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Vulcurator: A Vulnerability-Fixing Commit Detector, Truong Giang Nguyen, Cong Thanh Le, Hong Jin Kang, Xuan-Bach D. Le, David Lo
Vulcurator: A Vulnerability-Fixing Commit Detector, Truong Giang Nguyen, Cong Thanh Le, Hong Jin Kang, Xuan-Bach D. Le, David Lo
Research Collection School Of Computing and Information Systems
Open-source software (OSS) vulnerability management process is important nowadays, as the number of discovered OSS vulnerabilities is increasing over time. Monitoring vulnerability-fixing commits is a part of the standard process to prevent vulnerability exploitation. Manually detecting vulnerability-fixing commits is, however, time-consuming due to the possibly large number of commits to review. Recently, many techniques have been proposed to automatically detect vulnerability-fixing commits using machine learning. These solutions either: (1) did not use deep learning, or (2) use deep learning on only limited sources of information. This paper proposes VulCurator, a tool that leverages deep learning on richer sources of information, …
Cscw 2022 Chairs' Welcome, Gary Hsieh, Anthony Tang
Cscw 2022 Chairs' Welcome, Gary Hsieh, Anthony Tang
Research Collection School Of Computing and Information Systems
We are excited to bring you the 25th ACM Conference on Computer-Supported Cooperative Work and Social Computing – CSCW 2022 – virtually. We were initially poised to hold CSCW 2022 in Taiwan to help strengthen the CSCW community’s relationship with Asia, but the uncertainty around COVID-19 pandemic restrictions made this an impractical idea. Yet, as designers, we view challenges as learning opportunities and know learning opportunities lead to new ideas and approaches. From the online and virtual conferences experiences of the past few years, we considered questions like: what makes conferences interesting, what makes conferences worth attending, how can we …
Context-Aware Code Recommendation In Intellij Idea, Shamsa Abid, Hamid Abdul Basit, Shafay Shamail
Context-Aware Code Recommendation In Intellij Idea, Shamsa Abid, Hamid Abdul Basit, Shafay Shamail
Research Collection School Of Computing and Information Systems
Developers spend a lot of time online, searching for code to help them implement their desired features. While code recommenders help improve developers’ productivity, there is currently no support for context-aware code recommendation for opportunistic code reuse on-the-go. Typical code recommendation systems provide recommendations against a search query, whereas a code recommender that supports opportunistic reuse can recommend related code snippets that represent features that the developer may want to implement next. In this paper, we present a novel Context-aware Feature-driven API usage-based Code Recommender (CA-FACER) tool, which is an Intellij IDEA plugin that leverages a developer’s development context to …
How To Formulate Specific How-To Questions In Software Development?, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Jiazhan Xie, Huanjun Xu, Yanjun Yang
How To Formulate Specific How-To Questions In Software Development?, Mingwei Liu, Xin Peng, Andrian Marcus, Christoph Treude, Jiazhan Xie, Huanjun Xu, Yanjun Yang
Research Collection School Of Computing and Information Systems
Developers often ask how-to questions using search engines, technical Q&A communities, and interactive Q&A systems to seek help for specific programming tasks. However, they often do not formulate the questions in a specific way, making it hard for the systems to return the best answers. We propose an approach (TaskKG4Q) that interactively helps developers formulate a programming related how-to question. TaskKG4Q is using a programming task knowledge graph (task KG in short) mined from Stack Overflow questions, which provides a hierarchical conceptual structure for tasks in terms of [actions], [objects], and [constraints]. An empirical evaluation of the intrinsic quality of …
A Cognitive-Emotional Model From Mobile Short-Form Video Addiction To Intermittent Discontinuance: The Moderating Role Of Neutralization, Yafei Feng, Lifu Li, Anqi Zhao
A Cognitive-Emotional Model From Mobile Short-Form Video Addiction To Intermittent Discontinuance: The Moderating Role Of Neutralization, Yafei Feng, Lifu Li, Anqi Zhao
Research Collection School Of Computing and Information Systems
Discontinuance is considered very effective in the treatment of IT addiction. However, prior literature on the mechanism from IT addiction to discontinuance has reached inconsistent conclusions. Also, scholars cannot explain why some individuals do not make any changes after becoming addicted, even when they realize that their behavior is not in line with the norms. To fill the gaps, our research developed a cognitive-emotional model, with neutralization as a moderator, to understand the internal mechanism from mobile short-form video addiction to intermittent discontinuance. Based on the online survey of 493 Chinese mobile short-form video users, this study used partial least …
