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Demo2test: Transfer Testing Of Agent In Competitive Environment With Failure Demonstrations, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Dandan Wang, Qing Wang, Fanjiang Xu
Demo2test: Transfer Testing Of Agent In Competitive Environment With Failure Demonstrations, Jianming Chen, Yawen Wang, Junjie Wang, Xiaofei Xie, Dandan Wang, Qing Wang, Fanjiang Xu
Research Collection School Of Computing and Information Systems
The competitive game between agents exists in many critical applications, such as military unmanned aerial vehicles. It is urgent to test these agents to reduce the significant losses caused by their failures. Existing studies mainly are to construct a testing agent that competes with the target agent to induce its failures. These approaches usually focus on a single task, requiring much more time for multi-task testing. However, if the previously tested tasks (source tasks) and the task to be tested (target task) share similar agents or task objectives, the transferable knowledge in source tasks can potentially increase the effectiveness of …
Dims: Distributed Index For Similarity Search In Metric Spaces, Yifan Zhu, Chengyang Luo, Tang Qian, Lu Chen, Yunjun Gao, Baihua Zheng
Dims: Distributed Index For Similarity Search In Metric Spaces, Yifan Zhu, Chengyang Luo, Tang Qian, Lu Chen, Yunjun Gao, Baihua Zheng
Research Collection School Of Computing and Information Systems
Similarity search finds objects that are similar to a given query object based on a similarity metric. As the amount and variety of data continue to grow, similarity search in metric spaces has gained significant attention. Metric spaces can accommodate any type of data and support flexible distanc e metrics, making similarity search in metric spaces beneficial for many real-world applications, such as multimedia retrieval, personalized recommendation, trajectory analytics, data mining, decision planning, and distributed servers. However, existing studies mostly focus on indexing metric spaces on a single machine, which faces efficiency and scalability limitations with increasing data volume and …
Learning To Rank Aspects And Opinions For Comparative Explanations, Trung Hoang Le, Hady Wirawan Lauw
Learning To Rank Aspects And Opinions For Comparative Explanations, Trung Hoang Le, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Comparative recommendation explanations help to make sense of recommendations by comparing a recommended item along some aspects of interest with one or many items being considered. This work extends the notion of comparative explanations, by going beyond merely better/worse statements, to further incorporate aspect-level opinions for more informative comparisons. To enhance the quality of both the personalized recommendation and the explanation, we incorporate optimization objectives that preserve relative rankings of aspects and opinions, in addition to the classical rankings of overall preferences for items. We integrate the multiple ranking objectives and multi-tensor factorization together. Experiments on datasets of different domains …
Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun
Synthesizing Multi-Person And Rare Pose Images For Human Pose Estimation, Liuqing Zhao, Zichen Tian, Zou Peng, Richang Hong, Qianru Sun
Research Collection School Of Computing and Information Systems
Human pose estimation (HPE) models underperform in recognizing rare poses because they suffer from data imbalance problems (i.e., there are few image samples for rare poses) in their training datasets. From a data perspective, the most intuitive solution is to synthesize data for rare poses. Specifically, the rule-based methods apply manual manipulations (such as Cutout and GridMask) to the existing data, so the limited diversity of the data constrains the model. An alternative method is to learn the underlying data distribution via deep generative models (such as ControlNet and HumanSD) and then sample “new data” from the distribution. This works …
Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin
Charge Your Clients: Payable Secure Computation And Its Applications, Cong Zhang, Liqiang Peng, Weiran Liu, Shuaishuai Li, Meng Hao, Lei Zhang, Dongdai Lin
Research Collection School Of Computing and Information Systems
