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Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples, Andy Wee An Ta, Han Leong Goh, Christine Ang, Lian Yeow Koh, Ken Poon, Steven M. Miller
Two Singapore Public Healthcare Ai Applications For National Screening Programs And Other Examples, Andy Wee An Ta, Han Leong Goh, Christine Ang, Lian Yeow Koh, Ken Poon, Steven M. Miller
Research Collection School Of Computing and Information Systems
This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation-wide screening programs. The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases, targeting the rapidly increasing number of adults in the country with diabetes. In the second example, the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the …
Pros: An Efficient Pattern-Driven Compressive Sensing Framework For Low-Power Biopotential-Based Wearable With On-Chip Intelligence, Nhat Pham, Hong Jia, Minh Tran, Tuan Dinh, Nam Bui, Young Kwon, Dong Ma, Phuc Nguyen, Cecilia Mascolo, Tam Vu
Pros: An Efficient Pattern-Driven Compressive Sensing Framework For Low-Power Biopotential-Based Wearable With On-Chip Intelligence, Nhat Pham, Hong Jia, Minh Tran, Tuan Dinh, Nam Bui, Young Kwon, Dong Ma, Phuc Nguyen, Cecilia Mascolo, Tam Vu
Research Collection School Of Computing and Information Systems
While the global healthcare market of wearable devices has been growing signi!cantly in recent years and is predicted to reach $60 billion by 2028, many important healthcare applications such as seizure monitoring, drowsiness detection, etc. have not been deployed due to the limited battery lifetime, slow response rate, and inadequate biosignal quality. This study proposes PROS, an e"cient pattern-driven compressive sensing framework for low-power biopotential-based wearables. PROS eliminates the conventional trade-o# between signal quality, response time, and power consumption by introducing tiny pattern recognition primitives and a pattern-driven compressive sensing technique that exploits the sparsity of biosignals. Specifically, we (i) …
Learning Discriminative Representations Via Variational Self-Distillation For Cross-View Geo-Localization, Qian Hu, Wansi Li, Xing Xu, Ning Liu, Lei Wang
Learning Discriminative Representations Via Variational Self-Distillation For Cross-View Geo-Localization, Qian Hu, Wansi Li, Xing Xu, Ning Liu, Lei Wang
Research Collection School Of Computing and Information Systems
Cross-view geo-localization is to localize the same geographic target in images from different perspectives, e.g., satellite-view and drone-view. The primary challenge faced by existing methods is the large visual appearance changes across views. Most previous work utilizes the deep neural network to obtain the discriminative representations and directly uses them to accomplish the geo-localization task. However, these approaches ignore that the redundancy retained in the extracted features negatively impacts the result. In this paper, we argue that the information bottleneck (IB) can retain the most relevant information while removing as much redundancy as possible. The variational self-distillation (VSD) strategy provides …
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Research Collection School Of Computing and Information Systems
Electricity demands are increasing significantly and the traditional power grid system is facing huge challenges. As the desired next-generation power grid system, smart grid can provide secure and reliable power generation, and consumption, and can also realize the system’s coordinated and intelligent power distribution. Coordinating grid power distribution usually requires mutual communication between power distributors to accomplish coordination. However, the power network is complex, the network nodes are far apart, and the communication bandwidth is often expensive. Therefore, how to reduce the communication bandwidth in the cooperative power distribution process task is crucially important. One way to tackle this problem …
Unsupervised Video Hashing With Multi-Granularity Contextualization And Multi-Structure Preservation, Yanbin Hao, Jingru Duan, Hao Zhang, Bin Zhu, Pengyuan Zhou, Xiangnan He
Unsupervised Video Hashing With Multi-Granularity Contextualization And Multi-Structure Preservation, Yanbin Hao, Jingru Duan, Hao Zhang, Bin Zhu, Pengyuan Zhou, Xiangnan He
Research Collection School Of Computing and Information Systems
