial. To address this, first, we assume an action is represented using propositional variables, and action constraints are represented using Boolean functions. Second, we compactly encode the set of all valid actions that satisfy action constraints using a probabilistic sentential decision diagram (PSDD), a recently proposed knowledge compilation framework. Parameters of the PSDD compactly encode …

Enhancing Third-Party Software Reliability Through Bug Bounty Programs, Tianlu Zhou, Dan Ma, Nan Feng

Research Collection School Of Computing and Information Systems

Bug Bounty Programs (BBPs) reward external hackers for identifying and reporting software vulnerabilities. As the number of security issues caused by third-party applications has been significantly increased recently, many digital platforms are considering launching BBPs to help enhance the reliability of third-party software. BBPs bring benefits to the platform and vendors, meanwhile impose additional costs on them as well. As a result, the overall impact of using BBP is unclear. In this paper, we present an analytical model to examine the strategic decisions of launching and participating in a BBP for the platform and the third-party vendor, respectively. We find …

Gnnlens: A Visual Analytics Approach For Prediction Error Diagnosis Of Graph Neural Networks., Zhihua Jin, Yong Wang, Qianwen Wang, Yao Ming, Tengfei Ma, Huamin Qu

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) aim to extend deep learning techniques to graph data and have achieved significant progress in graph analysis tasks (e.g., node classification) in recent years. However, similar to other deep neural networks like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), GNNs behave like a black box with their details hidden from model developers and users. It is therefore difficult to diagnose possible errors of GNNs. Despite many visual analytics studies being done on CNNs and RNNs, little research has addressed the challenges for GNNs. This paper fills the research gap with an interactive visual analysis …

Invalidator: Automated Patch Correctness Assessment Via Semantic And Syntactic Reasoning, Tranh Le-Cong, Duc Minh Luong, Xuan Bach D. Le, David Lo, Nhat-Hoa Tran, Bui Quang-Huy, Quyet-Thang Huynh

Research Collection School Of Computing and Information Systems

Automated program repair (APR) has been gaining ground recently. However, a significant challenge that still remains is test overfitting, in which APR-generated patches plausibly pass the validation test suite but fail to generalize. A common practice to assess the correctness of APR-generated patches is to judge whether they are equivalent to ground truth, i.e., developer-written patches, by either generating additional test cases or employing human manual inspections. The former often requires the generation of at least one test that shows behavioral differences between the APR-patched and developer-patched programs. Searching for this test, however, can be difficult as the search space …

Venus: A Geometrical Representation For Quantum State Visualization, Shaolun Ruan, Ribo Yuan, Qiang Guan, Yanna Lin, Ying Mao, Weiwen Jiang, Zhepeng Wang, Wei Xu, Yong Wang

Research Collection School Of Computing and Information Systems

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum …

Privacy-Preserving Ranked Spatial Keyword Query In Mobile Cloud-Assisted Fog Computing, Qiuyun Tong, Yinbin Li Miao, Ximeng Liu, Robert H. Deng, Robert H. Deng

Research Collection School Of Computing and Information Systems

With the increasing popularity of GPS-equipped mobile devices in cloud-assisted fog computing scenarios, massive spatio-textual data is generated and outsourced to cloud servers for storage and analysis. Existing privacy-preserving range query or ranked keyword search schemes does not support a unified index, and are just applicable for the symmetric environment where all users sharing the same secret key. To solve this issue, we propose a Privacy-preserving Ranked Spatial keyword Query in mobile cloud-assisted Fog computing (PRSQ-F). Specifically, we design a novel comparable product encoding strategy that combines both spatial and textual conditions tightly to retrieve the objects in query range …

Deep Learning Recommendations For The Acl2 Interactive Theorem Prover, Robert K. Thompson, Robert K. Thompson

Master's Theses

Due to the difficulty of obtaining formal proofs, there is increasing interest in partially or completely automating proof search in interactive theorem provers. Despite being a theorem prover with an active community and plentiful corpus of 170,000+ theorems, no deep learning system currently exists to help automate theorem proving in ACL2. We have developed a machine learning system that generates recommendations to automatically complete proofs. We show that our system benefits from the copy mechanism introduced in the context of program repair. We make our system directly accessible from within ACL2 and use this interface to evaluate our system in …

An Empirical Evaluation Of Neural Process Meta-Learners For Financial Forecasting, Kevin G. Patel

Master's Theses

Challenges of financial forecasting, such as a dearth of independent samples and non- stationary underlying process, limit the relevance of conventional machine learning towards financial forecasting. Meta-learning approaches alleviate some of these is- sues by allowing the model to generalize across unrelated or loosely related tasks with few observations per task. The neural process family achieves this by con- ditioning forecasts based on a supplied context set at test time. Despite promise, meta-learning approaches remain underutilized in finance. To our knowledge, ours is the first application of neural processes to realized volatility (RV) forecasting and financial forecasting in general.

