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Articles 1 - 15 of 15
Full-Text Articles in Programming Languages and Compilers
Reinforcement Learning Approach To Coordinate Real-World Multi-Agent Dynamic Routing And Scheduling, Joe Waldy
Reinforcement Learning Approach To Coordinate Real-World Multi-Agent Dynamic Routing And Scheduling, Joe Waldy
Dissertations and Theses Collection (Open Access)
In this thesis, we study new variants of routing and scheduling problems motivated by real-world problems from the urban logistics and law enforcement domains. In particular, we focus on two key aspects: dynamic and multi-agent. While routing problems such as the Vehicle Routing Problem (VRP) is well-studied in the Operations Research (OR) community, we know that in real-world route planning today, initially-planned route plans and schedules may be disrupted by dynamically-occurring events. In addition, routing and scheduling plans cannot be done in silos due to the presence of other agents which may be independent and self-interested. These requirements create …
Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing, Yu, Sicheng
Robustness And Cross-Lingual Transfer: An Exploration Of Out-Of-Distribution Scenario In Natural Language Processing, Yu, Sicheng
Dissertations and Theses Collection (Open Access)
Most traditional machine learning or deep learning methods are based on the premise that training data and test data are independent and identical distributed, i.e., IID. However, it is just an ideal situation. In real-world applications, test set and training data often follow different distributions, which we refer to as the out of distribution, i.e., OOD, setting. As a result, models trained with traditional methods always suffer from an undesirable performance drop on the OOD test set. It's necessary to develop techniques to solve this problem for real applications. In this dissertation, we present four pieces of work in the …
Novel Deep Learning Methods Combined With Static Analysis For Source Code Processing, Duy Quoc Nghi Bui
Novel Deep Learning Methods Combined With Static Analysis For Source Code Processing, Duy Quoc Nghi Bui
Dissertations and Theses Collection (Open Access)
It is desirable to combine machine learning and program analysis so that one can leverage the best of both to increase the performance of software analytics. On one side, machine learning can analyze the source code of thousands of well-written software projects that can uncover patterns that partially characterize software that is reliable, easy to read, and easy to maintain. On the other side, the program analysis can be used to define rigorous and unique rules that are only available in programming languages, which enrich the representation of source code and help the machine learning to capture the patterns better. …
A Virtualization Based System Infrastructure For Dynamic Program Analysis, Jiaqi Hong
A Virtualization Based System Infrastructure For Dynamic Program Analysis, Jiaqi Hong
Dissertations and Theses Collection (Open Access)
Dynamic malware analysis schemes either run the target program as is in an isolated environment assisted by additional hardware facilities or modify it with instrumentation code statically or dynamically. The hardware-assisted schemes usually trap the target during its execution to a more privileged environment based on the available hardware events. The more privileged environment is not accessible by the untrusted kernel, thus this approach is often applied for transparent and secure kernel analysis. Nevertheless, the isolated environment induces a virtual address gap between the analyzer and the target, which hinders effective and efficient memory introspection and undermines the correctness of …
Multimodal Mobile Sensing Systems For Physiological And Psychological Assessment, Nguyen Phan Sinh Huynh
Multimodal Mobile Sensing Systems For Physiological And Psychological Assessment, Nguyen Phan Sinh Huynh
Dissertations and Theses Collection (Open Access)
Sensing systems for monitoring physiological and psychological states have been studied extensively in both academic and industry research for different applications across various domains. However, most of the studies have been done in the lab environment with controlled and complicated sensor setup, which is only suitable for serious healthcare applications in which the obtrusiveness and immobility can be compromised in a trade-off for accurate clinical screening or diagnosing. The recent substantial development of mobile devices with embedded miniaturized sensors are now allowing new opportunities to adapt and develop such sensing systems in the mobile context. The ability to sense physiological …
Preference Learning And Similarity Learning Perspectives On Personalized Recommendation, Duy Dung Le
Preference Learning And Similarity Learning Perspectives On Personalized Recommendation, Duy Dung Le
Dissertations and Theses Collection (Open Access)
Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for each user to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen …
Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh
Exploiting Approximation, Caching And Specialization To Accelerate Vision Sensing Applications, Nguyen Loc Huynh
Dissertations and Theses Collection (Open Access)
Over the past few years, deep learning has emerged as state-of-the-art solutions for many challenging computer vision tasks such as face recognition, object detection, etc. Despite of its outstanding performance, deep neural networks (DNNs) are computational intensive, which prevent them to be widely adopted on billions of mobile and embedded devices with scarce resources. To address that limitation, we
focus on building systems and optimization algorithms to accelerate those models, making them more computational-efficient.