Unsupervised video hashing typically aims to learn a compact binary vector to represent complex video content without using manual annotations. Existing unsupervised hashing methods generally suffer from incomplete exploration of various perspective dependencies (e.g., long-range and short-range) and data structures that exist in visual contents, resulting in less discriminative hash codes. In this paper, we propose aMulti-granularity Contextualized and Multi-Structure preserved Hashing (MCMSH) method, exploring multiple axial contexts for discriminative video representation generation and various structural information for unsupervised learning simultaneously. Specifically, we delicately design three self-gating modules to separately model three granularities of dependencies (i.e., long/middle/short-range dependencies) and densely …
Editing Out-Of-Domain Gan Inversion Via Differential Activations, Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He
Editing Out-Of-Domain Gan Inversion Via Differential Activations, Haorui Song, Yong Du, Tianyi Xiang, Junyu Dong, Jing Qin, Shengfeng He
Research Collection School Of Computing and Information Systems
Despite the demonstrated editing capacity in the latent space of a pretrained GAN model, inverting real-world images is stuck in a dilemma that the reconstruction cannot be faithful to the original input. The main reason for this is that the distributions between training and real-world data are misaligned, and because of that, it is unstable of GAN inversion for real image editing. In this paper, we propose a novel GAN prior based editing framework to tackle the out-of-domain inversion problem with a composition-decomposition paradigm. In particular, during the phase of composition, we introduce a differential activation module for detecting semantic …
Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang
Automatic Pull Request Title Generation, Ting Zhang, Ivana Clairine Irsan, Ferdian Thung, Donggyun Han, David Lo, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging into another branch in a repository. A PR needs to be reviewed and approved by the core team of the repository before the changes are merged into the branch. Usually, reviewers need to identify a PR that is in line with their interests before providing a review. By default, PRs are arranged in a list view that shows the titles of PRs. Therefore, it is desirable to have a precise and concise …
Towards Robust Models Of Code Via Energy-Based Learning On Auxiliary Datasets, Duy Quoc Nghi Bui, Yijun Yu
Towards Robust Models Of Code Via Energy-Based Learning On Auxiliary Datasets, Duy Quoc Nghi Bui, Yijun Yu
Research Collection School Of Computing and Information Systems
Existing approaches to improving the robustness of source code models concentrate on recognizing adversarial samples rather than valid samples that fall outside of a given distribution, which we refer to as out-of-distribution (OOD) samples. To this end, we propose to use an auxiliary dataset (out-of-distribution) such that, when trained together with the main dataset, they will enhance the model’s robustness. We adapt energy-bounded learning objective function to assign a higher score to in-distribution samples and a lower score to out-of-distribution samples in order to incorporate such out-of-distribution samples into the training process of source code models. In terms of OOD …
Codebert For Code Clone Detection: A Replication Study, Saad Arshad, Shamsa Abid, Shafay Shamail
Codebert For Code Clone Detection: A Replication Study, Saad Arshad, Shamsa Abid, Shafay Shamail
Research Collection School Of Computing and Information Systems
Large pre-trained models have dramatically improved the state-of-the-art on a variety of natural language processing (NLP) tasks. CodeBERT is one such pre-trained model for natural language (NL) and programming language (PL) which captures the semantics in natural language and programming language, and produces general-purpose representations. While it has been shown to support natural language code search and code documentation generation tasks, its effectiveness for code clone detection is not explored in depth. In this paper, we aim to replicate and evaluate the performance of CodeBERT for code clone detection on multiple datasets with varying functionalities to understand (1) whether CodeBERT …
A Comparative Analysis Of Clone Detection Techniques On Semanticclonebench, Sohaib Masood Rabbani, Nabeel Ahmad Gulzar, Saad Arshad, Shamsa Abid, Shafay Shamail
A Comparative Analysis Of Clone Detection Techniques On Semanticclonebench, Sohaib Masood Rabbani, Nabeel Ahmad Gulzar, Saad Arshad, Shamsa Abid, Shafay Shamail
Research Collection School Of Computing and Information Systems
Semantic code clone detection involves the detection of functionally similar code fragments which may otherwise be lexically, syntactically, or structurally dissimilar. The detection of semantic code clones has important applications in aspect mining and product line analysis. The accurate detection of semantic code clones is a challenging task and various techniques have been proposed. However, the evaluation of these techniques is performed using various datasets and we do not have a clear picture of the performance of these techniques relative to each other. Recently, SemanticCloneBench has been introduced as a benchmark for semantic clones. Now, we can use the SemanticCloneBench …
