Computer Sciences Commons^™

Is Surprisal In Issue Trackers Actionable?, James Caddy, Markus Wagner, Christoph Treude, Earl T. Barr, Miltiadis Allamanis

Research Collection School Of Computing and Information Systems

Background. From information theory, surprisal is a measurement of how unexpected an event is. Statistical language models provide a probabilistic approximation of natural languages, and because surprisal is constructed with the probability of an event occuring, it is therefore possible to determine the surprisal associated with English sentences. The issues and pull requests of software repository issue trackers give insight into the development process and likely contain the surprising events of this process. Objective. Prior works have identified that unusual events in software repositories are of interest to developers, and use simple code metrics-based methods for detecting them. In this …

Linkbreaker: Breaking The Backdoor-Trigger Link In Dnns Via Neurons Consistency Check, Zhenzhu Chen, Shang Wang, Anmin Fu, Yansong Gao, Shui Yu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Backdoor attacks cause model misbehaving by first implanting backdoors in deep neural networks (DNNs) during training and then activating the backdoor via samples with triggers during inference. The compromised models could pose serious security risks to artificial intelligence systems, such as misidentifying 'stop' traffic sign into '80km/h'. In this paper, we investigate the connection characteristic between the backdoor and the trigger in DNNs and observe the fact that the backdoor is implanted via establishing a link between a cluster of neurons, representing the backdoor, and the triggers. Based on this observation, we design LinkBreaker, a new generic scheme for defending …

Uipdroid: Unrooted Dynamic Monitor Of Android App Uis For Fine-Grained Permission Control, Mulin Duan, Lingxiao Jiang, Lwin Khin Shar, Debin Gao

Research Collection School Of Computing and Information Systems

Proper permission controls in Android systems are important for protecting users' private data when running applications installed on the devices. Currently Android systems require apps to obtain authorization from users at the first time when they try to access users' sensitive data, but every permission is only managed at the application level, allowing apps to (mis)use permissions granted by users at the beginning for different purposes subsequently without informing users. Based on privacy-by-design principles, this paper develops a new permission manager, named UIPDroid, that (1) enforces the users' basic right-to-know through user interfaces whenever an app uses permissions, and (2) …

Topic-Guided Conversational Recommender In Multiple Domains, Lizi Liao, Ryuichi Takanobu, Yunshan Ma, Xun Yang, Minlie Huang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Conversational systems have recently attracted significant attention. Both the research community and industry believe that it will exert huge impact on human-computer interaction, and specifically, the IR/RecSys community has begun to explore Conversational Recommendation. In real-life scenarios, such systems are often urgently needed in helping users accomplishing different tasks under various situations. However, existing works still face several shortcomings: (1) Most efforts are largely confined in single task setting. They fall short of hands in handling tasks across domains. (2) Aside from soliciting user preference from dialogue history, a conversational recommender naturally has access to the back-end data structure which …

Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo

Research Collection School Of Computing and Information Systems

Software engineers depend heavily on software libraries and have to update their dependencies once vulnerabilities are found in them. Software Composition Analysis (SCA) helps developers identify vulnerable libraries used by an application. A key challenge is the identification of libraries related to a given reported vulnerability in the National Vulnerability Database (NVD), which may not explicitly indicate the affected libraries. Recently, researchers have tried to address the problem of identifying the libraries from an NVD report by treating it as an extreme multi-label learning (XML) problem, characterized by its large number of possible labels and severe data sparsity. As input, …

Evaluating The Persuasiveness Of Mobile Health: The Intersection Of Persuasive System Design And Data Science, Aleise Mcgowan

Graduate Theses and Dissertations (2019 - present)

Persuasive technology is an umbrella term that encompasses any software (e.g., mobile app) or hardware (e.g., smartwatch) designed to influence users to perform a preferable behavior once or on a long-term basis. Considering the ubiquitous nature of mobile devices across all socioeconomic groups, user behavior modification thrives under the personalized care that persuasive technology can offer. This research examines the roles psychological characteristics play in interpreted mHealth screen perceived persuasiveness. A review of the literature revealed a gap regarding how developers of digital health technologies are often tasked with developing tools designed to engage patients, yet little emphasis has been …