The online realm has witnessed a surge in the buying and selling of data, prompting the emergence of dedicated data marketplaces. These platforms cater to servers (sellers), enabling them to set prices for access to their data, and clients (buyers), who can subsequently purchase these data, thereby streamlining and facilitating such transactions. However, the current data market is primarily confronted with the following issues. Firstly, they fail to protect client privacy, presupposing that clients submit their queries in plaintext. Secondly, these models are susceptible to being impacted by malicious client behavior, for example, enabling clients to potentially engage in arbitrage …
The Gradient Puppeteer: Adversarial Domination In Gradient Leakage Attacks Through Model Poisoning, Kunlan Xiang, Haomiao Yang, Meng Hao, Shaofeng Li, Haoxin Wang, Zikang Ding, Wenbo Jiang, Tianwei Zhang
The Gradient Puppeteer: Adversarial Domination In Gradient Leakage Attacks Through Model Poisoning, Kunlan Xiang, Haomiao Yang, Meng Hao, Shaofeng Li, Haoxin Wang, Zikang Ding, Wenbo Jiang, Tianwei Zhang
Research Collection School Of Computing and Information Systems
In Federated Learning (FL), clients share gradients with a central server while keeping their data local. However, malicious servers could deliberately manipulate the models to reconstruct clients' data from shared gradients, posing significant privacy risks. Although such Active Gradient Leakage Attacks (AGLAs) have been widely studied, they suffer from two severe limitations: 1) coverage: no existing AGLAs can reconstruct all samples in a batch from the shared gradients; 2) stealthiness: no existing AGLAs can evade principled checks of clients. In this paper, we address these limitations with two core contributions. First, we introduce a new theoretical analysis approach, which uniformly …
Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang
Fuzzing Drones For Anomaly Detection: A Systematic Literature Review, Vikas Kumar Malviya, Wei Minn, Lwin Khin Shar, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Drones, also referred to as Unmanned Aerial Vehicles (UAVs), are becoming popular today due to their uses in different fields and recent technological advancements which provide easy control of UAVs via mobile apps. However, UAVs may contain vulnerabilities or software bugs that cause serious safety and security concerns. For example, the communication protocol used by the UAV may contain authentication and authorization vulnerabilities, which may be exploited by attackers to gain remote access over the UAV. Drones must therefore undergo extensive testing before being released or deployed to identify and fix any software bugs or security vulnerabilities. Fuzzing is one …
A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria
A Review Of Chinese Sentiment Analysis: Subjects, Methods, And Trends, Zhaoxia Wang, Donghao Huang, Jingfeng Cui, Xinyue Zhang, Seng-Beng Ho, Erik Cambria
Research Collection School Of Computing and Information Systems
Sentiment analysis has emerged as a prominent research domain within the realm of natural language processing, garnering increasing attention and a growing body of literature. While numerous literature reviews have examined sentiment analysis techniques, methods, topics and applications, there remains a gap in the literature concerning thematic trends and research methodologies in sentiment analysis, particularly in the context of Chinese text. This study addresses this gap by presenting a comprehensive survey dedicated to the progression of research subjects, methods and trends in sentiment analysis of Chinese text. Employing a framework that combines keyword co-occurrence analysis with a sophisticated community detection …
Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo
Neuron Semantic-Guided Test Generation For Deep Neural Networks Fuzzing, Li Huang, Weifeng Sun, Meng Yan, Zhongxin Liu, Yan Lei, David Lo
Research Collection School Of Computing and Information Systems