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Pruning The Communication Bandwidth Between Reinforcement Learning Agents Through Causal Inference: An Innovative Approach To Designing A Smart Grid Power System, Xianjie Zhang, Yu Liu, Wenjun Li, Chen Gong
Research Collection School Of Computing and Information Systems
Electricity demands are increasing significantly and the traditional power grid system isfacing huge challenges. As the desired next-generation power grid system, smart grid can providesecure and reliable power generation, and consumption, and can also realize the system’s coordinatedand intelligent power distribution. Coordinating grid power distribution usually requiresmutual communication between power distributors to accomplish coordination. However, the powernetwork is complex, the network nodes are far apart, and the communication bandwidth is oftenexpensive. Therefore, how to reduce the communication bandwidth in the cooperative power distributionprocess task is crucially important. One way to tackle this problem is to build mechanismsto selectively send out …
Dualformer: Local-Global Stratified Transformer For Efficient Video Recognition, Yuxuan Liang, Pan Zhou, Roger Zimmermann, Shuicheng Yan
Dualformer: Local-Global Stratified Transformer For Efficient Video Recognition, Yuxuan Liang, Pan Zhou, Roger Zimmermann, Shuicheng Yan
Research Collection School Of Computing and Information Systems
While transformers have shown great potential on video recognition with their strong capability of capturing long-range dependencies, they often suffer high computational costs induced by the self-attention to the huge number of 3D tokens. In this paper, we present a new transformer architecture termed DualFormer, which can efficiently perform space-time attention for video recognition. Concretely, DualFormer stratifies the full space-time attention into dual cascaded levels, i.e., to first learn fine-grained local interactions among nearby 3D tokens, and then to capture coarse-grained global dependencies between the query token and global pyramid contexts. Different from existing methods that apply space-time factorization or …
Self-Promoted Supervision For Few-Shot Transformer, Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo
Self-Promoted Supervision For Few-Shot Transformer, Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo
Research Collection School Of Computing and Information Systems
The few-shot learning ability of vision transformers (ViTs) is rarely investigated though heavily desired. In this work, we empirically find that with the same few-shot learning frameworks, e.g. MetaBaseline, replacing the widely used CNN feature extractor with a ViT model often severely impairs few-shot classification performance. Moreover, our empirical study shows that in the absence of inductive bias, ViTs often learn the low-qualified token dependencies under few-shot learning regime where only a few labeled training data are available, which largely contributes to the above performance degradation. To alleviate this issue, for the first time, we propose a simple yet effective …
Video Graph Transformer For Video Question Answering, Junbin Xiao, Pan Zhou, Tat-Seng Chua, Shuicheng Yan
Video Graph Transformer For Video Question Answering, Junbin Xiao, Pan Zhou, Tat-Seng Chua, Shuicheng Yan
Research Collection School Of Computing and Information Systems
This paper proposes a Video Graph Transformer (VGT) model for Video Quetion Answering (VideoQA). VGT’s uniqueness are two-fold: 1) it designs a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations, and dynamics for complex spatio-temporal reasoning; and 2) it exploits disentangled video and text Transformers for relevance comparison between the video and text to perform QA, instead of entangled crossmodal Transformer for answer classification. Vision-text communication is done by additional cross-modal interaction modules. With more reasonable video encoding and QA solution, we show that VGT can achieve much better performances on VideoQA tasks …
Mix-Dann And Dynamic-Modal-Distillation For Video Domain Adaptation, Yuehao Yin, Bin Zhu, Jingjing Chen, Lechao Cheng, Yu-Gang Jiang
Mix-Dann And Dynamic-Modal-Distillation For Video Domain Adaptation, Yuehao Yin, Bin Zhu, Jingjing Chen, Lechao Cheng, Yu-Gang Jiang
Research Collection School Of Computing and Information Systems
