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Research Collection School Of Computing and Information Systems

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Visilience: An Interactive Visualization Framework For Resilience Analysis Using Control-Flow Graph, Hailong Jiang, Shaolun Ruan, Bo Fang, Yong Wang, Qiang Guan Oct 2023

Visilience: An Interactive Visualization Framework For Resilience Analysis Using Control-Flow Graph, Hailong Jiang, Shaolun Ruan, Bo Fang, Yong Wang, Qiang Guan

Research Collection School Of Computing and Information Systems

Soft errors have become one of the main concerns for the resilience of HPC applications, as these errors can cause HPC applications to generate serious outcomes such as silent data corruption (SDC). Many approaches have been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to perform resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of detail, which can create obstacles to compare and …

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Prirpt: Practical Blockchain-Based Privacy-Preserving Reporting System With Rewards, Rui. Shi, Yang Yang, Huamin. Feng, Feng. Yuan, Huiqin. Xie, Jianyi. Zhang Oct 2023

Prirpt: Practical Blockchain-Based Privacy-Preserving Reporting System With Rewards, Rui. Shi, Yang Yang, Huamin. Feng, Feng. Yuan, Huiqin. Xie, Jianyi. Zhang

Research Collection School Of Computing and Information Systems

In order to obtain evidence of a crime timely, most authorities encourage whistleblowers to provide valuable reports by rewarding them with prizes. However, criminals will try their best to delete or tamper with the reports and even threaten and revenge the whistleblowers to escape punishment. Hence, to make the reporting system work, it is essential to ensure the integrity of reported messages and the anonymity of the reporting and rewarding procedures in the reporting system. Most existing schemes for this problem are generally based on ring signatures, which incur high computational overhead and imperfect anonymity. In this paper, we introduce …

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Boosting Adversarial Training In Safety-Critical Systems Through Boundary Data Selection, Yifan Jia, Christopher M. Poskitt, Peixin Zhang, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay Oct 2023

Boosting Adversarial Training In Safety-Critical Systems Through Boundary Data Selection, Yifan Jia, Christopher M. Poskitt, Peixin Zhang, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay

Research Collection School Of Computing and Information Systems

AI-enabled collaborative robots are designed to be used in close collaboration with humans, thus requiring stringent safety standards and quick response times. Adversarial attacks pose a significant threat to the deep learning models of these systems, making it crucial to develop methods to improve the models' robustness against them. Adversarial training is one approach to improve their robustness: it works by augmenting the training data with adversarial examples. This, unfortunately, comes with the cost of increased computational overhead and extended training times. In this work, we balance the need for additional adversarial data with the goal of minimizing the training …

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Ubisurface: A Robotic Touch Surface For Supporting Mid-Air Planar Interactions In Room-Scale Vr, Ryota Gomi, Kazuki Takashima, Yuki Onishi, Kazuyuki Fujita, Yoshifumi Kitamura Oct 2023

Ubisurface: A Robotic Touch Surface For Supporting Mid-Air Planar Interactions In Room-Scale Vr, Ryota Gomi, Kazuki Takashima, Yuki Onishi, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

Room-scale VR has been considered an alternative to physical office workspaces. For office activities, users frequently require planar input methods, such as typing or handwriting, to quickly record annotations to virtual content. However, current off-The-shelf VR HMD setups rely on mid-Air interactions, which can cause arm fatigue and decrease input accuracy. To address this issue, we propose UbiSurface, a robotic touch surface that can automatically reposition itself to physically present a virtual planar input surface (VR whiteboard, VR canvas, etc.) to users and to permit them to achieve accurate and fatigue-less input while walking around a virtual room. We design …

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Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang Oct 2023

Invariant Training 2d-3d Joint Hard Samples For Few-Shot Point Cloud Recognition, Xuanyu Yi, Jiajun Deng, Qianru Sun, Xian-Sheng Hua, Joo-Hwee Lim, Hanwang Zhang

Research Collection School Of Computing and Information Systems

We tackle the data scarcity challenge in few-shot point cloud recognition of 3D objects by using a joint prediction from a conventional 3D model and a well-pretrained 2D model. Surprisingly, such an ensemble, though seems trivial, has hardly been shown effective in recent 2D-3D models. We find out the crux is the less effective training for the “joint hard samples”, which have high confidence prediction on different wrong labels, implying that the 2D and 3D models do not collaborate well. To this end, our proposed invariant training strategy, called INVJOINT, does not only emphasize the training more on the hard …

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Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro Oct 2023