Enhancing System Security Using Dynamic Hardware, Sydney L. Davis

Graduate Theses and Dissertations (2019 - present)

Within the ever-advancing field of computing, there is significant research into the many facets of cyber security. However, there is very little research to support the concept of using a Field Programmable Gate Array (FPGA) to increase the security of a system. While its most common use is to provide efficiency and speedup of processes, this research considers the use of an FPGA to mitigate vulnerabilities in both software and hardware. This paper proposes circuit variance within an FPGA as a method of Moving Target Defense (MTD) and investigates its effect on side-channels. We hypothesize that although the functionality of …

“Lasso The Moon? Is It Possible? What About Hack The Moon? Today’S International Framework For Activities On The Moon”, Diane M. Janosek, Armando Seay, Josa P. Natera

Military Cyber Affairs

The global interest in the moon and outer space continues to skyrocket. The current U.S. commercial investment in space is $350 billion annually, and it is expected to grow to $1 Trillion or more by 2040. The U.S. military investment in space defense and research likewise continues to grow, with the total investment amount remaining classified. With the frequent activity in space, as well as concerns about attacks to US space assets to and from space, the U.S, created the United States Space Command and its Space Force. With private space travel, nanosatellites, lunar exploration, and the proliferation of space …

The Iwar Range + 21 Years: Cyber Defense Education In 2022, Joseph H. Schafer, Chris Morrell, Ray Blaine

Military Cyber Affairs

Twenty-one years ago, The IWAR Range paper published by CCSC described nascent information assurance (now cybersecurity[1]) education programs and the inspiration and details for constructing cyber ranges and facilitating cyber exercises. This paper updates the previously published work by highlighting the dramatic evolution of the cyber curricula, exercise networks and ranges, influences, and environments over the past twenty years.

[1] In 2014, DoD adopted “cybersecurity” instead of “information assurance.” [34:1]

Hypergaming For Cyber: Strategy For Gaming A Wicked Problem, Joshua A. Sipper

Military Cyber Affairs

Cyber as a domain and battlespace coincides with the defined attributes of a “wicked problem” with complexity and inter-domain interactions to spare. Since its elevation to domain status, cyber has continued to defy many attempts to explain its reach, importance, and fundamental definition. Corresponding to these intricacies, cyber also presents many interlaced attributes with other information related capabilities (IRCs), namely electromagnetic warfare (EW), information operations (IO), and intelligence, surveillance, and reconnaissance (ISR), within an information warfare (IW) construct that serves to add to its multifaceted nature. In this cyber analysis, the concept of hypergaming will be defined and discussed in …

Framework Of Hardware Trojan Detection Leveraging Structural Checking Tool, Rafael Dacanay Del Carmen

Graduate Theses and Dissertations

Since there is a significant demand for obtaining third-party soft Intellectual Property (IP) by first-party integrated circuit (IC) vendors, it is becoming easier for adversaries to insert malicious logic known as hardware Trojans into designs. Due to this, vendors need to find ways to screen the third-party IPs for possible security threats and then mitigate them. The development of the Structural Checking (SC) tool provides a solution to this issue. This tool analyzes the structure of an unknown soft IP design and creates a network of all the signals within the design and how they are connected to each other. …

Hybrid Machine And Deep Learning-Based Cyberattack Detection And Classification In Smart Grid Networks, Adedayo Aribisala

Electronic Theses and Dissertations

Power grids have rapidly evolved into Smart grids and are heavily dependent on Supervisory Control and Data Acquisition (SCADA) systems for monitoring and control. However, this evolution increases the susceptibility of the remote (VMs, VPNs) and physical interfaces (sensors, PMUs LAN, WAN, sub-stations power lines, and smart meters) to sophisticated cyberattacks. The continuous supply of power is critical to power generation plants, power grids, industrial grids, and nuclear grids; the halt to global power could have a devastating effect on the economy's critical infrastructures and human life.