Video domain adaptation is non-trivial due to video is inherently involved with multi-dimensional and multi-modal information. Existing works mainly adopt adversarial learning and self-supervised tasks to align features. Nevertheless, the explicit interaction between source and target in the temporal dimension, as well as the adaptation between modalities, are unexploited. In this paper, we propose Mix-Domain-Adversarial Neural Network and Dynamic-Modal-Distillation (MD-DMD), a novel multi-modal adversarial learning framework for unsupervised video domain adaptation. Our approach incorporates the temporal information between source and target domains, as well as the diversity of adaptability between modalities. On the one hand, for every single modality, we …
Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay
Physical Adversarial Attack On A Robotic Arm, Yifan Jia, Christopher M. Poskitt, Jun Sun, Sudipta Chattopadhyay
Research Collection School Of Computing and Information Systems
Collaborative Robots (cobots) are regarded as highly safety-critical cyber-physical systems (CPSs) owing to their close physical interactions with humans. In settings such as smart factories, they are frequently augmented with AI. For example, in order to move materials, cobots utilize object detectors based on deep learning models. Deep learning, however, has been demonstrated as vulnerable to adversarial attacks: a minor change (noise) to benign input can fool the underlying neural networks and lead to a different result. While existing works have explored such attacks in the context of picture/object classification, less attention has been given to attacking neural networks used …
Guaranteeing Timed Opacity Using Parametric Timed Model Checking, Étienne André, Didier Lime, Dylan Marinho, Jun Sun
Guaranteeing Timed Opacity Using Parametric Timed Model Checking, Étienne André, Didier Lime, Dylan Marinho, Jun Sun
Research Collection School Of Computing and Information Systems
Information leakage can have dramatic consequences on systems security. Among harmful information leaks, the timing information leakage occurs whenever an attacker successfully deduces confidential internal information. In this work, we consider that the attacker has access (only) to the system execution time. We address the following timed opacity problem: given a timed system, a private location and a final location, synthesize the execution times from the initial location to the final location for which one cannot deduce whether the system went through the private location. We also consider the full timed opacity problem, asking whether the system is opaque for …
What Machines Can't Do (Yet) In Real Work Settings, Thomas H. Davenport, Steven M. Miller
What Machines Can't Do (Yet) In Real Work Settings, Thomas H. Davenport, Steven M. Miller
Research Collection School Of Computing and Information Systems
AI systems may perform well in the research lab or under highly controlled application settings, but they still needed human help in the types of real world work settings we researched for a new book, Working With AI: Real Stories of Human-Machine Collaboration. Human workers were very much in evidence across our 30 case studies. In this article, we use those examples to illustrate our list of AI-enabled activities that still require human assistance. These are activities where organizations need to continue to invest in human capital, and where practitioners can expect job continuity for the immediate future
Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing, Liang Li, Baihua Zheng, Weiwei Sun
Adaptive Structural Similarity Preserving For Unsupervised Cross Modal Hashing, Liang Li, Baihua Zheng, Weiwei Sun
Research Collection School Of Computing and Information Systems
Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well …
Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang
Ergo: Event Relational Graph Transformer For Document-Level Event Causality Identification, Meiqi Chen, Yixin Cao, Kunquan Deng, Mukai Li, Kun Wang, Jing Shao, Yan Zhang
Research Collection School Of Computing and Information Systems
Document-level Event Causality Identification (DECI) aims to identify event-event causal relations in a document. Existing works usually build an event graph for global reasoning across multiple sentences. However, the edges between events have to be carefully designed through heuristic rules or external tools. In this paper, we propose a novel Event Relational Graph TransfOrmer (ERGO) framework1 for DECI, to ease the graph construction and improve it over the noisy edge issue. Different from conventional event graphs, we define a pair of events as a node and build a complete event relational graph without any prior knowledge or tools. This naturally …
Field Experiments In Operations Management, Yang Gao, Meng Li, Shujing Sun
Field Experiments In Operations Management, Yang Gao, Meng Li, Shujing Sun
Research Collection School Of Computing and Information Systems