Residual Pattern Learning For Pixel-Wise Out-Of-Distribution Detection In Semantic Segmentation, Y Liu, Choubo Ding, Yu Tian, Guansong Pang, Vasileios Belagiannis, Ian Reid, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Semantic segmentation models classify pixels into a set of known ("in-distribution") visual classes. When deployed in an open world, the reliability of these models depends on their ability to not only classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has motivated the design of methods based on model re-training using synthetic training images that include OoD visual objects. Although successful, these re-trained methods have two issues: 1) their in-distribution segmentation accuracy may drop during re-training, and 2) their OoD detection accuracy does not generalise well to new contexts (e.g., …

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Designing A Human-Centered Intelligent System To Monitor & Explain Abnormal Patterns Of Older Adults, Min Hun Lee, Daniel P. Siewiorek, Alexandre Bernardino Oct 2023

Designing A Human-Centered Intelligent System To Monitor & Explain Abnormal Patterns Of Older Adults, Min Hun Lee, Daniel P. Siewiorek, Alexandre Bernardino

Research Collection School Of Computing and Information Systems

Older adult care technologies are increasingly explored to support the independent living of older adults by monitoring their abnormal activities and informing caregivers to provide intervention if necessary. However, the adoption of these technologies remains challenging due to several factors (e.g. lack of usability). In this work, we present a human-centered, intelligent system for older adult care. Our proposed designs of the system were created based on the findings from a focus group session with caregivers. This system monitors the abnormal activities of an older adult using wireless motion sensors and machine learning models. In addition, unlike previous work that …

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Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng Oct 2023

Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an Owner-free Distributed Symmetric searchable encryption supporting Conjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) …

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Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein Oct 2023

Dexbert: Effective, Task-Agnostic And Fine-Grained Representation Learning Of Android Bytecode, Tiezhu Sun, Kevin Allix, Kisub Kim, Xin Zhou, Dongsun Kim, David Lo, Tegawendé F. Bissyande, Jacques Klein

Research Collection School Of Computing and Information Systems

The automation of an increasingly large number of software engineering tasks is becoming possible thanks to Machine Learning (ML). One foundational building block in the application of ML to software artifacts is the representation of these artifacts ( e.g. , source code or executable code) into a form that is suitable for learning. Traditionally, researchers and practitioners have relied on manually selected features, based on expert knowledge, for the task at hand. Such knowledge is sometimes imprecise and generally incomplete. To overcome this limitation, many studies have leveraged representation learning, delegating to ML itself the job of automatically devising suitable …

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Diffuse3d: Wide-Angle 3d Photography Via Bilateral Diffusion, Yutao Jiang, Yang Zhou, Yuan Liang, Wenxi Liu, Jianbo Jiao, Yuhui Quan, Shengfeng He Oct 2023

Diffuse3d: Wide-Angle 3d Photography Via Bilateral Diffusion, Yutao Jiang, Yang Zhou, Yuan Liang, Wenxi Liu, Jianbo Jiao, Yuhui Quan, Shengfeng He

Research Collection School Of Computing and Information Systems

This paper aims to resolve the challenging problem of wide-angle novel view synthesis from a single image, a.k.a. wide-angle 3D photography. Existing approaches rely on local context and treat them equally to inpaint occluded RGB and depth regions, which fail to deal with large-region occlusion (i.e., observing from an extreme angle) and foreground layers might blend into background inpainting. To address the above issues, we propose Diffuse3D which employs a pre-trained diffusion model for global synthesis, while amending the model to activate depth-aware inference. Our key insight is to alter the convolution mechanism in the denoising process. We inject depth …

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Supporting Artefact Awareness In Partially-Replicated Workspaces, Emran Poh, Anthony Tang, Jenanie S. Lee, Zhao Shengdong Oct 2023

Supporting Artefact Awareness In Partially-Replicated Workspaces, Emran Poh, Anthony Tang, Jenanie S. Lee, Zhao Shengdong

Research Collection School Of Computing and Information Systems

Using Cross Reality (CR) approaches for remote collaboration will often result in partially-replicated workspaces. Here, workspace artefacts are not equally accessible - i.e. a physical artefact may only be manipulated by one collaborator - and in general, the artefacts become desynchronised over time. In this paper, we introduce a framework for artefact awareness that can help collaborators maintain an understanding of each others' manipulations with workspace artefacts. We illustrate our design explorations through sketches, and outline how we aim to study the effectiveness and utility of artefact awareness in cross reality remote collaboration. In our work, we expect to show …

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Achieving Efficient And Privacy-Preserving Neural Network Training And Prediction In Cloud Environments, Chuan Zhang, Chenfei Hu, Tong Wu, Liehuang Zhu, Ximeng Liu Oct 2023