Untouchable Once Revoking: A Practical And Secure Dynamic Ehr Sharing System Via Cloud, Shengmin Xu, Jianting Ning, Xinyi Huang, Yingjiu Li, Guowen Xu
Untouchable Once Revoking: A Practical And Secure Dynamic Ehr Sharing System Via Cloud, Shengmin Xu, Jianting Ning, Xinyi Huang, Yingjiu Li, Guowen Xu
Research Collection School Of Computing and Information Systems
Healthcare Internet-of-Things (IoT) enables lightweight devices to observe patients’ vital signals and outsource them to a remote cloud to enjoy flexible data sharing. However, it faces many security threats as the outsourced data is no longer physically controlled by data owners, and the cloud that hosts the outsourced data is not fully trusted. Many privacy protection technologies have been adopted to solve this problem, among which cryptographic mechanisms have become one of the most promising tools. Unfortunately, current cryptographic mechanisms in healthcare IoT mainly suffer from the following challenges: 1) dynamic user groups for managing users’ accessibility; 2) efficient revocation …
Reliable Policy Updating Under Efficient Policy Hidden Fine-Grained Access Control Framework For Cloud Data Sharing, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu, Robert H. Deng
Reliable Policy Updating Under Efficient Policy Hidden Fine-Grained Access Control Framework For Cloud Data Sharing, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu, Robert H. Deng
Research Collection School Of Computing and Information Systems
Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is one of the potent encryption paradigms in protecting data confidentiality in the cloud data sharing scenario. However, the access policy of the traditional CP-ABE is in plaintext form that reveals significant sensitive information of data owners and data visitors. To mitigate this problem, two approaches have been proposed in the literature. One is partially hidden, where the attributes in the access policy are divided into two parts: the plaintext attribute names and the hidden attribute values. The other approach fully hides the attributes in the access policy which, unfortunately, hinders efficient and correct decryption as …
Artificial Intelligence For Natural Disaster Management, Guansong Pang
Artificial Intelligence For Natural Disaster Management, Guansong Pang
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) can leverage massive amount of diverse types of data, such as geospatial data, social media data, and wireless network sensor data, to enhance our understanding of natural disasters, their forecasting and detection, and humanitarian assistance in natural disaster management (NDM). Due to this potential, different communities have been dedicating enormous efforts to the development and/or adoption of AI technologies for NDM. This article provides an overview of these efforts and discusses major challenges and opportunities in this topic.
Fully Deformable Network For Multiview Face Image Synthesis, Cheng Xu, Keke Li, Xuandi Luo, Xuemiao Xu, Shengfeng He, Kun Zhang
Fully Deformable Network For Multiview Face Image Synthesis, Cheng Xu, Keke Li, Xuandi Luo, Xuemiao Xu, Shengfeng He, Kun Zhang
Research Collection School Of Computing and Information Systems
Photorealistic multiview face synthesis from a single image is a challenging problem. Existing works mainly learn a texture mapping model from the source to the target faces. However, they rarely consider the geometric constraints on the internal deformation arising from pose variations, which causes a high level of uncertainty in face pose modeling, and hence, produces inferior results for large pose variations. Moreover, current methods typically suffer from undesired facial details loss due to the adoption of the de-facto standard encoder-decoder architecture without any skip connections (SCs). In this article, we directly learn and exploit geometric constraints and propose a …
Hapticpuppet: A Kinesthetic Mid-Air Multidirectional Force-Feedback Drone-Based Interface, Martin Feick, Anthony Tang, Antonio Kruger
Hapticpuppet: A Kinesthetic Mid-Air Multidirectional Force-Feedback Drone-Based Interface, Martin Feick, Anthony Tang, Antonio Kruger
Research Collection School Of Computing and Information Systems
Providing kinesthetic force-feedback for human-scale interactions is challenging due to the relatively large forces needed. Therefore, robotic actuators are predominantly used to deliver this kind of haptic feedback; however, they offer limited flexibility and spatial resolution. In this work, we introduce HapticPuppet, a drone-based force-feedback interface which can exert multidirectional forces onto the human body. This can be achieved by attaching strings to different parts of the human body such as fingers, hands or ankles, which can then be affixed to multiple coordinated drones - puppeteering the user. HapticPuppet opens up a wide range of potential applications in virtual, augmented …