While the field experiment is a powerful and well-established method to investigate causal relationships, operations management (OM) has embraced this methodology only in recent years. This paper provides a comprehensive review of the existing OM literature leveraging field experiments and serves as a one-stop guide for future application of field experiments in the OM area. We start by recapping the characteristics that distinguish field experiments from other common types of experiments and organizing the relevant OM studies by topic. Corresponding to the commonly overlooked issues in field experiment-based OM studies, we then provide a detailed roadmap, ranging from experimental design …
Interactive Video Corpus Moment Retrieval Using Reinforcement Learning, Zhixin Ma, Chong-Wah Ngo
Interactive Video Corpus Moment Retrieval Using Reinforcement Learning, Zhixin Ma, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Known-item video search is effective with human-in-the-loop to interactively investigate the search result and refine the initial query. Nevertheless, when the first few pages of results are swamped with visually similar items, or the search target is hidden deep in the ranked list, finding the know-item target usually requires a long duration of browsing and result inspection. This paper tackles the problem by reinforcement learning, aiming to reach a search target within a few rounds of interaction by long-term learning from user feedbacks. Specifically, the system interactively plans for navigation path based on feedback and recommends a potential target that …
Reflecting On Experiences For Response Generation, Chenchen Ye, Lizi Liao, Suyu Liu, Tat-Seng Chua
Reflecting On Experiences For Response Generation, Chenchen Ye, Lizi Liao, Suyu Liu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Multimodal dialogue systems attract much attention recently, but they are far from skills like: 1) automatically generate context- specific responses instead of safe but general responses; 2) naturally coordinate between the different information modalities (e.g. text and image) in responses; 3) intuitively explain the reasons for generated responses and improve a specific response without re-training the whole model. To approach these goals, we propose a different angle for the task - Reflecting Experiences for Response Generation (RERG). This is supported by the fact that generating a response from scratch can be hard, but much easier if we can access other …
Ngram-Oaxe: Phrase-Based Order-Agnostic Cross Entropy For Non-Autoregressive Machine Translation, Cunxiao Du, Zhaopeng Tu, Longyue Wang, Jing Jiang
Ngram-Oaxe: Phrase-Based Order-Agnostic Cross Entropy For Non-Autoregressive Machine Translation, Cunxiao Du, Zhaopeng Tu, Longyue Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Recently, a new training oaxe loss has proven effective to ameliorate the effect of multimodality for non-autoregressive translation (NAT), which removes the penalty of word order errors in the standard cross-entropy loss. Starting from the intuition that reordering generally occurs between phrases, we extend oaxe by only allowing reordering between ngram phrases and still requiring a strict match of word order within the phrases. Extensive experiments on NAT benchmarks across language pairs and data scales demonstrate the effectiveness and universality of our approach. Further analyses show that ngram noaxe indeed improves the translation of ngram phrases, and produces more fluent …
Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra
Softskip: Empowering Multi-Modal Dynamic Pruning For Single-Stage Referring Comprehension, Dulanga Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra
Research Collection School Of Computing and Information Systems
Supporting real-time referring expression comprehension (REC) on pervasive devices is an important capability for human-AI collaborative tasks. Model pruning techniques, applied to DNN models, can enable real-time execution even on resource-constrained devices. However, existing pruning strategies are designed principally for uni-modal applications, and suffer a significant loss of accuracy when applied to REC tasks that require fusion of textual and visual inputs. We thus present a multi-modal pruning model, LGMDP, which uses language as a pivot to dynamically and judiciously select the relevant computational blocks that need to be executed. LGMDP also introduces a new SoftSkip mechanism, whereby 'skipped' visual …
Accurate Generation Of Trigger-Action Programs With Domain-Adapted Sequence-To-Sequence Learning, Imam Nur Bani Yusuf, Lingxiao Jiang, David Lo
Accurate Generation Of Trigger-Action Programs With Domain-Adapted Sequence-To-Sequence Learning, Imam Nur Bani Yusuf, Lingxiao Jiang, David Lo
Research Collection School Of Computing and Information Systems