Achieving Efficient And Privacy-Preserving Neural Network Training And Prediction In Cloud Environments, Chuan Zhang, Chenfei Hu, Tong Wu, Liehuang Zhu, Ximeng Liu

Research Collection School Of Computing and Information Systems

The neural network has been widely used to train predictive models for applications such as image processing, disease prediction, and face recognition. To produce more accurate models, powerful third parties (e.g., clouds) are usually employed to collect data from a large number of users, which however may raise concerns about user privacy. In this paper, we propose an Efficient and Privacy-preserving Neural Network scheme, named EPNN, to deal with the privacy issues in cloud-based neural networks. EPNN is designed based on a two-cloud model and techniques of data perturbation and additively homomorphic cryptosystem. This scheme enables two clouds to cooperatively …

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Problems In Microservice Development: Supporting Visualisation, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner Oct 2023

Problems In Microservice Development: Supporting Visualisation, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

In microservice architectures, developers can face significant problems understanding the structure of the system and how the different microservices interact. This difficulty results from the distributed nature of the system, and the abundance of inter-service communication within the architecture. We want to determine if network visualisations can address these problems given their ability to convey complex topologies. However, to identify what architectural characteristics should be visualised, and how this should be done, we must first determine the needs of microservice developers. This paper identifies and presents the impact and frequency of problems faced by a cohort of microservice developers using …

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Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic Oct 2023

Reachability Poorman Discrete-Bidding Games, Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Dorde Zikelic

Research Collection School Of Computing and Information Systems

We consider bidding games, a class of two-player zerosum graph games. The game proceeds as follows. Both players have bounded budgets. A token is placed on a vertex of a graph, in each turn the players simultaneously submit bids, and the higher bidder moves the token, where we break bidding ties in favor of Player 1. Player 1 wins the game iff the token visits a designated target vertex. Weconsider, for the first time, poorman discrete-bidding in which the granularity of the bids is restricted and the higher bid is paid to the bank. Previous work either did not impose …

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Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li Oct 2023

Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li

Research Collection School Of Computing and Information Systems

Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense …

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Robust Bidirectional Poly-Matching, Ween Jiann Lee, Maksim Tkachenko, Hady Wirawan Lauw Oct 2023

Robust Bidirectional Poly-Matching, Ween Jiann Lee, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A fundamental problem in many scenarios is to match entities across two data sources. It is frequently presumed in prior work that entities to be matched are of comparable granularity. In this work, we address one-to-many or poly-matching in the scenario where entities have varying granularity. A distinctive feature of our problem is its bidirectional nature, where the 'one' or the 'many' could come from either source arbitrarily. Moreover, to deal with diverse entity representations that give rise to noisy similarity values, we incorporate novel notions of receptivity and reclusivity into a robust matching objective. As the optimal solution to …

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Constructing Cyber-Physical System Testing Suites Using Active Sensor Fuzzing, Fan. Zhang, Qianmei. Wu, Bohan. Xuan, Yuqi. Chen, Wei. Lin, Christopher M. Poskitt, Jun Sun, Binbin. Chen Oct 2023

Constructing Cyber-Physical System Testing Suites Using Active Sensor Fuzzing, Fan. Zhang, Qianmei. Wu, Bohan. Xuan, Yuqi. Chen, Wei. Lin, Christopher M. Poskitt, Jun Sun, Binbin. Chen

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) automating critical public infrastructure face a pervasive threat of attack, motivating research into different types of countermeasures. Assessing the effectiveness of these countermeasures is challenging, however, as benchmarks are difficult to construct manually, existing automated testing solutions often make unrealistic assumptions, and blindly fuzzing is ineffective at finding attacks due to the enormous search spaces and resource requirements. In this work, we propose active sensor fuzzing , a fully automated approach for building test suites without requiring any a prior knowledge about a CPS. Our approach employs active learning techniques. Applied to a real-world water treatment system, …

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Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro Oct 2023

Unsupervised Anomaly Detection In Medical Images With A Memory-Augmented Multi-Level Cross-Attentional Masked Autoencoder, Yu Tian, Guansong Pang, Yuyuan Liu, Chong Wang, Yuanhong Chen, Fengbei Liu, Rajvinder Singh, Johan W. Verjans, Mengyu Wang, Gustavo Carneiro

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) aims to find anomalous images by optimising a detector using a training set that contains only normal images. UAD approaches can be based on reconstruction methods, self-supervised approaches, and Imagenet pre-trained models. Reconstruction methods, which detect anomalies from image reconstruction errors, are advantageous because they do not rely on the design of problem-specific pretext tasks needed by self-supervised approaches, and on the unreliable translation of models pre-trained from non-medical datasets. However, reconstruction methods may fail because they can have low reconstruction errors even for anomalous images. In this paper, we introduce a new reconstruction-based UAD approach …