Tourgether360: Collaborative Exploration Of 360° Videos Using Pseudo-Spatial Navigation, Kartikaeya Kumar, Lev Poretski, Jianan Li, Anthony Tang
Tourgether360: Collaborative Exploration Of 360° Videos Using Pseudo-Spatial Navigation, Kartikaeya Kumar, Lev Poretski, Jianan Li, Anthony Tang
Research Collection School Of Computing and Information Systems
Collaborative exploration of 360 videos with contemporary interfaces is challenging because collaborators do not have awareness of one another's viewing activities. Tourgether360 enhances social exploration of 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 are embodied as avatars along a track depending on their position in the video timeline and can point and synchronize their playback. We evaluated the Tourgether360 concept through two studies: first, a comparative study with a simplified version of Tourgether360 with …
Daot: Domain-Agnostically Aligned Optimal Transport For Domain-Adaptive Crowd Counting, Huilin Zhu, Jingling Yuan, Xian Zhong, Zhengwei Yang, Zheng Wang, Shengfeng He
Daot: Domain-Agnostically Aligned Optimal Transport For Domain-Adaptive Crowd Counting, Huilin Zhu, Jingling Yuan, Xian Zhong, Zhengwei Yang, Zheng Wang, Shengfeng He
Research Collection School Of Computing and Information Systems
Domain adaptation is commonly employed in crowd counting to bridge the domain gaps between different datasets. However, existing domain adaptation methods tend to focus on inter-dataset differences while overlooking the intra-differences within the same dataset, leading to additional learning ambiguities. These domain-agnostic factors,e.g., density, surveillance perspective, and scale, can cause significant in-domain variations, and the misalignment of these factors across domains can lead to a drop in performance in cross-domain crowd counting. To address this issue, we propose a Domain-agnostically Aligned Optimal Transport (DAOT) strategy that aligns domain-agnostic factors between domains. The DAOT consists of three steps. First, individual-level differences …
Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang
Towards Automated Safety Vetting Of Smart Contracts In Decentralized Applications, Yue Duan, Xin Zhao, Yu Pan, Shucheng Li, Minghao Li, Fengyuan Xu, Mu Zhang
Research Collection School Of Computing and Information Systems
We propose VetSC, a novel UI-driven, program analysis guided model checking technique that can automatically extract contract semantics in DApps so as to enable targeted safety vetting. To facilitate model checking, we extract business model graphs from contract code that capture its intrinsic business and safety logic. To automatically determine what safety specifications to check, we retrieve textual semantics from DApp user interfaces. To exclude untrusted UI text, we also validate the UI-logic consistency and detect any discrepancies. We have implemented VetSC and applied it to 34 real-world DApps. Experiments have demonstrated that VetSC can accurately interpret smart contract code, …
Api-Related Developer Information Needs In Stack Overflow, Mingwei Liu, Xin Peng, Andrian Marcus, Shuangshuang Xing, Christoph Treude, Chengyuan Zhao
Api-Related Developer Information Needs In Stack Overflow, Mingwei Liu, Xin Peng, Andrian Marcus, Shuangshuang Xing, Christoph Treude, Chengyuan Zhao
Research Collection School Of Computing and Information Systems
Stack Overflow (SO) provides informal documentation for APIs in response to questions that express API related developer needs. Navigating the information available on SO and getting information related to a particular API and need is challenging due to the vast amount of questions and answers and the tag-driven structure of SO. In this paper we focus on identifying and classifying fine-grained developer needs expressed in sentences of API-related SO questions, as well as the specific information types used to express such needs, and the different roles APIs play in these questions and their answers. We derive a taxonomy, complementing existing …
In War And Peace: The Impact Of World Politics On Software Ecosystems, Raula Kula, Christoph Treude
In War And Peace: The Impact Of World Politics On Software Ecosystems, Raula Kula, Christoph Treude
Research Collection School Of Computing and Information Systems
Reliance on third-party libraries is now commonplace in contemporary software engineering. Being open source in nature, these libraries should advocate for a world where the freedoms and opportunities of open source software can be enjoyed by all. Yet, there is a growing concern related to maintainers using their influence to make political stances (i.e., referred to as protestware). In this paper, we reflect on the impact of world politics on software ecosystems, especially in the context of the ongoing War in Ukraine. We show three cases where world politics has had an impact on a software ecosystem, and how these …