In recent years, significant progress has been made in testing methods for deep neural networks (DNNs) to ensure their correctness and robustness. Coverage-guided criteria, such as neuron-wise, layer-wise, and path-/trace-wise, have been proposed for DNN fuzzing. However, existing coverage-based criteria encounter performance bottlenecks for several reasons: Testing Adequacy: Partial neural coverage criteria have been observed to achieve full coverage using only a small number of test inputs. In this case, increasing the number of test inputs does not consistently improve the quality of models. Interpretability: The current coverage criteria lack interpretability. Consequently, testers are unable to identify and understand which …
Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang
Marrying Top-K With Skyline Queries: Operators With Relaxed Preference Input And Controllable Output Size, Kyriakos Mouratidis, Keming Li, Bo Tang
Research Collection School Of Computing and Information Systems
The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records' attributes (typically, using a top-k query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …
Transforming Urban Dynamics: Harnessing Large Language Models For Smarter Mobility, Hao Xue, Ming Jin, Shirui Pan, Flora Salim, Guansong Pang
Transforming Urban Dynamics: Harnessing Large Language Models For Smarter Mobility, Hao Xue, Ming Jin, Shirui Pan, Flora Salim, Guansong Pang
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) has the potential to analyze mobility data and make mobility systems smarter by leveraging diverse data sources such as geospatial data, transportation logs, and real-time sensor data to optimize traffic flow, enhance public transportation systems, and support the development of autonomous vehicles. With the newly emerged generative AI paradigm, exemplified by large language models (LLMs), there is great potential to transform the current AI applications in mobility, transportation, and urban domains. This article provides an overview of recent efforts and aims to shed light on the challenges and future opportunities to facilitate the adaptation of LLMs for …
Performance Evaluation Of Newsql Databases In A Distributed Architecture, Zhiyao Zhang, Alan @ Ali Madjelisi Megargel, Lingxiao Jiang
Performance Evaluation Of Newsql Databases In A Distributed Architecture, Zhiyao Zhang, Alan @ Ali Madjelisi Megargel, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
In the last decade, application architectures have evolved drastically, moving from monolithic architectures to distributed architectures where deployment has shifted from dedicated on-premises servers to the cloud. Distributed architectures and cloud computing has enabled businesses to scale their application components across different geographical locations. While it is easy to scale the application layer, scaling its database layer that relies on traditional SQL databases is challenging and often is a common source of bottlenecks when it comes to application performance. This paper evaluates the performance characteristics between two NewSQL databases solutions, MySQL NDB Cluster vs. TIBCO ActiveSpaces IMDG. Serving as an …
Interactive Video Search With Multi-Modal Llm Video Captioning, Yu-Tong Cheng, Jiaxin Wu, Zhixin Ma, Jiangshan He, Xiao-Yong Wei, Chong-Wah Ngo
Interactive Video Search With Multi-Modal Llm Video Captioning, Yu-Tong Cheng, Jiaxin Wu, Zhixin Ma, Jiangshan He, Xiao-Yong Wei, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Cross-modal representation learning is essential for interactive text-to-video search tasks. However, the representation learning is limited by the size and quality of video-caption pairs. To improve the search accuracy, we propose to enlarge the size of available video-caption pairs by leveraging multi-model LLM on video captioning. Specifically, we use LLM to generate video captions for a large video collection (i.e., WebVid dataset) and use the generated video-caption pairs to pre-train a text-to-video search model. Additionally, we use LLM to generate fine-grained captions for test video collections to enable text-to-caption retrieval. Furthermore, we build a semantic overview of the retrieved rank …
Adapting Installation Instructions In Rapidly Evolving Software Ecosystems, Haoyu Gao, Christoph Treude, Mansooreh Zahedi
Adapting Installation Instructions In Rapidly Evolving Software Ecosystems, Haoyu Gao, Christoph Treude, Mansooreh Zahedi
Research Collection School Of Computing and Information Systems
