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Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao May 2023

Neural Episodic Control With State Abstraction, Zhuo Li, Derui Zhu, Yujing Hu, Xiaofei Xie, Lei Ma, Yan Zheng, Yan Song, Yingfeng Chen, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency.Generally, episodic control-based approaches are solutions that leveragehighly-rewarded past experiences to improve sample efficiency of DRL algorithms.However, previous episodic control-based approaches fail to utilize the latentinformation from the historical behaviors (e.g., state transitions, topological similarities,etc.) and lack scalability during DRL training. This work introducesNeural Episodic Control with State Abstraction (NECSA), a simple but effectivestate abstraction-based episodic control containing a more comprehensive episodicmemory, a novel state evaluation, and a multi-step state analysis. We evaluate ourapproach to the MuJoCo and Atari tasks in OpenAI gym domains. The experimentalresults indicate that NECSA achieves higher …

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Multi-Lingual Multi-Partite Product Title Matching, Huan Lin Tay, Wei Jie Tay, Hady Wirawan Lauw May 2023

Multi-Lingual Multi-Partite Product Title Matching, Huan Lin Tay, Wei Jie Tay, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

In a globalized marketplace, one could access products or services from almost anywhere. However, resolving which product in one language corresponds to another product in a different language remains an under-explored problem. We explore this from two perspectives. First, given two products of different languages, how to assess their similarity that could signal a potential match. Second, given products from various languages, how to arrive at a multi-partite clustering that respects cardinality constraints efficiently. We describe algorithms for each perspective and integrate them into a promising solution validated on real-world datasets.

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Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang May 2023

Mando-Hgt: Heterogeneous Graph Transformers For Smart Contract Vulnerability Detection, Huu Hoang Nguyen, Nhat Minh Nguyen, Chunyao Xie, Zahra Ahmadi, Daniel Kudendo, Thanh-Nam Doan, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Smart contracts in blockchains have been increasingly used for high-value business applications. It is essential to check smart contracts' reliability before and after deployment. Although various program analysis and deep learning techniques have been proposed to detect vulnerabilities in either Ethereum smart contract source code or bytecode, their detection accuracy and scalability are still limited. This paper presents a novel framework named MANDO-HGT for detecting smart contract vulnerabilities. Given Ethereum smart contracts, either in source code or bytecode form, and vulnerable or clean, MANDO-HGT custom-builds heterogeneous contract graphs (HCGs) to represent control-flow and/or function-call information of the code. It then …

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Resale Hdb Price Prediction Considering Covid-19 Through Sentiment Analysis, Srinaath Anbu Durai, Zhaoxia Wang May 2023

Resale Hdb Price Prediction Considering Covid-19 Through Sentiment Analysis, Srinaath Anbu Durai, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or …

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Generative Stresnet For Crime Prediction, Ba Phong Tran, Hoong Chuin Lau May 2023

Generative Stresnet For Crime Prediction, Ba Phong Tran, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

In this work, we combine STResnet (Zhang et al., 2017) with VAE Kingma & Welling (2013) to generate crime distribution. The outputs can be used for downstream tasks such as patrol deployment planning Chase et al. (2021).

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Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports, Yunbo Lyu, Thanh Le Cong, Hong Jin Kang, Ratnadira Widyasari, Zhipeng Zhao, Xuan-Bach Dinh Le, Ming Li, David Lo May 2023

Chronos: Time-Aware Zero-Shot Identification Of Libraries From Vulnerability Reports, Yunbo Lyu, Thanh Le Cong, Hong Jin Kang, Ratnadira Widyasari, Zhipeng Zhao, Xuan-Bach Dinh Le, Ming Li, David Lo

Research Collection School Of Computing and Information Systems

Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases record the libraries related to each vulnerability. However, the vulnerability reports may not explicitly list every library and human analysis is required to determine all the relevant libraries. Human analysis may be slow and expensive, which motivates the need for automated approaches. Researchers and practitioners have proposed to automatically identify libraries from vulnerability reports using extreme multi-label learning (XML). While state-of-the-art XML techniques showed promising performance, their experimental settings do not practically fit what happens in reality. Previous studies …

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Colefunda: Explainable Silent Vulnerability Fix Identification, Jiayuan Zhou, Michael Pacheco, Jinfu Chen, Xing Hu, Xin Xia, David Lo, Ahmed E. Hassan May 2023

Colefunda: Explainable Silent Vulnerability Fix Identification, Jiayuan Zhou, Michael Pacheco, Jinfu Chen, Xing Hu, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