We …

Joint Beamforming And Phase Shift Design For Hybrid Irs And Uav-Aided Directional Modulation Networks, Rongen Dong, Hangjia He, Feng Shu, Qi Zhang, Riqing Chen, Shihao Yan, Jiangzhou Wang

Research outputs 2022 to 2026

Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme …

Star-Ris-Uav-Aided Coordinated Multipoint Cellular System For Multi-User Networks, Baihua Shi, Yang Wang, Danqi Li, Wenlong Cai, Jinyong Lin, Shuo Zhang, Weiping Shi, Shihao Yan, Feng Shu

Research outputs 2022 to 2026

Different from conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit signals to the receiver. In this paper, to serve more ground users and increase deployment flexibility, we investigate an unmanned aerial vehicle (UAV) equipped with STAR-RIS (STAR-RIS-UAV)-aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV-aided coordinated multipoint (CoMP) cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficient matrices are optimized. Specifically, …

Two Rapid Power Iterative Doa Estimators For Uav Emitter Using Massive/Ultra-Massive Receive Array, Yiwen Chen, Qijuan Jie, Yiqiao Zhang, Feng Shu, Xichao Zhan, Shihao Yan, Wenlong Cai, Xuehui Wang, Zhongwen Sun, Peng Zhang, Peng Chen

Research outputs 2022 to 2026

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance …

Stream-Evolving Bot Detection Framework Using Graph-Based And Feature-Based Approaches For Identifying Social Bots On Twitter, Eiman Alothali

Dissertations

This dissertation focuses on the problem of evolving social bots in online social networks, particularly Twitter. Such accounts spread misinformation and inflate social network content to mislead the masses. The main objective of this dissertation is to propose a stream-based evolving bot detection framework (SEBD), which was constructed using both graph- and feature-based models. It was built using Python, a real-time streaming engine (Apache Kafka version 3.2), and our pretrained model (bot multi-view graph attention network (Bot-MGAT)). The feature-based model was used to identify predictive features for bot detection and evaluate the SEBD predictions. The graph-based model was used to …

Blockchain-Enabled Ehr Sharing In Healthcare Federation: Sharding And Interblockchain Communication, Faiza Hashim

Dissertations

Electronic Health Records (EHRs) are crucial components of the healthcare system, facilitating accurate and efficient diagnosis. Blockchain technology has emerged as a promising solution to improve EHRs sharing among medical practitioners while ensuring privacy and security. By leveraging its decentralized, distributed, immutable, and secure architecture, blockchain has the potential to revolutionize the healthcare system. However, due to security concerns, blockchain networks in healthcare typically operate in private or consortium modes, resulting in isolated networks within a federation. Scalability remains a significant challenge for blockchain networks, as the number of participating nodes increases within each network of the federation. Consensus mechanisms …

Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Böther, Daniel Martin Katz

Research Collection Yong Pung How School Of Law

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce textbased and graph-based methods to identify instances of law smells, confirming their utility in …

Law Smells: Defining And Detecting Problematic Patterns In Legal Drafting, Corinna Coupette, Dirk Hartung, Janis Beckedorf, Maximilian Bother, Daniel Martin Katz

Research Collection Yong Pung How School Of Law

Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in …

A System Dynamics Approach To Evaluate Advanced Persistent Threat Vectors, Mathew Nicho, Christopher D. Mcdermott, Hussein Fakhry, Shini Girija

All Works

Cyber-attacks targeting high-profile entities are focused, persistent, and employ common vectors with varying levels of sophistication to exploit social-technical vulnerabilities. Advanced persistent threats (APTs) deploy zero-day malware against such targets to gain entry through multiple security layers, exploiting the dynamic interplay of vulnerabilities in the target network. System dynamics (SD) offers an alternative approach to analyze non-linear, complex, and dynamic social-technical systems. This research applied SD to three high-profile APT attacks - Equifax, Carphone, and Zomato - to identify and simulate socio-technical variables leading to breaches. By modeling APTs using SD, managers can evaluate threats, predict attacks, and reduce damage …

Machine Learning And Network Embedding Methods For Gene Co-Expression Networks, Niloofar Aghaieabiane

Dissertations

High-throughput technologies such as DNA microarrays and RNA-seq are used to measure the expression levels of large numbers of genes simultaneously. To support the extraction of biological knowledge, individual gene expression levels are transformed into Gene Co-expression Networks (GCNs). GCNs are analyzed to discover gene modules. GCN construction and analysis is a well-studied topic, for nearly two decades. While new types of sequencing and the corresponding data are now available, the software package WGCNA and its most recent variants are still widely used, contributing to biological discovery.