Epic-Kitchens Visor Benchmark: Video Segmentations And Object Relations, Ahmad Ak Dar Khalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard Higgins, David Fouhey, Sanja Fidler, Dima Damen
Epic-Kitchens Visor Benchmark: Video Segmentations And Object Relations, Ahmad Ak Dar Khalil, Dandan Shan, Bin Zhu, Jian Ma, Amlan Kar, Richard Higgins, David Fouhey, Sanja Fidler, Dima Damen
Research Collection School Of Computing and Information Systems
We introduce VISOR, a new dataset of pixel annotations and a benchmark suite for segmenting hands and active objects in egocentric video. VISOR annotates videos from EPIC-KITCHENS, which comes with a new set of challenges not encountered in current video segmentation datasets. Specifically, we need to ensure both short- and long-term consistency of pixel-level annotations as objects undergo transformative interactions, e.g. an onion is peeled, diced and cooked - where we aim to obtain accurate pixel-level annotations of the peel, onion pieces, chopping board, knife, pan, as well as the acting hands. VISOR introduces an annotation pipeline, AI-powered in parts, …
Inception Transformer, Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan
Inception Transformer, Chenyang Si, Weihao Yu, Pan Zhou, Yichen Zhou, Xinchao Wang, Shuicheng Yan
Research Collection School Of Computing and Information Systems
Recent studies show that Transformer has strong capability of building long-range dependencies, yet is incompetent in capturing high frequencies that predominantly convey local information. To tackle this issue, we present a novel and general-purpose Inception Transformer, or iFormer for short, that effectively learns comprehensive features with both high- and low-frequency information in visual data. Specifically, we design an Inception mixer to explicitly graft the advantages of convolution and max-pooling for capturing the high-frequency information to Transformers. Different from recent hybrid frameworks, the Inception mixer brings greater efficiency through a channel splitting mechanism to adopt parallel convolution/max-pooling path and self-attention path …
Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang
Which Neural Network Makes More Explainable Decisions? An Approach Towards Measuring Explainability, Mengdi Zhang, Jun Sun, Jingyi Wang
Research Collection School Of Computing and Information Systems
Neural networks are getting increasingly popular thanks to their exceptional performance in solving many real-world problems. At the same time, they are shown to be vulnerable to attacks, difficult to debug and subject to fairness issues. To improve people’s trust in the technology, it is often necessary to provide some human-understandable explanation of neural networks’ decisions, e.g., why is that my loan application is rejected whereas hers is approved? That is, the stakeholder would be interested to minimize the chances of not being able to explain the decision consistently and would like to know how often and how easy it …
Predictive Self-Organizing Neural Networks For In-Home Detection Of Mild Cognitive Impairment, Seng Khoon Teh, Iris Rawtaer, Ah-Hwee Tan
Predictive Self-Organizing Neural Networks For In-Home Detection Of Mild Cognitive Impairment, Seng Khoon Teh, Iris Rawtaer, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
In-home sensing of daily living patterns from older adults coupled with machine learning is a promisingapproach to detect Mild Cognitive Impairment (MCI), a potentially reversible condition with early detectionand appropriate intervention. However, the number of subjects involved in such real-world studies istypically limited, posing the so-called small data problem to most predictive models which rely on a sizablenumber of labeled data. In this work, a predictive self-organizing neural network known as fuzzy AdaptiveResonance Associate Map (fuzzy ARAM) is proposed to detect MCI using in-home sensor data collected from aunique Singapore cross-sectional study. Specifically, mean and standard deviation of nine in-home …
Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun
Adaptive Fairness Improvement Based Causality Analysis, Mengdi Zhang, Jun Sun
Research Collection School Of Computing and Information Systems
Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i.e., accuracy). Multiple categories of fairness improving methods have been proposed for neural networks, including pre-processing, in-processing and postprocessing. Our empirical study however shows that these methods are not always effective (e.g., they may improve fairness by paying the price of huge accuracy drop) or even not helpful (e.g., they may even worsen both fairness and accuracy). In this work, we propose an approach which adaptively chooses the fairness improving method based on causality analysis. That is, we choose the …