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Underwater Image Translation Via Multi-Scale Generative Adversarial Network, Dongmei Yang, Tianzi Zhang, Boquan Li, Menghao Li, Weijing Chen, Xiaoqing Li, Xingmei Wang Oct 2023

Underwater Image Translation Via Multi-Scale Generative Adversarial Network, Dongmei Yang, Tianzi Zhang, Boquan Li, Menghao Li, Weijing Chen, Xiaoqing Li, Xingmei Wang

Research Collection School Of Computing and Information Systems

The role that underwater image translation plays assists in generating rare images for marine applications. However, such translation tasks are still challenging due to data lacking, insufficient feature extraction ability, and the loss of content details. To address these issues, we propose a novel multi-scale image translation model based on style-independent discriminators and attention modules (SID-AM-MSITM), which learns the mapping relationship between two unpaired images for translation. We introduce Convolution Block Attention Modules (CBAM) to the generators and discriminators of SID-AM-MSITM to improve its feature extraction ability. Moreover, we construct style-independent discriminators that enable the discriminant results of SID-AM-MSITM to …

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Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang Sep 2023

Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang

Research Collection School Of Computing and Information Systems

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method …

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An Activity System-Based Perspective Of Generative Ai: Challenges And Research Directions, Fiona Fui-Hoon Nah, Jingyuan Cai, Ruilin Zheng, Natalie Pang Sep 2023

An Activity System-Based Perspective Of Generative Ai: Challenges And Research Directions, Fiona Fui-Hoon Nah, Jingyuan Cai, Ruilin Zheng, Natalie Pang

Research Collection School Of Computing and Information Systems

With its remarkable ability to generate content, generative artificial intelligence (GAI) has been recognized as a milestone in the development of artificial general intelligence. To understand the challenges, potential impact, and implications associated with GAI, we adopt a socio-technical perspective to analyze them. First, we identify the key characteristics of GAI, which include content generation, generalization ability, and reinforcement learning based on human feedback. Next, we address technological, ethical, societal, economic, regulatory, and governance challenges. Finally, we deploy activity theory to explore research directions in GAI. Research questions that warrant further investigation include how GAI may impact the future of …

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Literature Review In The Generative Ai Era: How To Make A Compelling Contribution, Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah Sep 2023

Literature Review In The Generative Ai Era: How To Make A Compelling Contribution, Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

As we write this editorial for this special issue, we are amidst the significant technological changes that are continuing to shape society. Since the emergence of ChatGPT in November 2022, humanity has become aware of the potential of generative AI (i.e., AI that can generate content) and large language models (LLMs) (i.e., AI models trained on a massive corpus of unstructured data). There is growing debate and discussion about the promise and perils of generative AI for the future of work, and academia is not immune. Premier journals in the IS domain, such as Information Systems Research, have published editorials …

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Testsgd: Interpretable Testing Of Neural Networks Against Subtle Group Discrimination, Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun Sep 2023

Testsgd: Interpretable Testing Of Neural Networks Against Subtle Group Discrimination, Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun

Research Collection School Of Computing and Information Systems

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains. One widely concerning type of discrimination, testing against group discrimination, mostly hidden, is much less studied, compared with identifying individual discrimination. In this work, we propose TestSGD, an interpretable testing approach which systematically identifies and measures hidden (which we call ‘subtle’) group discrimination of a neural network characterized by conditions over combinations of the sensitive attributes. Specifically, given a neural network, TestSGD first automatically generates an interpretable rule set which categorizes the input space into two groups. Alongside, TestSGD …

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Automated Question Title Reformulation By Mining Modifcation Logs From Stack Overflow, Ke Liu, Xiang Chen, Chunyang Chen, Xiaofei Xie, Zhanqi Cui Sep 2023

Automated Question Title Reformulation By Mining Modifcation Logs From Stack Overflow, Ke Liu, Xiang Chen, Chunyang Chen, Xiaofei Xie, Zhanqi Cui

Research Collection School Of Computing and Information Systems

In Stack Overflow, developers may not clarify and summarize the critical problems in the question titles due to a lack of domain knowledge or poor writing skills. Previous studies mainly focused on automatically generating the question titles by analyzing the posts’ problem descriptions and code snippets. In this study, we aim to improve title quality from the perspective of question title reformulation and propose a novel approach QETRA motivated by the findings of our formative study. Specifically, by mining modification logs from Stack Overflow, we first extract title reformulation pairs containing the original title and the reformulated title. Then we …