It is common practice for OSS users to leverage and monitor security advisories to discover newly disclosed OSS vulnerabilities and their corresponding patches for vulnerability remediation. It is common for vulnerability fixes to be publicly available one week earlier than their disclosure. This gap in time provides an opportunity for attackers to exploit the vulnerability. Hence, OSS users need to sense the fix as early as possible so that the vulnerability can be remediated before it is exploited. However, it is common for OSS to adopt a vulnerability disclosure policy which causes the majority of vulnerabilities to be fixed silently, …

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Generation-Based Code Review Automation: How Far Are We?, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, Junda He, David Lo May 2023

Generation-Based Code Review Automation: How Far Are We?, Xin Zhou, Kisub Kim, Bowen Xu, Donggyun Han, Junda He, David Lo

Research Collection School Of Computing and Information Systems

Code review is an effective software quality assurance activity; however, it is labor-intensive and time-consuming. Thus, a number of generation-based automatic code review (ACR) approaches have been proposed recently, which leverage deep learning techniques to automate various activities in the code review process (e.g., code revision generation and review comment generation).We find the previous works carry three main limitations. First, the ACR approaches have been shown to be beneficial in each work, but those methods are not comprehensively compared with each other to show their superiority over their peer ACR approaches. Second, general-purpose pre-trained models such as CodeT5 are proven …

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What Do Users Ask In Open-Source Ai Repositories? An Empirical Study Of Github Issues, Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang, Pavneet Singh Kochhar, Qinghua Lu, Zhenchang Xing, David Lo May 2023

What Do Users Ask In Open-Source Ai Repositories? An Empirical Study Of Github Issues, Zhou Yang, Chenyu Wang, Jieke Shi, Thong Hoang, Pavneet Singh Kochhar, Qinghua Lu, Zhenchang Xing, David Lo

Research Collection School Of Computing and Information Systems

Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets and increasing computational power, have become effective solutions to various critical tasks, such as natural language understanding, speech recognition, and image processing. The advancement of these AI systems is inseparable from open-source software (OSS). Specifically, many benchmarks, implementations, and frameworks for constructing AI systems are made open source and accessible to the public, allowing researchers and practitioners to reproduce the reported results and broaden the application of AI systems. The development of AI systems follows a data-driven paradigm and is sensitive to hyperparameter settings and data separation. Developers …

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Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy May 2023

Exploring A Gradient-Based Explainable Ai Technique For Time-Series Data: A Case Study Of Assessing Stroke Rehabilitation Exercises, Min Hun Lee, Yi Jing Choy

Research Collection School Of Computing and Information Systems

Explainable artificial intelligence (AI) techniques are increasingly being explored to provide insights into why AI and machine learning (ML) models provide a certain outcome in various applications. However, there has been limited exploration of explainable AI techniques on time-series data, especially in the healthcare context. In this paper, we describe a threshold-based method that utilizes a weakly supervised model and a gradient-based explainable AI technique (i.e. saliency map) and explore its feasibility to identify salient frames of time-series data. Using the dataset from 15 post-stroke survivors performing three upper-limb exercises and labels on whether a compensatory motion is observed or …

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Diffseer: Difference-Based Dynamic Weighted Graph Visualization, Xiaolin Wen, Yong Wang, Meixuan Wu, Fengjie Wang, Xuanwu Yue, Qiaomu Shen, Yuxin Ma, Min Zhu May 2023

Diffseer: Difference-Based Dynamic Weighted Graph Visualization, Xiaolin Wen, Yong Wang, Meixuan Wu, Fengjie Wang, Xuanwu Yue, Qiaomu Shen, Yuxin Ma, Min Zhu

Research Collection School Of Computing and Information Systems

Existing dynamic weighted graph visualization approaches rely on users’ mental comparison to perceive temporal evolution of dynamic weighted graphs, hindering users from effectively analyzing changes across multiple timeslices. We propose DiffSeer, a novel approach for dynamic weighted graph visualization by explicitly visualizing the differences of graph structures (e.g., edge weight differences) between adjacent timeslices. Specifically, we present a novel nested matrix design that overviews the graph structure differences over a time period as well as shows graph structure details in the timeslices of user interest. By collectively considering the overall temporal evolution and structure details in each timeslice, an optimization-based …

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Boosting Just-In-Time Defect Prediction With Specific Features Of C/C++ Programming Languages In Code Changes, Chao Ni, Xiaodan Xu, Kaiwen Yang, David Lo May 2023

Boosting Just-In-Time Defect Prediction With Specific Features Of C/C++ Programming Languages In Code Changes, Chao Ni, Xiaodan Xu, Kaiwen Yang, David Lo

Research Collection School Of Computing and Information Systems

Just-in-time (JIT) defect prediction can identify changes as defect-inducing ones or clean ones and many approaches are proposed based on several programming language-independent change-level features. However, different programming languages have different characteristics and consequently may affect the quality of software projects. Meanwhile, the C programming language, one of the most popular ones, is widely used to develop foundation applications (i.e., operating system, database, compiler, etc.) in IT companies and its change-level characteristics on project quality have not been fully investigated. Additionally, whether open-source C projects have similar important features to commercial projects has not been studied much.To address the aforementioned …