files play an important role in providing installation-related instructions to software users and are widely used in open source software systems on platforms such as GitHub. Software projects evolve rapidly alongside their dependencies in dynamic software ecosystems, requiring frequent updates to installation instructions. These instructions are crucial for users to start with a software project. Despite their significance, there is a lack of systematic understanding regarding the documentation efforts invested in README files and the triggers behind them. To fill the research gap, we conducted a qualitative study, investigating 400 GitHub repositories with 1,163 README commits that focused on updates …
Adversarial Generative Flow Network For Solving Vehicle Routing Problems, Ni Zhang, Jingfeng Yang, Zhiguang Cao, Xu Chi
Adversarial Generative Flow Network For Solving Vehicle Routing Problems, Ni Zhang, Jingfeng Yang, Zhiguang Cao, Xu Chi
Research Collection School Of Computing and Information Systems
Recent research into solving vehicle routing problems (VRPs) has gained significant traction, particularly through the application of deep (reinforcement) learning for end-to-end solution construction. However, many current construction-based neural solvers predominantly utilize Transformer architectures, which can face scalability challenges and struggle to produce diverse solutions. To address these limitations, we introduce a novel framework beyond Transformer-based approaches, i.e., Adversarial Generative Flow Networks (AGFN). This framework integrates the generative flow network (GFlowNet)-a probabilistic model inherently adept at generating diverse solutions (routes)-with a complementary model for discriminating (or evaluating) the solutions. These models are trained alternately in an adversarial manner to improve …
Double Oracle Neural Architecture Search For Game Theoretic Deep Learning Models, Aye Phyu Phyu Aung, Xinrun Wang, Ruiyu Wang, Hau Chan, Bo An, Xiaoli Li, J. Senthilnath
Double Oracle Neural Architecture Search For Game Theoretic Deep Learning Models, Aye Phyu Phyu Aung, Xinrun Wang, Ruiyu Wang, Hau Chan, Bo An, Xiaoli Li, J. Senthilnath
Research Collection School Of Computing and Information Systems
In this paper, we propose a new approach to train deep learning models using game theory concepts including Generative Adversarial Networks (GANs) and Adversarial Training (AT) where we deploy a double-oracle framework using best response oracles. GAN is essentially a two-player zero-sum game between the generator and the discriminator. The same concept can be applied to AT with attacker and classifier as players. Training these models is challenging as a pure Nash equilibrium may not exist and even finding the mixed Nash equilibrium is difficult as training algorithms for both GAN and AT have a large-scale strategy space. Extending our …
An End-To-End Bi-Objective Approach To Deep Graph Partitioning, Pengcheng Wei, Yuan Fang, Zhihao Wen, Zheng Xiao, Binbin Chen
An End-To-End Bi-Objective Approach To Deep Graph Partitioning, Pengcheng Wei, Yuan Fang, Zhihao Wen, Zheng Xiao, Binbin Chen
Research Collection School Of Computing and Information Systems
Graphs are ubiquitous in real-world applications, such as computation graphs and social networks. Partitioning large graphs into smaller, balanced partitions is often essential, with the biobjective graph partitioning problem aiming to minimize both the“cut” across partitions and the imbalance in partition sizes. However, existing heuristic methods face scalability challenges or overlook partition balance, leading to suboptimal results. Recent deep learning approaches, while promising, typically focus only on node-level features and lack a truly end-to-end framework, resulting in limited performance. In this paper, we introduce a novel method based on graph neural networks (GNNs) that leverages multilevel graph features and addresses …
Lara : A Light And Anti-Overfitting Retraining Approach For Unsupervised Time Series Anomaly Detection, Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, Qingsong Wen
Lara : A Light And Anti-Overfitting Retraining Approach For Unsupervised Time Series Anomaly Detection, Feiyi Chen, Zhen Qin, Mengchu Zhou, Yingying Zhang, Shuiguang Deng, Lunting Fan, Guansong Pang, Qingsong Wen
Research Collection School Of Computing and Information Systems
