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Articles 2401 - 2430 of 3699
Full-Text Articles in Computer Sciences
An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan
An Intelligent Framework Towards Fully Autonomous Driving Fueled By Smart Roads, Muhammad Jalal Khan
Dissertations
Autonomous Vehicles (AVs) are transforming next-generation autonomous mobility. These vehicles promise to increase road safety, improve traffic efficiency, reduce vehicle emissions, and enhance overall mobility. They achieve higher levels of Autonomous Driving (AD) by integrating sensors, communication technologies, computation, and Artificial Intelligence (AI). Apart from these advancements, significant gaps remain in integrating heterogeneous technologies and disciplines essential for optimizing AD. Therefore, the current solution approaches lack the capability to exploit intelligent road infrastructures and effectively orchestrate perceptual services for complex driving scenarios. The main objective of this dissertation is to address these challenges by proposing a novel end-to-end intelligent framework …
Enhancing Health Analytics: Secure And Private Federated Learning Solutions, Nisha Thorakkattu Madathil
Enhancing Health Analytics: Secure And Private Federated Learning Solutions, Nisha Thorakkattu Madathil
Dissertations
Federated Learning (FL) is a collaborative method allowing individuals to train a model jointly without sharing their local datasets. It utilizes decentralized data sources to protect privacy, making it particularly promising in medical contexts where data confidentiality is paramount. FL facilitates the use of diverse datasets from various healthcare organizations while upholding patient confidentiality. It also plays a crucial role in advancing medical research and healthcare services while adhering to data distribution and compliance requirements. The primary challenges within federated healthcare encompass privacy preservation among sensitive distributed data, ensuring efficient communication, addressing data heterogeneity, and ultimately guaranteeing model accuracy. To …
Continual Normalization: Rethinking Batch Normalization For Online Continual Learning, Quang Pham, Chenghao Liu, Steven Hoi
Continual Normalization: Rethinking Batch Normalization For Online Continual Learning, Quang Pham, Chenghao Liu, Steven Hoi
Research Collection School Of Computing and Information Systems
Existing continual learning methods use Batch Normalization (BN) to facilitate training and improve generalization across tasks. However, the non-i.i.d and non-stationary nature of continual learning data, especially in the online setting, amplify the discrepancy between training and testing in BN and hinder the performance of older tasks. In this work, we study the cross-task normalization effect of BN in online continual learning where BN normalizes the testing data using moments biased towards the current task, resulting in higher catastrophic forgetting. This limitation motivates us to propose a simple yet effective method that we call Continual Normalization (CN) to facilitate training …
Development Of An Explainable Artificial Intelligence Model For Asian Vascular Wound Images, Zhiwen Joseph Lo, Malcolm Han Wen Mak, Shanying Liang, Yam Meng Chan, Cheng Cheng Goh, Tina Peiting Lai, Audrey Hui Min Tan, Patrick Thng, Patrick Thng, Tillman Weyde, Sylvia Smit
Development Of An Explainable Artificial Intelligence Model For Asian Vascular Wound Images, Zhiwen Joseph Lo, Malcolm Han Wen Mak, Shanying Liang, Yam Meng Chan, Cheng Cheng Goh, Tina Peiting Lai, Audrey Hui Min Tan, Patrick Thng, Patrick Thng, Tillman Weyde, Sylvia Smit
Research Collection School Of Computing and Information Systems
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into …
Environmental, Social, And Governance (Esg) And Artificial Intelligence In Finance: State-Of-The-Art And Research Takeaways, Tristan Lim
Research Collection School Of Computing and Information Systems
The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical …
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Research Collection School Of Computing and Information Systems
The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …
Marco: A Stochastic Asynchronous Concolic Explorer, Jie Hu, Yue Duan, Heng Yin
Marco: A Stochastic Asynchronous Concolic Explorer, Jie Hu, Yue Duan, Heng Yin
Research Collection School Of Computing and Information Systems
Concolic execution is a powerful program analysis technique for code path exploration. Despite recent advances that greatly improved the efficiency of concolic execution engines, path constraint solving remains a major bottleneck of concolic testing. An intelligent scheduler for inputs/branches becomes even more crucial. Our studies show that the previously under-studied branch-flipping policy adopted by state-of-the-art concolic execution engines has several limitations. We propose to assess each branch by its potential for new code coverage from a global view, concerning the path divergence probability at each branch. To validate this idea, we implemented a prototype Marco and evaluated it against the …
Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen
Acav: A Framework For Automatic Causality Analysis In Autonomous Vehicle Accident Recordings, Huijia Sun, Christopher M. Poskitt, Yang Sun, Jun Sun, Yuqi Chen
Research Collection School Of Computing and Information Systems