The discovery of biologically significant modules of genes from raw expression data is …

Trustworthy Machine Learning Through The Lens Of Privacy And Security, Thi Kim Phung Lai

Dissertations

Nowadays, machine learning (ML) becomes ubiquitous and it is transforming society. However, there are still many incidents caused by ML-based systems when ML is deployed in real-world scenarios. Therefore, to allow wide adoption of ML in the real world, especially in critical applications such as healthcare, finance, etc., it is crucial to develop ML models that are not only accurate but also trustworthy (e.g., explainable, privacy-preserving, secure, and robust). Achieving trustworthy ML with different machine learning paradigms (e.g., deep learning, centralized learning, federated learning, etc.), and application domains (e.g., computer vision, natural language, human study, malware systems, etc.) is challenging, …

Mapping Programs To Equations, Hessamaldin Mohammadi

Dissertations

Extracting the function of a program from a static analysis of its source code is a valuable capability in software engineering; at a time when there is increasing talk of using AI (Artificial Intelligence) to generate software from natural language specifications, it becomes increasingly important to determine the exact function of software as written, to figure out what AI has understood the natural language specification to mean. For all its criticality, the ability to derive the domain-to-range function of a program has proved to be an elusive goal, due primarily to the difficulty of deriving the function of iterative statements. …

Deep Hybrid Modeling Of Neuronal Dynamics Using Generative Adversarial Networks, Soheil Saghafi

Dissertations

Mechanistic modeling and machine learning methods are powerful techniques for approximating biological systems and making accurate predictions from data. However, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. This dissertation constructs Deep Hybrid Models that address these shortcomings by combining deep learning with mechanistic modeling. In particular, this dissertation uses Generative Adversarial Networks (GANs) to provide an inverse mapping of data to mechanistic models and identifies the distributions of mechanistic model parameters coherent to the data.

Chapter 1 provides background information on …

Neutrosophic Mcdm Methodology For Risk Assessment Of Autonomous Underwater Vehicles, Shimaa S. Mohamed, Ahmed Abdel-Monem, Alshaimaa A. Tantawy

Neutrosophic Systems with Applications

Due to its usefulness in several industries and the military, researchers have concentrated on developing autonomous underwater vehicles (AUVs). However, AUV navigation continues to be a difficult challenge to solve owing to the variety of underwater settings. The usage of AUVs, or autonomous underwater vehicles, is not without dangers like malfunction, ecological risks, loss of communications, cybersecurity risks, collisions, and others. There are many criteria to assess these risks technical, operational, economic, and regulatory. So, the methods of multi-criteria decision-making (MCDM) is used to deal with these various criteria. The analytical hierarchy process (AHP) method is an MCDM methodology, that …

Domain Decomposition Methods For Linear And Non-Linear Elliptic Problems, Tadanaga Takahashi

Dissertations

The primary purpose of this dissertation is to expand upon the circle of domain decomposition methods (DDM) which are algorithms that reformulate a boundary value problem in terms of multiple localized problems on subdomains. The first project involves expanding upon DDMs in a relatively mature field: the Helmholtz equation for wave scattering applications. The proposed method is an adaptation of a continuous cross-point Finite Element Non-overlapping DDM algorithm. The usual unbounded computational domain is truncated and then the near-field wave pattern is solved with a parallelized finite element method. Several improvements over the standard transmission operator are discussed in this …

What Effects Do Large Language Models Have On Cybersecurity, Josiah Marshall

Cybersecurity Undergraduate Research Showcase

Large Language Models (LLMs) are artificial intelligence (AI) tools that can process, summarize, and translate texts and predict future words in a sentence, letting the LLM generate sentences similar to how humans talk and write. One concern that needs to be flagged is that, often, the content generated by different LLMs is inaccurate. LLMs are trained on code that can be used to detect data breaches, detect ransomware, and even pinpoint organizational vulnerabilities in advance of a cyberattack. LLMs are new but have unbelievable potential with their ability to generate code that brings awareness to cyber analysts and IT professionals. …

Automated Approaches To Enable Innovative Civic Applications From Citizen Generated Imagery, Hye Seon Yi

USF Tampa Graduate Theses and Dissertations

Smart governance is an area, that is increasingly becoming important, not only in advanced countries, but all across the globe. Thanks to global scale network connectivity, permeance of smart-devices of various form-factors, and overall improvement in digital literary, we are now seeing "smartness" everywhere, or if not, the general public is expecting the same. Ultimately, the goal of smart governance is to facilitate state-of-the-art technologies to improve citizens’ lives. With the ubiquity of smart phone technologies today, citizens more readily participate in collaboration with public officials for improved quality of life and their communities. By utilizing optimal tools, public officials …