Most of current anomaly detection models assume that the normal pattern remains the same all the time. However, the normal patterns of web services can change dramatically and frequently over time. The model trained on old-distribution data becomes outdated and ineffective after such changes. Retraining the whole model whenever the pattern is changed is computationally expensive. Further, at the beginning of normal pattern changes, there is not enough observation data from the new distribution. Retraining a large neural network model with limited data is vulnerable to overfitting. Thus, we propose a Light Anti-overfitting Retraining Approach (LARA) based on deep variational …
Bi-Objective Dynamic Tugboat Scheduling With Speed Optimization Under Stochastic And Time-Varying Service Demands, Xiaoyang Wei, Hoong Chuin Lau, Zhe Xiao, Xiuju Fu, Xiaocai Zhang, Zheng Qin
Bi-Objective Dynamic Tugboat Scheduling With Speed Optimization Under Stochastic And Time-Varying Service Demands, Xiaoyang Wei, Hoong Chuin Lau, Zhe Xiao, Xiuju Fu, Xiaocai Zhang, Zheng Qin
Research Collection School Of Computing and Information Systems
With the growing emphasis on green shipping to reduce the environmental impact of maritime transportation, optimizing fuel consumption with maintaining high service quality has become critical in port operations. Ports are essential nodes in global supply chains, where tugboats play a pivotal role in the safe and efficient maneuvering of ships within constrained environments. However, existing literature lacks approaches that address tugboat scheduling under realistic operational conditions. To fill the research gap, this is the first work to propose the bi-objective dynamic tugboat scheduling problem that optimizes speed under stochastic and time-varying demands, aiming to minimize fuel consumption and manage …
Impact Of Achievement-Oriented Gamification In Erp Systems: Examining Subjective And Objective User Outcomes, E. Adeborna, Fiona Fui-Hoon Nah, L. Motiwalla
Impact Of Achievement-Oriented Gamification In Erp Systems: Examining Subjective And Objective User Outcomes, E. Adeborna, Fiona Fui-Hoon Nah, L. Motiwalla
Research Collection School Of Computing and Information Systems
This research explores the effect of gamification using achievement-oriented affordances in Enterprise Resource Planning (ERP) systems on subjective (behavioral intention) and objective (performance) outcomes. Drawing on the cognitive-affectiveconative (CAC) framework, a research model was developed to explain behavioral intention and tested in a pilot experiment with 63 participants. These participants completed a post-study questionnaire for assessing the impact of gamification on users’ behavioral intention that is mediated by CAC constructs: focused immersion, enjoyment, and selfrewarding experience. Preliminary results show that gamification enhances enjoyment and self-rewarding experience, which in turn positively influence and fully mediate behavioral intention. Objective performance outcomes were …
The Effectiveness Of Local Updates For Decentralized Learning Under Data Heterogeneity, Tongle Wu, Zhize Li, Ying Sun
The Effectiveness Of Local Updates For Decentralized Learning Under Data Heterogeneity, Tongle Wu, Zhize Li, Ying Sun
Research Collection School Of Computing and Information Systems
We revisit two fundamental decentralized optimization methods, Decentralized Gradient Tracking (DGT) and Decentralized Gradient Descent (DGD), with multiple local updates. We consider two settings and demonstrate that incorporating local update steps can reduce communication complexity. Specifically, for $\mu$-strongly convex and $L$-smooth loss functions, we proved that local DGT achieves communication complexity {}{$\tilde{\mathcal{O}} \Big(\frac{L}{\mu(K+1)} + \frac{\delta + {}{\mu}}{\mu (1 - \rho)} + \frac{\rho }{(1 - \rho)^2} \cdot \frac{L+ \delta}{\mu}\Big)$}, where $K$ is the number of additional local update}, $\rho$ measures the network connectivity and $\delta$ measures the second-order heterogeneity of the local losses. Our results reveal the tradeoff between communication and …
Risk Spillover Effect Of China-Asean Supply Chains: Insights Of Industrial Transfer, Zeyang Bian, Yuning Zhang, Keng Siau, Yaqian Zhang, Jianjia He
Risk Spillover Effect Of China-Asean Supply Chains: Insights Of Industrial Transfer, Zeyang Bian, Yuning Zhang, Keng Siau, Yaqian Zhang, Jianjia He
Research Collection School Of Computing and Information Systems