Trigger-action programming allows end users to write event-driven rules to automate smart devices and internet services. Users can create a trigger-action program (TAP) by specifying triggers and actions from a set of predefined functions along with suitable data fields for the functions. Many trigger-action programming platforms have emerged as the popularity grows, e.g., IFTTT, Microsoft Power Automate, and Samsung SmartThings. Despite their simplicity, composing trigger-action programs (TAPs) can still be challenging for end users due to the domain knowledge needed and enormous search space of many combinations of triggers and actions. We propose RecipeGen, a new deep learning-based approach that …
Compressing Pre-Trained Models Of Code Into 3 Mb, Jieke Shi, Zhou Yang, Bowen Xu, Hong Jin Kang, David Lo
Compressing Pre-Trained Models Of Code Into 3 Mb, Jieke Shi, Zhou Yang, Bowen Xu, Hong Jin Kang, David Lo
Research Collection School Of Computing and Information Systems
Although large pre-trained models of code have delivered significant advancements in various code processing tasks, there is an impediment to the wide and fluent adoption of these powerful models in software developers’ daily workflow: these large models consume hundreds of megabytes of memory and run slowly on personal devices, which causes problems in model deployment and greatly degrades the user experience. It motivates us to propose Compressor, a novel approach that can compress the pre-trained models of code into extremely small models with negligible performance sacrifice. Our proposed method formulates the design of tiny models as simplifying the pre-trained model …
Generic Conversions From Cpa To Cca Without Ciphertext Expansion For Threshold Abe With Constant-Size Ciphertexts, J Lai, F Guo, W Susilo, P Jiang, Guomin Yang, X. Huang
Generic Conversions From Cpa To Cca Without Ciphertext Expansion For Threshold Abe With Constant-Size Ciphertexts, J Lai, F Guo, W Susilo, P Jiang, Guomin Yang, X. Huang
Research Collection School Of Computing and Information Systems
CCA security is desirable when designing encryption schemes because it captures active attackers. One efficient approach for achieving CCA security is to use generic conversions. We first design a CPA secure scheme, and then transform it into a CCA secure scheme using the Fujisaki-Okamoto technique (CRYPTO ’99) in the random oracle model or the Canetti-Halevi-Katz technique (EUROCRYPT ’04) in the standard model if the CPA secure scheme satisfies some conditions. These transformations are generic so they can be applied to encryption primitives such as IBE and ABE. Nevertheless, both techniques result in an inevitable ciphertext expansion. Subsequent generic constructions mainly …
Lawbreaker: An Approach For Specifying Traffic Laws And Fuzzing Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Jun Sun, Yuqi Chen, Zijiang Yang
Lawbreaker: An Approach For Specifying Traffic Laws And Fuzzing Autonomous Vehicles, Yang Sun, Christopher M. Poskitt, Jun Sun, Yuqi Chen, Zijiang Yang
Research Collection School Of Computing and Information Systems
Autonomous driving systems (ADSs) must be tested thoroughly before they can be deployed in autonomous vehicles. High-fidelity simulators allow them to be tested against diverse scenarios, including those that are difficult to recreate in real-world testing grounds. While previous approaches have shown that test cases can be generated automatically, they tend to focus on weak oracles (e.g. reaching the destination without collisions) without assessing whether the journey itself was undertaken safely and satisfied the law. In this work, we propose LawBreaker, an automated framework for testing ADSs against real-world traffic laws, which is designed to be compatible with different scenario …
Vpsl: Verifiable Privacy-Preserving Data Search For Cloud-Assisted Internet Of Things, Qiuyun Tong, Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng
Vpsl: Verifiable Privacy-Preserving Data Search For Cloud-Assisted Internet Of Things, Qiuyun Tong, Yinbin Miao, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
Cloud-assisted Internet of Things (IoT) is increasingly prevalent used in various fields, such as the healthcare system. While in such a scenario, sensitive data (e.g., personal electronic medical records) can be easily revealed, which incurs potential security challenges. Thus, Symmetric Searchable Encryption (SSE) has been extensively studied due to its capability of supporting efficient search on encrypted data. However, most SSE schemes require the data owner to share the complete key with query users and take malicious cloud servers out of consideration. Seeking to address these limitations, in this paper we propose a Verifiable Privacy-preserving data Search scheme with Limited …