The rapid progress of autonomous vehicles (AVs) has brought the prospect of a driverless future closer than ever. Recent fatalities, however, have emphasized the importance of safety validation through large-scale testing. Multiple approaches achieve this fully automatically using high-fidelity simulators, i.e., by generating diverse driving scenarios and evaluating autonomous driving systems (ADSs) against different test oracles. While effective at finding violations, these approaches do not identify the decisions and actions that caused them -- information that is critical for improving the safety of ADSs. To address this challenge, we propose ACAV, an automated framework designed to conduct causality analysis for …
Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Qi Guo, Shangqing Liu, Junming Cao, Xiaohong Li, Xin Peng, Xiaofei Xie, Bihuan Chen
Exploring The Potential Of Chatgpt In Automated Code Refinement: An Empirical Study, Qi Guo, Shangqing Liu, Junming Cao, Xiaohong Li, Xin Peng, Xiaofei Xie, Bihuan Chen
Research Collection School Of Computing and Information Systems
Code review is an essential activity for ensuring the quality and maintainability of software projects. However, it is a time-consuming and often error-prone task that can significantly impact the development process. Recently, ChatGPT, a cutting-edge language model, has demonstrated impressive performance in various natural language processing tasks, suggesting its potential to automate code review processes. However, it is still unclear how well ChatGPT performs in code review tasks. To fill this gap, in this paper, we conduct the first empirical study to understand the capabilities of ChatGPT in code review tasks, specifically focusing on automated code refinement based on given …
W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy
W4-Groups: Modeling The Who, What, When And Where Of Group Behavior Via Mobility Sensing, Akansha Atrey, Camellia Zakaria, Rajesh Krishna Balan, Prashant Shenoy
Research Collection School Of Computing and Information Systems
Human social interactions occur in group settings of varying sizes and locations, depending on the type of social activity. The ability to distinguish group formations based on their purposes transforms how group detection mechanisms function. Not only should such tools support the effective detection of serendipitous encounters, but they can derive categories of relation types among users. Determining who is involved, what activity is performed, and when and where the activity occurs are critical to understanding group processes in greater depth, including supporting goal-oriented applications (e.g., performance, productivity, and mental health) that require sensing social factors. In this work, we …
Improving Automated Code Reviews: Learning From Experience, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet
Improving Automated Code Reviews: Learning From Experience, Hong Yi Lin, Patanamon Thongtanunam, Christoph Treude, Wachiraphan Charoenwet
Research Collection School Of Computing and Information Systems
Modern code review is a critical quality assurance process that is widely adopted in both industry and open source software environments. This process can help newcomers learn from the feedback of experienced reviewers; however, it often brings a large workload and stress to reviewers. To alleviate this burden, the field of automated code reviews aims to automate the process, teaching large language models to provide reviews on submitted code, just as a human would. A recent approach pre-trained and fine-tuned the code intelligent language model on a large-scale code review corpus. However, such techniques did not fully utilise quality reviews …
Bidirectional Paper-Repository Tracing In Software Engineering, Daniel Garijo, Miguel Arroyo, Esteban González Guardia, Christoph Treude, Nicola Tarocco
Bidirectional Paper-Repository Tracing In Software Engineering, Daniel Garijo, Miguel Arroyo, Esteban González Guardia, Christoph Treude, Nicola Tarocco
Research Collection School Of Computing and Information Systems
While computer science papers frequently include their associated code repositories, establishing a clear link between papers and their corresponding implementations may be challenging due to the number of code repositories used in research publications. In this paper we describe a lightweight method for effectively identifying bidirectional links between papers and repositories from both LaTeX and PDF sources. We have used our approach to analyze more than 14000 PDF and Latex files in the Software Engineering category of Arxiv, generating a dataset of more than 1400 paper-code implementations and assessing current citation practices on it.
Extracting Relevant Test Inputs From Bug Reports For Automatic Test Case Generation, Wendkuuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawende F. Bissyandé
Extracting Relevant Test Inputs From Bug Reports For Automatic Test Case Generation, Wendkuuni C. Ouédraogo, Laura Plein, Kader Kaboré, Andrew Habib, Jacques Klein, David Lo, Tegawende F. Bissyandé
Research Collection School Of Computing and Information Systems
The pursuit of automating software test case generation, particularly for unit tests, has become increasingly important due to the labor-intensive nature of manual test generation [6]. However, a significant challenge in this domain is the inability of automated approaches to generate relevant inputs, which compromises the efficacy of the tests [6].