As labour costs in China increase, labour-intensive industries are migrating to ASEAN countries, attracted by lower labour costs and market potential. This shift not only affects the economies of China and ASEAN but also reshapes the global manufacturing landscape. This paper investigates the correlation and spillover of supply chain risks using production exposure indicators derived from inter-country input-output data and the R-Vine Copula model. We assess the risk spillover of each country within the global supply chain. Our findings indicate that industrial relocation can significantly alter supply chain structures, thereby affecting the concentration and direction of risks. While China's role …
An Agent-Based Computational Finance Simulation Model To Study Market Efficiency, Wei Feng, Keng Siau, Wee-Yeap Lau, Lim-Thye Goh, Haonan Chen
An Agent-Based Computational Finance Simulation Model To Study Market Efficiency, Wei Feng, Keng Siau, Wee-Yeap Lau, Lim-Thye Goh, Haonan Chen
Research Collection School Of Computing and Information Systems
The advancement of computational modeling, data systems, and digital infrastructure has enabled the rise of agent-based computational finance (ACF). This study models interactions among heterogeneous investors. By embedding behavioral logics such as environmental, social, and governance (ESG) preferences and volatility thresholds, the model captures microstructural dynamics under different trading rules. Using ACF, the authors compare transaction plus 0 day (T+0) to transaction plus 1 day (T+1). Results show that T+0 improves price discovery, deepens liquidity, and reduces transaction costs. From a computational perspective, this research contributes to ACF by showing how policy logic and investor heterogeneity can be encoded and …
More Effective Javascript Breaking Change Detection Via Dynamic Object Relation Graph, Dezhen Kong, Jiakun Liu, Chao Ni, David Lo, Lingfeng Bao
More Effective Javascript Breaking Change Detection Via Dynamic Object Relation Graph, Dezhen Kong, Jiakun Liu, Chao Ni, David Lo, Lingfeng Bao
Research Collection School Of Computing and Information Systems
JavaScript libraries are characterized by their widespread use, frequent code changes, and a high tolerance for backward incompatible changes. Awareness of such breaking changes can help developers adapt to version updates and avoid negative impacts. Several tools have been targeted to or can be used to detect breaking change detection in the JavaScript community. However, these tools detect breaking changes using different ways, and there are currently no systematic reviews of these approaches. From a preliminary study on popular JavaScript libraries, we find that existing approaches, including simple regression testing, model-based testing and type differencing cannot detect many breaking changes …
Don’T Complete It! Preventing Unhelpful Code Completion For Productive And Sustainable Neural Code Completion Systems, Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li, David Lo
Don’T Complete It! Preventing Unhelpful Code Completion For Productive And Sustainable Neural Code Completion Systems, Zhensu Sun, Xiaoning Du, Fu Song, Shangwen Wang, Mingze Ni, Li Li, David Lo
Research Collection School Of Computing and Information Systems
Currently, large pre-trained language models are widely applied in neural code completion systems. Though large code models significantly outperform their smaller counterparts, around 70% of displayed code completions from Github Copilot are not accepted by developers. Being reviewed but not accepted, their help to developer productivity is considerably limited and may conversely aggravate the workload of developers, as the code completions are automatically and actively generated in state-of-the-art code completion systems as developers type out once the service is enabled. Even worse, considering the high cost of the large code models, it is a huge waste of computing resources and …
Privacy-Preserving User Recruitment With Sensing Quality Evaluation In Mobile Crowdsensing, Jieying An, Yanbing Ren, Xinghua Li, Man Zhang, Bin Luo, Yinbin Miao, Ximeng Liu, Deng, Robert H.
Privacy-Preserving User Recruitment With Sensing Quality Evaluation In Mobile Crowdsensing, Jieying An, Yanbing Ren, Xinghua Li, Man Zhang, Bin Luo, Yinbin Miao, Ximeng Liu, Deng, Robert H.