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Ncq: Code Reuse Support For Node.Js Developers, Brittany Reid, Marcelo D'Amorim, Markus Wagner, Christoph Treude May 2023

Ncq: Code Reuse Support For Node.Js Developers, Brittany Reid, Marcelo D'Amorim, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Code reuse is an important part of software development. The adoption of code reuse practices is especially common among Node.js developers. The Node.js package manager, NPM, indexes over 1 Million packages and developers often seek out packages to solve programming tasks. Due to the vast number of packages, selecting the right package is difficult and time consuming. With the goal of improving productivity of developers that heavily reuse code through third-party packages, we present Node Code Query (NCQ), a Read-Eval-Print-Loop environment that allows developers to 1) search for NPM packages using natural language queries, 2) search for code snippets related …

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Understanding The Role Of Images On Stack Overflow, Dong Wang, Tao Xiao, Christoph Treude, Raula Kula, Hideaki Hata, Yasutaka Kamei May 2023

Understanding The Role Of Images On Stack Overflow, Dong Wang, Tao Xiao, Christoph Treude, Raula Kula, Hideaki Hata, Yasutaka Kamei

Research Collection School Of Computing and Information Systems

Images are increasingly being shared by software developers in diverse channels including question-and-answer forums like Stack Overflow. Although prior work has pointed out that these images are meaningful and provide complementary information compared to their associated text, how images are used to support questions is empirically unknown. To address this knowledge gap, in this paper we specifically conduct an empirical study to investigate (I) the characteristics of images, (II) the extent to which images are used in different question types, and (III) the role of images on receiving answers. Our results first show that user interface is the most common …

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Stop Words For Processing Software Engineering Documents: Do They Matter, Yaohou Fan, Chetan Arora, Christoph Treude May 2023

Stop Words For Processing Software Engineering Documents: Do They Matter, Yaohou Fan, Chetan Arora, Christoph Treude

Research Collection School Of Computing and Information Systems

Stop words, which are considered non-predictive, are often eliminated in natural language processing tasks. However, the definition of uninformative vocabulary is vague, so most algorithms use general knowledge-based stop lists to remove stop words. There is an ongoing debate among academics about the usefulness of stop word elimination, especially in domainspecific settings. In this work, we investigate the usefulness of stop word removal in a software engineering context. To do this, we replicate and experiment with three software engineering research tools from related work. Additionally, we construct a corpus of software engineering domain-related text from 10,000 Stack Overflow questions and …

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Message From The Chairs: Techdebt 2023, Christoph Treude, Yuanfang Cai, Xin Xia, Zadia Codabux, Hideaki Hata, Florian Deissenboeck, Rodrigo Spinola May 2023

Message From The Chairs: Techdebt 2023, Christoph Treude, Yuanfang Cai, Xin Xia, Zadia Codabux, Hideaki Hata, Florian Deissenboeck, Rodrigo Spinola

Research Collection School Of Computing and Information Systems

Welcome to the 6th ACM/IEEE International Conference on Technical Debt, TechDebt 2023, co-located with the International Conference on Software Engineering (ICSE) 2023, in the beautiful city of Melbourne, Australia. After several years of virtual and hybrid conferences, TechDebt 2023 marks the first predominantly in-person edition of the conference series since the onset of the Covid-19 pandemic.

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Navigating Complexity In Software Engineering: A Prototype For Comparing Gpt-N Solutions, Christoph Treude May 2023

Navigating Complexity In Software Engineering: A Prototype For Comparing Gpt-N Solutions, Christoph Treude

Research Collection School Of Computing and Information Systems

Navigating the diverse solution spaces of non-trivial software engineering tasks requires a combination of technical knowledge, problem-solving skills, and creativity. With multiple possible solutions available, each with its own set of trade-offs, it is essential for programmers to evaluate the various options and select the one that best suits the specific requirements and constraints of a project. Whether it is choosing from a range of libraries, weighing the pros and cons of different architecture and design solutions, or finding unique ways to fulfill user requirements, the ability to think creatively is crucial for making informed decisions that will result in …

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Lpt: Long-Tailed Prompt Tuning For Image Classification, Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo May 2023

Lpt: Long-Tailed Prompt Tuning For Image Classification, Bowen Dong, Pan Zhou, Shuicheng Yan, Wangmeng Zuo