The Impact Of Bug Localization Based On Crash Report Mining: A Developers' Perspective, Marcos Medeiros, Uirá Kulesza, Roberta Coelho, Rodrigo Bonifacio, Christoph Treude, Eiji Adachi Barbosa
The Impact Of Bug Localization Based On Crash Report Mining: A Developers' Perspective, Marcos Medeiros, Uirá Kulesza, Roberta Coelho, Rodrigo Bonifacio, Christoph Treude, Eiji Adachi Barbosa
Research Collection School Of Computing and Information Systems
Developers often use crash reports to understand the root cause of bugs. However, locating the buggy source code snippet from such information is a challenging task, mainly when the log database contains many crash reports. To mitigate this issue, recent research has proposed and evaluated approaches for grouping crash report data and using stack trace information to locate bugs. The effectiveness of such approaches has been evaluated by mainly comparing the candidate buggy code snippets with the actual changed code in bug-fix commits—which happens in the context of retrospective repository mining studies. Therefore, the existing literature still lacks discussing the …
Classifying Source Code: How Far Can Compressor-Based Classifiers Go?, Zhou Yang
Classifying Source Code: How Far Can Compressor-Based Classifiers Go?, Zhou Yang
Research Collection School Of Computing and Information Systems
Pre-trained language models of code, which are built upon large-scale datasets, millions of trainable parameters, and high computational resources cost, have achieved phenomenal success. Recently, researchers have proposed a compressor-based classifier (Cbc); it trains no parameters but is found to outperform BERT. We conduct the first empirical study to explore whether this lightweight alternative can accurately classify source code. Our study is more than applying Cbc to code-related tasks. We first identify an issue that the original implementation overestimates Cbc. After correction, Cbc's performance on defect prediction drops from 80.7% to 63.0%, which is still comparable to CodeBERT (63.7%). We …
Beyond A Joke: Dead Code Elimination Can Delete Live Code, Haoxin Tu, Lingxiao Jiang, Debin Gao, He Jiang
Beyond A Joke: Dead Code Elimination Can Delete Live Code, Haoxin Tu, Lingxiao Jiang, Debin Gao, He Jiang
Research Collection School Of Computing and Information Systems
Dead Code Elimination (DCE) is a fundamental compiler optimization technique that removes dead code (e.g., unreachable or reachable but whose results are unused) in the program to produce smaller or faster executables. However, since compiler optimizations are typically aggressively performed and there are complex relationships/interplay among a vast number of compiler optimizations (including DCE), it is not known whether DCE is indeed correctly performed and will only delete dead code in practice. In this study, we open a new research problem to investigate: can DCE happen to erroneously delete live code? To tackle this problem, we design a new approach …
Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude
Enhancing Source Code Representations For Deep Learning With Static Analysis, Xueting Guan, Christoph Treude
Research Collection School Of Computing and Information Systems
Deep learning techniques applied to program analysis tasks such as code classification, summarization, and bug detection have seen widespread interest. Traditional approaches, however, treat programming source code as natural language text, which may neglect significant structural or semantic details. Additionally, most current methods of representing source code focus solely on the code, without considering beneficial additional context. This paper explores the integration of static analysis and additional context such as bug reports and design patterns into source code representations for deep learning models. We use the Abstract Syntax Tree-based Neural Network (ASTNN) method and augment it with additional context information …
Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Test Optimization In Dnn Testing: A Survey, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Research Collection School Of Computing and Information Systems
This article presents a comprehensive survey on test optimization in deep neural network (DNN) testing. Here, test optimization refers to testing with low data labeling effort. We analyzed 90 papers, including 43 from the software engineering (SE) community, 32 from the machine learning (ML) community, and 15 from other communities. Our study: (i) unifies the problems as well as terminologies associated with low-labeling cost testing, (ii) compares the distinct focal points of SE and ML communities, and (iii) reveals the pitfalls in existing literature. Furthermore, we highlight the research opportunities in this domain.