Research Collection School Of Computing and Information Systems
Recruiting users in mobile crowdsensing (MCS) can make the platform obtain high-quality data to provide better services. Although the privacy leakage during the process of user recruitment has received a lot of research attention, none of the existing work considers the evaluation of the sensing quality of privacy-preserving data submitted by users, which makes the platform incapable of recruiting users suitably to obtain high-quality sensing data, thereby reducing the reliability of MCS services. To solve this problem, we first propose a sensing quality evaluation method based on the deviation and variance of sensing data. According to it, the platform can …
Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo
Gotcha ! This Model Uses My Code ! Evaluating Membership Leakage Risks In Code Models, Zhou Yang, Zhipeng Zhao, Chenyu Wang, Jieke Shi, Dongsum Kim, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
Leveraging large-scale datasets from open-source projects and advances in large language models, recent progress has led to sophisticated code models for key software engineering tasks, such as program repair and code completion. These models are trained on data from various sources, including public open-source projects like GitHub and private, confidential code from companies, raising significant privacy concerns. This paper investigates a crucial but unexplored question: What is the risk of membership information leakage in code models? Membership leakage refers to the vulnerability where an attacker can infer whether a specific data point was part of the training dataset. We present …
Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra
Delidar: Decoupling Lidars For Pervasive Spatial Computing, Kanatta Gamage Ramesh Darshana Rathnayake, Razat Sutradhar, Abbaas A. M. Nishar, Weerakoon Dulaj S., Ashwin Ashok, Archan Misra
Research Collection School Of Computing and Information Systems
Unbounded proliferation of LiDAR-equipped pervasive devices generates two challenges: (a) mutual interference among emitters and (b) significantly higher sensing energy overhead. We propose a fundamentally different approach for LiDAR sensing, in indoor spaces, that decouples the sensor’s emitter and receiver components. Our proposed approach, called DeLiDAR, centralizes the emitter functionality in one or more stationary nodes that continually emit pulses; this decoupling allows each mobile LiDAR sensor to be an ultra-low power, pure receiver unit consisting solely of passive multiple photodiodes. We explain how the emitter can utilize VLC-based encoding of its pulses to convey parameter settings that allow a …
Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks, Shengmin Xu, Jianfei Sun, Hangcheng Cao, Yulan Gao, Ziyang He, Cong Wu
Shield-U: Safeguarding Traffic Sign Recognition Against Perturbation Attacks, Shengmin Xu, Jianfei Sun, Hangcheng Cao, Yulan Gao, Ziyang He, Cong Wu
Research Collection School Of Computing and Information Systems
Traffic sign recognition systems are crucial for the navigation and situation awareness of autonomous vehicles. They leverage deep learning technologies to swiftly and accurately identify traffic signs, even in the most challenging traffic environments. However, security researchers have uncovered a critical vulnerability in these systems: learning-based TSRs are particularly susceptible to physical-world perturbation attacks. Through subtle modifications (i.e., attaching well-designed patches on traffic signs), attackers can deceive the recognition system into making erroneous judgments, which can further lead to serious traffic accidents. Although several defense mechanisms have been proposed to enhance the security of sign recognition systems, these solutions generally …
A Comprehensive Study On Static Application Security Testing (Sast) Tools For Android, Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan, Junjie Wang, Xiaofei Xie
A Comprehensive Study On Static Application Security Testing (Sast) Tools For Android, Jingyun Zhu, Kaixuan Li, Sen Chen, Lingling Fan, Junjie Wang, Xiaofei Xie
Research Collection School Of Computing and Information Systems
To identify security vulnerabilities in Android applications, numerous static application security testing (SAST) tools have been proposed. However, it poses significant challenges to assess their overall performance on diverse vulnerability types. The task is non-trivial and poses considerable challenges. Firstly, the absence of a unified evaluation platform for defining and describing tools’ supported vulnerability types, coupled with the lack of normalization for the intricate and varied reports generated by different tools, significantly adds to the complexity. Secondly, there is a scarcity of adequate benchmarks, particularly those derived from real-world scenarios. To address these problems, we are the first to propose …