Real World Projects, Real Faults: Evaluating Spectrum Based Fault Localization Techniques On Python Projects, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Shaowei Wang, David Lo
Real World Projects, Real Faults: Evaluating Spectrum Based Fault Localization Techniques On Python Projects, Ratnadira Widyasari, Gede Artha Azriadi Prana, Stefanus Agus Haryono, Shaowei Wang, David Lo
Research Collection School Of Computing and Information Systems
Spectrum Based Fault Localization (SBFL) is a statistical approach to identify faulty code within a program given a program spectra (i.e., records of program elements executed by passing and failing test cases). Several SBFL techniques have been proposed over the years, but most evaluations of those techniques were done only on Java and C programs, and frequently involve artificial faults. Considering the current popularity of Python, indicated by the results of the Stack Overflow survey among developers in 2020, it becomes increasingly important to understand how SBFL techniques perform on Python projects. However, this remains an understudied topic. In this …
Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li
Large-Scale Analysis Of Non-Termination Bugs In Real-World Oss Projects, Xiuhan Shi, Xiaofei Xie, Yi Li, Yao Zhang, Sen Chen, Xiaohong Li
Research Collection School Of Computing and Information Systems
Termination is a crucial program property. Non-termination bugs can be subtle to detect and may remain hidden for long before they take effect. Many real-world programs still suffer from vast consequences (e.g., no response) caused by non-termination bugs. As a classic problem, termination proving has been studied for many years. Many termination checking tools and techniques have been developed and demonstrated effectiveness on existing wellestablished benchmarks. However, the capability of these tools in finding practical non-termination bugs has yet to be tested on real-world projects. To fill in this gap, in this paper, we conducted the first large-scale empirical study …
Photovoltaic Cells For Energy Harvesting And Indoor Positioning, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef
Photovoltaic Cells For Energy Harvesting And Indoor Positioning, Hamada Rizk, Dong Ma, Mahbub Hassan, Moustafa Youssef
Research Collection School Of Computing and Information Systems
We propose SoLoc, a lightweight probabilistic fingerprinting-based technique for energy-free device-free indoor localization. The system harnesses photovoltaic currents harvested by the photovoltaic cells in smart environments for simultaneously powering digital devices and user positioning. The basic principle is that the location of the human interferes with the lighting received by the photovoltaic cells, thus producing a location fingerprint on the generated photocurrents. To ensure resilience to noisy measurements, SoLoc constructs probability distributions as a photovoltaic fingerprint at each location. Then, we employ a probabilistic graphical model for estimating the user location in the continuous space. Results show that SoLoc can …
M-Edese: Multi-Domain, Easily Deployable, And Efficiently Searchable Encryption, Jiaming Yuan, Yingjiu Li, Jianting Ning, Robert H. Deng
M-Edese: Multi-Domain, Easily Deployable, And Efficiently Searchable Encryption, Jiaming Yuan, Yingjiu Li, Jianting Ning, Robert H. Deng
Research Collection School Of Computing and Information Systems
Searchable encryption is an essential component of cryptography, which allows users to search for keywords and retrieve records from an encrypted database at cloud storage while ensuring the confidentiality of users’ queries. While most existing research on searchable encryption focuses on the single domain setting, we propose the first Multi-Domain, Easily-Deployable, Efficiently-Searchable Encryption (M-EDESE) system that allows users to query keywords cross domains with high efficiency and preserved privacy without additional cooperation from the cloud storage. In the multi-domain setting, a user who belongs to a domain can query keywords from another domain under an inter-domain partnership. Any party can …
Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang
Vlstereoset: A Study Of Stereotypical Bias In Pre-Trained Vision-Language Models, Kankan Zhou, Yibin Lai, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper we study how to measure stereotypical bias in pre-trained vision-language models. We leverage a recently released text-only dataset, StereoSet, which covers a wide range of stereotypical bias, and extend it into a vision-language probing dataset called VLStereoSet to measure stereotypical bias in vision-language models. We analyze the differences between text and image and propose a probing task that detects bias by evaluating a model’s tendency to pick stereotypical statements as captions for anti-stereotypical images. We further define several metrics to measure both a vision-language model’s overall stereotypical bias and its intra-modal and inter-modal bias. Experiments on six …
Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang
Mando-Guru: Vulnerability Detection For Smart Contract Source Code By Heterogeneous Graph Embeddings, Huu Hoang Nguyen, Nhat Minh Nguyen, Hong-Phuc Doan, Zahrai Ahmadi, Thanh Nam Doan, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Smart contracts are increasingly used with blockchain systems for high-value applications. It is highly desired to ensure the quality of smart contract source code before they are deployed. This paper proposes a new deep learning-based tool, MANDO-GURU, that aims to accurately detect vulnerabilities in smart contracts at both coarse-grained contract-level and fine-grained line-level. Using a combination of control-flow graphs and call graphs of Solidity code, we design new heterogeneous graph attention neural networks to encode more structural and potentially semantic relations among different types of nodes and edges of such graphs and use the encoded embeddings of the graphs and …
Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang
Codematcher: A Tool For Large-Scale Code Search Based On Query Semantics Matching, Chao Liu, Xuanlin Bao, Xin Xia, Meng Yan, David Lo, Ting Zhang
Research Collection School Of Computing and Information Systems
Due to the emergence of large-scale codebases, such as GitHub and Gitee, searching and reusing existing code can help developers substantially improve software development productivity. Over the years, many code search tools have been developed. Early tools leveraged the information retrieval (IR) technique to perform an efficient code search for a frequently changed large-scale codebase. However, the search accuracy was low due to the semantic mismatch between query and code. In the recent years, many tools leveraged Deep Learning (DL) technique to address this issue. But the DL-based tools are slow and the search accuracy is unstable.In this paper, we …
Autopruner: Transformer-Based Call Graph Pruning, Cong Thanh Le, Hong Jin Kang, Truong Giang Nguyen, Stefanus Agus Haryono, David Lo, Xuan-Bach D. Le, Huynh Quyet Thang
Autopruner: Transformer-Based Call Graph Pruning, Cong Thanh Le, Hong Jin Kang, Truong Giang Nguyen, Stefanus Agus Haryono, David Lo, Xuan-Bach D. Le, Huynh Quyet Thang
Research Collection School Of Computing and Information Systems
Constructing a static call graph requires trade-offs between soundness and precision. Program analysis techniques for constructing call graphs are unfortunately usually imprecise. To address this problem, researchers have recently proposed call graph pruning empowered by machine learning to post-process call graphs constructed by static analysis. A machine learning model is built to capture information from the call graph by extracting structural features for use in a random forest classifier. It then removes edges that are predicted to be false positives. Despite the improvements shown by machine learning models, they are still limited as they do not consider the source code …
A Quality Metric For K-Means Clustering Based On Centroid Locations, Manoj Thulasidas
A Quality Metric For K-Means Clustering Based On Centroid Locations, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
K-Means clustering algorithm does not offer a clear methodology to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. In this paper, we present a new metric for clustering quality and describe its use for K selection. The proposed metric, based on the locations of the centroids, as well as the desired properties of the clusters, is developed in two stages. In the initial stage, we take into account the full covariance matrix of the clustering variables, thereby making it mathematically similar to a reduced chi2. We then extend it to account for …
Dynamic Temporal Filtering In Video Models, Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Chong-Wah Ngo, Tao Mei
Dynamic Temporal Filtering In Video Models, Fuchen Long, Zhaofan Qiu, Yingwei Pan, Ting Yao, Chong-Wah Ngo, Tao Mei
Research Collection School Of Computing and Information Systems
Video temporal dynamics is conventionally modeled with 3D spatial-temporal kernel or its factorized version comprised of 2D spatial kernel and 1D temporal kernel. The modeling power, nevertheless, is limited by the fixed window size and static weights of a kernel along the temporal dimension. The pre-determined kernel size severely limits the temporal receptive fields and the fixed weights treat each spatial location across frames equally, resulting in sub-optimal solution for longrange temporal modeling in natural scenes. In this paper, we present a new recipe of temporal feature learning, namely Dynamic Temporal Filter (DTF), that novelly performs spatial-aware temporal modeling in …