Machine Learning and Deep Learning-based cyberattack detection modeling have yielded promising results when …

Supervised Representation Learning For Improving Prediction Performance In Medical Decision Support Applications, Phawis Thammasorn

Graduate Theses and Dissertations

Machine learning approaches for prediction play an integral role in modern-day decision supports system. An integral part of the process is extracting interest variables or features to describe the input data. Then, the variables are utilized for training machine-learning algorithms to map from the variables to the target output. After the training, the model is validated with either validation or testing data before making predictions with a new dataset. Despite the straightforward workflow, the process relies heavily on good feature representation of data. Engineering suitable representation eases the subsequent actions and copes with many practical issues that potentially prevent the …

Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo

Senior Honors Papers / Undergraduate Theses

Supervised machine learning suffers from the ``garbage-in garbage-out" phenomenon where the performance of a model is limited by the quality of the data. While a myriad of data is collected every second, there is no general rigorous method of evaluating the quality of a given dataset. This hinders fair pricing of data in scenarios where a buyer may look to buy data for use with machine learning. In this work, I propose using the expected loss corresponding to a dataset as a measure of its quality, relying on Bayesian methods for uncertainty quantification. Furthermore, I present a secure multi-party computation …

Radiomic Features To Predict Overall Survival Time For Patients With Glioblastoma Brain Tumors Based On Machine Learning And Deep Learning Methods, Lina Chato

UNLV Theses, Dissertations, Professional Papers, and Capstones

Machine Learning (ML) methods including Deep Learning (DL) Methods have been employed in the medical field to improve diagnosis process and patient’s prognosis outcomes. Glioblastoma multiforme is an extremely aggressive Glioma brain tumor that has a poor survival rate. Understanding the behavior of the Glioblastoma brain tumor is still uncertain and some factors are still unrecognized. In fact, the tumor behavior is important to decide a proper treatment plan and to improve a patient’s health. The aim of this dissertation is to develop a Computer-Aided-Diagnosis system (CADiag) based on ML/DL methods to automatically estimate the Overall Survival Time (OST) for …

Hci In Southeast Asia: The Journey Forward, E. Sari, J.A. Tedjasaputra, Y. Kurniawan, E. Zulaikha, A. Asfarian, M. Ghazali, A. Sivaji, J.A. Abu Bakar, C.Y. Wong, N.M. Norowi, Tamas Makany, D. Perera-Schulz, T. Chintakovid, S. Nuchitprasitchai, Ethel Ong

Research Collection Lee Kong Chian School Of Business

SEACHI 2022 has been conducted to bring HCI and UX leaders in Southeast Asia to discuss the current state-of-the-art HCI and UX teaching, practice, and support they experience in their region. This activity aims to explore the potentials and challenges and identify the gaps amongst different sectors in different countries. Through this workshop, we will have a common understanding of what we face. It explores how we can work collaboratively to achieve a better purpose, i.e., to grow HCI and UX fields in Southeast Asia. This one-day online workshop was conducted as a collocated event of CHI 2022 and was …

Who Will Support My Project? Interactive Search Of Potential Crowdfunding Investors Through Insearch., Songheng Zhang, Yong Wang, Haotian Li, Wanyu Zhang

Research Collection School Of Computing and Information Systems

Crowdfunding provides project founders with a convenient way to reach online investors. However, it is challenging for founders to find the most potential investors and successfully raise money for their projects on crowdfunding platforms. A few machine learning based methods have been proposed to recommend investors’ interest in a specific crowdfunding project, but they fail to provide project founders with explanations in detail for these recommendations, thereby leading to an erosion of trust in predicted investors. To help crowdfunding founders find truly interested investors, we conducted semi-structured interviews with four crowdfunding experts and presentsinSearch, a visual analytic system. inSearch allows …

Static Inference Meets Deep Learning: A Hybrid Type Inference Approach For Python, Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they cannot guarantee the correctness of the predicted types. Their performance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Unfortunately, to our knowledge, precise type …

Tourgether360: Exploring 360° Tour Videos With Others, Kartikaeya Kumar, Lev Poretski, Jiannan Li, Anthony Tang