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Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw Sep 2023

Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users …

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Revisiting The Identification Of The Co-Evolution Of Production And Test Code, Weifeng Sun, Meng Yan, Zhongxin Liu, Xin Xia, Yan Lei, David Lo Sep 2023

Revisiting The Identification Of The Co-Evolution Of Production And Test Code, Weifeng Sun, Meng Yan, Zhongxin Liu, Xin Xia, Yan Lei, David Lo

Research Collection School Of Computing and Information Systems

Many software processes advocate that the test code should co-evolve with the production code. Prior work usually studies such co-evolution based on production-test co-evolution samples mined from software repositories. A production-test co-evolution sample refers to a pair of a test code change and a production code change where the test code change triggers or is triggered by the production code change. The quality of the mined samples is critical to the reliability of research conclusions. Existing studies mined production-test co-evolution samples based on the following assumption: if a test class and its associated production class change together in one commit, …

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Fair Cloud Auditing Based On Blockchain For Resource-Constrained Iot Devices, Lei Zhou, Anmin Fu, Guomin Yang, Yansong Gao, Shui Yu, Robert H. Deng Sep 2023

Fair Cloud Auditing Based On Blockchain For Resource-Constrained Iot Devices, Lei Zhou, Anmin Fu, Guomin Yang, Yansong Gao, Shui Yu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Internet of Things (IoT) devices upload their data into the cloud for storage because of their limited resources. However, cloud storage data has been subject to potential integrity threats, and consequently auditing techniques are demanded to ensure the integrity of stored data. Unfortunately, existing auditing approaches require owners to undertake expensive tag calculations, which is unsuitable for resource-constrained IoT devices. To resolve the issue, we present a Fair Cloud Auditing proposal by employing the Blockchain (FCAB). We combine certificateless signatures with the designed dynamic structure to constructively offload the cost of tag computation from the IoT device to the introduced …

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Autoconf: Automated Configuration Of Unsupervised Learning Systems Using Metamorphic Testing And Bayesian Optimization, Lwin Khin Shar, Goknil Arda, Erik Johannes Husom, Sagar Sen Sen, Naing Tun Yan, Kisub Kim Sep 2023

Autoconf: Automated Configuration Of Unsupervised Learning Systems Using Metamorphic Testing And Bayesian Optimization, Lwin Khin Shar, Goknil Arda, Erik Johannes Husom, Sagar Sen Sen, Naing Tun Yan, Kisub Kim

Research Collection School Of Computing and Information Systems

Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the com- plexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for config- uring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a …

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Hercules: Boosting The Performance Of Privacy-Preserving Federated Learning, Guowen Xu, Xingshuo Han, Shengmin Xu, Tianwei Zhang, Hongwei Li, Xinyi Huang, Robert H. Deng Sep 2023

Hercules: Boosting The Performance Of Privacy-Preserving Federated Learning, Guowen Xu, Xingshuo Han, Shengmin Xu, Tianwei Zhang, Hongwei Li, Xinyi Huang, Robert H. Deng

Research Collection School Of Computing and Information Systems

In this paper, we address the problem of privacy-preserving federated neural network training with N users. We present Hercules, an efficient and high-precision training framework that can tolerate collusion of up to N−1 users. Hercules follows the POSEIDON framework proposed by Sav et al. (NDSS’21), but makes a qualitative leap in performance with the following contributions: (i) we design a novel parallel homomorphic computation method for matrix operations, which enables fast Single Instruction and Multiple Data (SIMD) operations over ciphertexts. For the multiplication of two h×h dimensional matrices, our method reduces the computation complexity from O(h3) to O(h) . This …

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Understanding Multi-Homing And Switching By Platform Drivers, Xiaotong Guo, Andreas Haupt, Hai Wang, Rida Qadri, Jinhua Zhao Sep 2023

Understanding Multi-Homing And Switching By Platform Drivers, Xiaotong Guo, Andreas Haupt, Hai Wang, Rida Qadri, Jinhua Zhao

Research Collection School Of Computing and Information Systems

Freelance drivers in the shared mobility market frequently switch or work for multiple platforms, affecting driver labor supply. Due to the importance of driver labor supply for the shared mobility market, understanding drivers’ switching and multi-homing behavior is vital to managing service quality on – and effective regulation of – mobility platforms. However, a lack of individual-level data on driver behavior has thus far impeded a deeper understanding. This paper taxonomizes and estimates perceived switching and multi-homing frictions on mobility platforms. Based on a structural model of driver labor supply, we estimate switching and multi-homing costs in a platform duopoly …

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