Minimon: Minimizing Android Applications With Intelligent Monitoring-Based Debloating, Jiakun Liu, Zicheng Zhang, Xing Hu, Thung Ferdian, Shahar Maoz, Debin Gao, Eran Toch, Zhipeng Zhao, David Lo
Minimon: Minimizing Android Applications With Intelligent Monitoring-Based Debloating, Jiakun Liu, Zicheng Zhang, Xing Hu, Thung Ferdian, Shahar Maoz, Debin Gao, Eran Toch, Zhipeng Zhao, David Lo
Research Collection School Of Computing and Information Systems
The size of Android applications is getting larger to fulfill the requirements of various users. However, not all the features of the applications are needed and desired by a specific user. The unnecessary and non-desired features can increase the attack surface and consume system resources such as storage and memory. To address this issue, we propose a framework, MiniMon, to debloat unnecessary features from an Android app based on the logs of specific users' interactions with the app.However, rarely used features may not be recorded during the data collection, and users' preferences may change slightly over time. To address these …
Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo
Code Search Is All You Need? Improving Code Suggestions With Code Search, Junkai Chen, Xing Hu, Zhenhao Li, Cuiyun Gao, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
Modern integrated development environments (IDEs) provide various automated code suggestion techniques (e.g., code completion and code generation) to help developers improve their efficiency. Such techniques may retrieve similar code snippets from the code base or leverage deep learning models to provide code suggestions. However, how to effectively enhance the code suggestions using code retrieval has not been systematically investigated. In this paper, we study and explore a retrieval-augmented framework for code suggestions. Specifically, our framework leverages different retrieval approaches and search strategies to search similar code snippets. Then the retrieved code is used to further enhance the performance of language …
Out Of Sight, Out Of Mind: Better Automatic Vulnerability Repair By Broadening Input Ranges And Sources, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, David Lo
Out Of Sight, Out Of Mind: Better Automatic Vulnerability Repair By Broadening Input Ranges And Sources, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
The advances of deep learning (DL) have paved the way for automatic software vulnerability repair approaches, which effectively learn the mapping from the vulnerable code to the fixed code. Nevertheless, existing DL-based vulnerability repair methods face notable limitations: 1) they struggle to handle lengthy vulnerable code, 2) they treat code as natural language texts, neglecting its inherent structure, and 3) they do not tap into the valuable expert knowledge present in the expert system. To address this, we propose VulMaster, a Transformer-based neural network model that excels at generating vulnerability repairs by comprehensively understanding the entire vulnerable code, irrespective of …
Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo
Greening Large Language Models Of Code, Jieke Shi, Zhou Yang, Hong Jin Kang, Bowen Xu, Junda He, David Lo
Research Collection School Of Computing and Information Systems
Large language models of code have shown remarkable effectiveness across various software engineering tasks. Despite the availability of many cloud services built upon these powerful models, there remain several scenarios where developers cannot take full advantage of them, stemming from factors such as restricted or unreliable internet access, institutional privacy policies that prohibit external transmission of code to third-party vendors, and more. Therefore, developing a compact, efficient, and yet energy-saving model for deployment on developers' devices becomes essential.To this aim, we propose Avatar, a novel approach that crafts a deployable model from a large language model of code by optimizing …
Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li
Ps3: Precise Patch Presence Test Based On Semantic Symbolic Signature, Qi Zhan, Xing Hu, Zhiyang Li, Xin Xia, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which …
Mut: Human-In-The-Loop Unit Test Migration, Yi Gao, Xing Hu, Tongtong Xu, Xin Xia, David Lo, Xiaohu Yang
Mut: Human-In-The-Loop Unit Test Migration, Yi Gao, Xing Hu, Tongtong Xu, Xin Xia, David Lo, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Test migration, which enables the reuse of test cases crafted with knowledge and creativity by testers across various platforms and programming languages, has exhibited effectiveness in mobile app testing. However, unit test migration at the source code level has not garnered adequate attention and exploration. In this paper, we propose a novel cross-language and cross-platform test migration methodology, named MUT, which consists of four modules: code mapping, test case filtering, test case translation, and test case adaptation. MUT initially calculates code mappings to establish associations between source and target projects, and identifies suitable unit tests for migration from the source …
Curiosity-Driven Testing For Sequential Decision-Making Process, Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
Curiosity-Driven Testing For Sequential Decision-Making Process, Junda He, Zhou Yang, Jieke Shi, Chengran Yang, Kisub Kim, Bowen Xu, Xin Zhou, David Lo