Research Collection School Of Computing and Information Systems

Contemporary 360° video players do not provide ways to let people explore the videos together. Tourgether360 addresses this problem for 360° tour videos using a pseudo-spatial navigation technique that provides both an overhead “context” view of the environment as a minimap, as well as a shared pseudo-3D environment for exploring the video. Collaborators appear as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. In this work, we describe the intellectual precedents for this work, our design goals, and our implementation approach of Tourgether360. Finally, we discuss future work based …

Parallel Construction Of Random Forest On Gpu, Kennedy Senagi, Nicolas Jouandeau

All Peer-Reviewed Publications

There is tremendous growth of data generated from different industries, i.e., health, agriculture, engineering, etc. Consequently, there is demand for more processing power. Compared to computer processing units, general-purpose graphics processing units (GPUs) are rapidly emerging as a promising solution to achieving high performance and energy efficiency in various computing domains. Multiple forms of parallelism and complexity in memory access have posed a challenge in developing Random Forest (RF) GPU-based algorithm. RF is a popular and robust machine learning algorithm. In this paper, coarse-grained and dynamic parallelism approaches on GPU are integrated into RF(dpRFGPU). Experiment results of dpRFGPU are compared …

Development Of Modeling And Simulation Platform For Path-Planning And Control Of Autonomous Underwater Vehicles In Three-Dimensional Spaces, Sai Krishna Abhiram Kondapalli

Mechanical & Aerospace Engineering Theses & Dissertations

Autonomous underwater vehicles (AUVs) operating in deep sea and littoral environments have diverse applications including marine biology exploration, ocean environment monitoring, search for plane crash sites, inspection of ship-hulls and pipelines, underwater oil rig maintenance, border patrol, etc. Achieving autonomy in underwater vehicles relies on a tight integration between modules of sensing, navigation, decision-making, path-planning, trajectory tracking, and low-level control. This system integration task benefits from testing the related algorithms and techniques in a simulated environment before implementation in a physical test bed. This thesis reports on the development of a modeling and simulation platform that supports the design and …

Software Engineering User Study Recruitment On Prolific: An Experience Report, Brittany Reid, Markus Wagner, Marcelo D’Amorim, Christoph Treude

Research Collection School Of Computing and Information Systems

Online participant recruitment platforms such as Prolific have been gaining popularity in research, as they enable researchers to easily access large pools of participants. However, participant quality can be an issue; participants may give incorrect information to gain access to more studies, adding unwanted noise to results. This paper details our experience recruiting participants from Prolific for a user study requiring programming skills in Node.js, with the aim of helping other researchers conduct similar studies. We explore a method of recruiting programmer participants using prescreening validation, attention checks and a series of programming knowledge questions. We received 680 responses, and …

Press A To Jump: Design Strategies For Video Game Learnability, Lev Poretski, Anthony Tang

Research Collection School Of Computing and Information Systems

Learnability is a core aspect of software usability. Video games are not an exception, as game designers need to teach players how to play their creations. We analyzed 40 contemporary video games to identify how video games approach learning experiences. We found that games have advanced far beyond using simple tutorials or demonstration screens and adopt a range of repeatable and reusable design strategies using visual cues to facilitate learning. We provide a detailed descriptive framework of these design strategies, elucidating how and when they can be used, and describing how the visual cues are used to build them. Our …

Designing Visuo-Haptic Illusions With Proxies In Virtual Reality: Exploration Of Grasp, Movement Trajectory And Object Mass, Martin Feick, Kora Persephone Regitz, Anthony Tang, Antonio Kruger

Research Collection School Of Computing and Information Systems

Visuo-haptic illusions are a method to expand proxy-based interactions in VR by introducing unnoticeable discrepancies between the virtual and real world. Yet how different design variables affect the illusions with proxies is still unclear. To unpack a subset of variables, we conducted two user studies with 48 participants to explore the impact of (1) different grasping types and movement trajectories, and (2) different grasping types and object masses on the discrepancy which may be introduced. Our Bayes analysis suggests that grasping types and object masses (≤ 500 g) did not noticeably affect the discrepancy, but for movement trajectory, results were …