First, this thesis explores the computational capabilities of different existing processors (or co-processors) on modern mobile devices. It recognizes that by leveraging the mobile Graphics Processing …
Feature-Based Transfer Learning In Natural Language Processing, Jianfei Yu
Feature-Based Transfer Learning In Natural Language Processing, Jianfei Yu
Dissertations and Theses Collection (Open Access)
In the past few decades, supervised machine learning approach is one of the most important methodologies in the Natural Language Processing (NLP) community. Although various kinds of supervised learning methods have been proposed to obtain the state-of-the-art performance across most NLP tasks, the bottleneck of them lies in the heavy reliance on the large amount of manually annotated data, which is not always available in our desired target domain/task. To alleviate the data sparsity issue in the target domain/task, an attractive solution is to find sufficient labeled data from a related source domain/task. However, for most NLP applications, due to …
Overfitting In Automated Program Repair: Challenges And Solutions, Dinh Xuan Bach Le
Overfitting In Automated Program Repair: Challenges And Solutions, Dinh Xuan Bach Le
Dissertations and Theses Collection (Open Access)
This chapter discusses the main problem and motivation of this dissertation. It also discusses a quantification of various research issues directly related to the dissertation. A summary of works done will also be presented along with the structure of the dissertation.
Testing And Debugging: A Reality Check, Pavneet Singh Kochhar
Testing And Debugging: A Reality Check, Pavneet Singh Kochhar
Dissertations and Theses Collection
Testing and debugging are important activities during software development and maintenance. Testing is performed to check if the code contains errors whereas debugging is done to locate and fix these errors. Testing can be manual or automated and can be of different types such as unit, integration, system, stress etc. Debugging can also be manual or automated. These two activities have drawn attention of researchers in the recent years. Past studies have proposed many testing techniques such as automated test generation, test minimization, test case selection etc. Studies related to debugging have proposed new techniques to find bugs using various …
Real-Time Bursty Topic Detection And Virality Forecasting In Microblogs, Wei Xie
Real-Time Bursty Topic Detection And Virality Forecasting In Microblogs, Wei Xie
Dissertations and Theses Collection
Microblogs such as Twitter have become the largest social platforms for users around the world to share anything happening around them with friends and beyond. A bursty topic in microblogs is one that triggers a surge of relevant tweets within a short period of time, which often reflects important events of mass interest. How to leverage microblogs for early detection and further impact analysis of bursty topics has, therefore, become an important research problem with immense practical value.
Fusing Mobile, Wearable And Infrastructure Sensing For Immersive Daily Lifestyle Analytics, Sougata Sen
Fusing Mobile, Wearable And Infrastructure Sensing For Immersive Daily Lifestyle Analytics, Sougata Sen
Dissertations and Theses Collection
With the prevalence of sensors in public infrastructure as well as in personal devices, exploitation of data from these sensors to monitor and profile basic activities (e.g., locomotive states such as walking, and gestural actions such as smoking) has gained popularity. Basic activities identified by these sensors will drive the next generation of lifestyle monitoring applications and services. To provide more advanced and personalized services, these next-generation systems will need to capture and understand increasingly finer-grained details of various common daily life activities. In this dissertation, I demonstrate the possibility of building systems using offthe- shelf devices, that not only …
Proactive And Reactive Strategies To Handle Surges In Urban Crowds, Jiali Du
Proactive And Reactive Strategies To Handle Surges In Urban Crowds, Jiali Du
Dissertations and Theses Collection
Most urban infrastructures are built to cater a planned capacity, yet surges in usage do happen in times (can be either expected or unexpected), and this has long been a major challenge for urban planner. In this thesis, I propose to study approaches handle surges in urban crowd movement. In particular, the surges in demand studied are limited to situations where a large crowd of commuters/visitors gather in a small vicinity, and I am concerned with their movements both within the vicinity and out of the vicinity (the egress from the vicinity). Significant crowd build-ups and congestions can be observed …
Techniques For Identifying Mobile Platform Vulnerabilities And Detecting Policy-Violating Applications, Mon Kywe Su
Techniques For Identifying Mobile Platform Vulnerabilities And Detecting Policy-Violating Applications, Mon Kywe Su
Dissertations and Theses Collection
Mobile systems are generally composed of three layers of software: application layer where third-party applications are installed, framework layer where Application Programming Interfaces (APIs) are exposed, and kernel layer where low-level system operations are executed. In this dissertation, we focus on security and vulnerability analysis of framework and application layers. Security mechanisms, such as Android’s sandbox and permission systems, exist in framework layer, while malware scanners protects application layer. However, there are rooms for improvement in both mechanisms. For instance, Android’s permission system is known to be implemented in ad-hoc manner and not well-tested for vulnerabilities. Application layer also focuses …
Decision Modeling And Empirical Analysis Of Mobile Financial Services, Jun Liu
Decision Modeling And Empirical Analysis Of Mobile Financial Services, Jun Liu
Dissertations and Theses Collection
The past twenty years have been a time of many new technological developments, changing business practices, and interesting innovations in the financial information system (IS) and technology landscape. As the financial services industry has been undergoing the digital transformation, the emergence of mobile financial services has been changing the way that customers pay for goods and services purchases and interact with financial institutions. This dissertation seeks to understand the evolution of the mobile payments technology ecosystem and how firms make mobile payments investment decisions under uncertainty, as well as examines the influence of mobile banking on customer behavior and financial …