Research Collection School Of Computing and Information Systems
Sequential decision-making processes (SDPs) are fundamental for complex real-world challenges, such as autonomous driving, robotic control, and traffic management. While recent advances in Deep Learning (DL) have led to mature solutions for solving these complex problems, SDMs remain vulnerable to learning unsafe behaviors, posing significant risks in safety-critical applications. However, developing a testing framework for SDMs that can identify a diverse set of crash-triggering scenarios remains an open challenge. To address this, we propose CureFuzz, a novel curiosity-driven black-box fuzz testing approach for SDMs. CureFuzz proposes a curiosity mechanism that allows a fuzzer to effectively explore novel and diverse scenarios, …
Towards Speedy Permission-Based Debloating For Android Apps, Thung Ferdian, Jiakun Liu, Pattarakrit Rattanukul, Shahar Maoz, Eran Toch, Debin Gao, David Lo
Towards Speedy Permission-Based Debloating For Android Apps, Thung Ferdian, Jiakun Liu, Pattarakrit Rattanukul, Shahar Maoz, Eran Toch, Debin Gao, David Lo
Research Collection School Of Computing and Information Systems
Android apps typically include many functionalities that not all users require. These result in software bloat that increases possible attack surface and app size. Common functionalities that users may not require are related to permissions that they intend to disallow in the first place. As these permissions are disallowed, their related code would never be executed and therefore can be safely removed. Existing work has proposed a solution to debloat Android apps according to the disallowed permissions. However, for large and complex applications, the debloating process could take hours, typically due the long time that may be needed to construct …
Deep Reinforcement Learning For Dynamic Algorithm Selection: A Proof-Of-Principle Study On Differential Evolution, Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong
Deep Reinforcement Learning For Dynamic Algorithm Selection: A Proof-Of-Principle Study On Differential Evolution, Hongshu Guo, Yining Ma, Zeyuan Ma, Jiacheng Chen, Xinglin Zhang, Zhiguang Cao, Jun Zhang, Yue-Jiao Gong
Research Collection School Of Computing and Information Systems
Evolutionary algorithms, such as differential evolution, excel in solving real-parameter optimization challenges. However, the effectiveness of a single algorithm varies across different problem instances, necessitating considerable efforts in algorithm selection or configuration. This article aims to address the limitation by leveraging the complementary strengths of a group of algorithms and dynamically scheduling them throughout the optimization progress for specific problems. We propose a deep reinforcement learning-based dynamic algorithm selection framework to accomplish this task. Our approach models the dynamic algorithm selection a Markov decision process, training an agent in a policy gradient manner to select the most suitable algorithm according …
Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang
Concretely Mapped Symbolic Memory Locations For Memory Error Detection, Haoxin Tu, Lingxiao Jiang, Jiaqi Hong, Xuhua Ding, He Jiang
Research Collection School Of Computing and Information Systems
Memory allocation is a fundamental operation for managing memory objects in many programming languages. Misusing allocated memory objects (e.g., buffer overflow and use-after-free) can lead to catastrophic consequences. Symbolic execution-based approaches are often used to detect such memory errors, leveraging their capabilities in automatic path exploration and test case generation. However, existing symbolic execution engines face significant limitations in modeling dynamic memory layouts. These engines either represent memory object locations as concrete addresses, limiting analyses to specific address layouts and missing errors that occur at special addresses, or represent locations as simple symbolic variables without sufficient constraints, resulting in memory …
Adaptive Content-Aware Influence Maximization Via Online Learning To Rank, Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady Wirawan Lauw
Adaptive Content-Aware Influence Maximization Via Online Learning To Rank, Konstantinos Theocharidis, Panagiotis Karras, Manolis Terrovitis, Spiros Skiadopoulos, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a …
Cybersecurity Education And Continuous Learning Towards Uae Ncsp Fulfilment, Saleh Hamad Aldaajeh
Cybersecurity Education And Continuous Learning Towards Uae Ncsp Fulfilment, Saleh Hamad Aldaajeh
Dissertations
This dissertation delves into enhancing cybersecurity education by aligning academic curricula with national cybersecurity strategic plan (NCSP) objectives, emphasizing the crucial role of Higher Education Institutions (HEIs) in developing a skilled cybersecurity workforce. Analyzing ten NCSPs, it identifies strategic themes and gaps between national goals and HEI offerings. The study reviews NCSP guidelines, international cybersecurity indices, and literature, including the NICE-NIST framework, to develop a framework that bridges the educational gap, improving learning outcomes and arming students with vital skills, knowledge, and competencies. Furthermore, it introduces a platform for continuous cybersecurity learning, employing micro-credentials, blockchain technology, and AI-driven systems. Based …