Graphcode2vec: Generic Code Embedding Via Lexical And Program Dependence Analyses, Wei Ma, Mengjie Zhao, Ezekiel Soremekun, Qiang Hu, Jie M. Zhang, Mike Papadakis, Maxime Cordy, Xiaofei Xie, Yves Le Traon

Research Collection School Of Computing and Information Systems

Code embedding is a keystone in the application of machine learning on several Software Engineering (SE) tasks. To effectively support a plethora of SE tasks, the embedding needs to capture program syntax and semantics in a way that is generic. To this end, we propose the first self-supervised pre-training approach (called Graphcode2vec) which produces task-agnostic embedding of lexical and program dependence features. Graphcode2vec achieves this via a synergistic combination of code analysis and Graph Neural Networks. Graphcode2vec is generic, it allows pre-training, and it is applicable to several SE downstream tasks. We evaluate the effectiveness of Graphcode2vec on four (4) …

Learning Transferable Perturbations For Image Captioning, Hanjie Wu, Yongtuo Liu, Hongmin Cai, Shengfeng He

Research Collection School Of Computing and Information Systems

Present studies have discovered that state-of-the-art deep learning models can be attacked by small but well-designed perturbations. Existing attack algorithms for the image captioning task is time-consuming, and their generated adversarial examples cannot transfer well to other models. To generate adversarial examples faster and stronger, we propose to learn the perturbations by a generative model that is governed by three novel loss functions. Image feature distortion loss is designed to maximize the encoded image feature distance between original images and the corresponding adversarial examples at the image domain, and local-global mismatching loss is introduced to separate the mapping encoding representation …

Hierarchical Value Decomposition For Effective On-Demand Ride Pooling, Hao Jiang, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

On-demand ride-pooling (e.g., UberPool, GrabShare) services focus on serving multiple different customer requests using each vehicle, i.e., an empty or partially filled vehicle can be assigned requests from different passengers with different origins and destinations. On the other hand, in Taxi on Demand (ToD) services (e.g., UberX), one vehicle is assigned to only one request at a time. On-demand ride pooling is not only beneficial to customers (lower cost), drivers (higher revenue per trip) and aggregation companies (higher revenue), but is also of crucial importance to the environment as it reduces the number of vehicles required on the roads. Since …

Itss: Interactive Web-Based Authoring And Playback Integrated Environment For Programming Tutorials, Eng Lieh Ouh, Benjamin Gan, David Lo

Research Collection School Of Computing and Information Systems

Video-based programming tutorials are a popular form of tutorial used by authors to guide learners to code. Still, the interactivity of these videos is limited primarily to control video flow. There are existing works with increased interactivity that are shown to improve the learning experience. Still, these solutions require setting up a custom recording environment and are not well-integrated with the playback environment. This paper describes our integrated ITSS environment and evaluates the ease of authoring and playback of our interactive programming tutorials. Our environment is designed to run within the browser sandbox and is less intrusive to record interactivity …

Undiscounted Recursive Path Choice Models: Convergence Properties And Algorithms, Tien Mai, Emma Frejinger

Research Collection School Of Computing and Information Systems

Traffic flow predictions are central to a wealth of problems in transportation. Path choice models can be used for this purpose, and in state-of-the-art models—so-called recursive path choice (RPC) models—the choice of a path is formulated as a sequential arc choice process using undiscounted Markov decision process (MDP) with an absorbing state. The MDP has a utility maximization objective with unknown parameters that are estimated based on data. The estimation and prediction using RPC models require repeatedly solving value functions that are solutions to the Bellman equation. Although there are several examples of successful applications of RPC models in the …

Unified Route Planning For Shared Mobility: An Insertion-Based Framework, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Lei Chen, Ke. Xu

Research Collection School Of Computing and Information Systems

There has been a dramatic growth of shared mobility applications such as ride-sharing, food delivery, and crowdsourced parcel delivery. Shared mobility refers to transportation services that are shared among users, where a central issue is route planning. Given a set of workers and requests, route planning finds for each worker a route, i.e., a sequence of locations to pick up and drop off passengers/parcels that arrive from time to time, with different optimization objectives. Previous studies lack practicability due to their conflicted objectives and inefficiency in inserting a new request into a route, a basic operation called insertion. In addition, …