Research Collection School Of Computing and Information Systems

For long-tailed classification tasks, most works often pretrain a big model on a large-scale (unlabeled) dataset, and then fine-tune the whole pretrained model for adapting to long-tailed data. Though promising, fine-tuning the whole pretrained model tends to suffer from high cost in computation and deployment of different models for different tasks, as well as weakened generalization capability for overfitting to certain features of long-tailed data. To alleviate these issues, we propose an effective Long-tailed Prompt Tuning (LPT) method for long-tailed classification tasks. LPT introduces several trainable prompts into a frozen pretrained model to adapt it to long-tailed data. For better …

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Towards Understanding Why Mask Reconstruction Pretraining Helps In Downstream Tasks, Jiachun Pan, Pan Zhou, Shuicheng Yan May 2023

Towards Understanding Why Mask Reconstruction Pretraining Helps In Downstream Tasks, Jiachun Pan, Pan Zhou, Shuicheng Yan

Research Collection School Of Computing and Information Systems

For unsupervised pretraining, mask-reconstruction pretraining (MRP) approaches, e.g. MAE (He et al., 2021) and data2vec (Baevski et al., 2022), randomly mask input patches and then reconstruct the pixels or semantic features of these masked patches via an auto-encoder. Then for a downstream task, supervised fine-tuning the pretrained encoder remarkably surpasses the conventional “supervised learning" (SL) trained from scratch. However, it is still unclear 1) how MRP performs semantic feature learning in the pretraining phase and 2) why it helps in downstream tasks. To solve these problems, we first theoretically show that on an auto-encoder of a two/one-layered convolution encoder/decoder, MRP …

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Graph Neural Point Process For Temporal Interaction Prediction, Wenwen Xia, Yuchen Li, Shengdong Li May 2023

Graph Neural Point Process For Temporal Interaction Prediction, Wenwen Xia, Yuchen Li, Shengdong Li

Research Collection School Of Computing and Information Systems

Temporal graphs are ubiquitous data structures in many scenarios, including social networks, user-item interaction networks, etc. In this paper, we focus on predicting the exact time of the next interaction, given a node pair on a temporal graph. This novel problem can support interesting applications, such as time-sensitive items recommendation, congestion prediction on road networks, and many others. We present Graph Neural Point Process (GNPP) to tackle this problem. GNPP relies on the graph neural message passing and the temporal point process framework. Most previous graph neural models only utilize the chronological order of observed events and ignore exact timestamps. …

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Arousing Motives Or Eliciting Stories? On The Role Of Pictures In A Picture–Story Exercise, Philipp Schäpers, Stefan Krumm, Filip Lievens, Nikola Stenzel Apr 2023

Arousing Motives Or Eliciting Stories? On The Role Of Pictures In A Picture–Story Exercise, Philipp Schäpers, Stefan Krumm, Filip Lievens, Nikola Stenzel

Research Collection Lee Kong Chian School Of Business

Picture–story exercises (PSE) form a popular measurement approach that has been widely used for the assessment of implicit motives. However, current theorizing offers two diverging perspectives on the role of pictures in PSEs: either to elicit stories or to arouse motives. In the current study, we tested these perspectives in an experimental design. We administered a PSE either with or without pictures. Results from N = 281 participants revealed that the experimental manipulation had a medium to large effect for the affiliation and power motive domains, but no effect for the achievement motive domain. We conclude that the herein chosen …

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Engaging Students Through Conversational Chatbots And Digital Content: A Climate Action Perspective, Thomas Menkhoff, Benjamin Gan Apr 2023

Engaging Students Through Conversational Chatbots And Digital Content: A Climate Action Perspective, Thomas Menkhoff, Benjamin Gan

Research Collection Lee Kong Chian School Of Business

In this case study, we report experiences deploying a conversational chatbot as a pre-class and post-class engagement tool for undergraduate students enrolled in sustainability-related courses aimed at educating them about the severity of climate change and the importance of climate action by offsetting one’s carbon footprint (e.g, by planting trees or mangroves in SEA). The intitiative supports the university’s sustainability efforts in general and our new sustainability major in particular aimed at helping students to achieve sustainability-related learning outcomes with reference to climate change and climate action (SDG 13), one of the 17 Sustainable Development Goals established by the United …

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Knowledge, Skills, And Workforce Development: Commentary On Ackerman, Frederick L. Oswald, Karyssa A. Courey, Zihan Liu Apr 2023

Knowledge, Skills, And Workforce Development: Commentary On Ackerman, Frederick L. Oswald, Karyssa A. Courey, Zihan Liu

Research Collection Lee Kong Chian School Of Business

Ackerman (2023) proposed incorporating a more comprehensive range of knowledge and skills into the conceptualization and operationalization of intelligence beyond traditional assessments. In agreeing with and extending this proposition, the current commentary focuses on the specific context of workforce development and highlights three critical considerations: (a) the development of knowledge and skills at different life stages of education and work, (b) the role of social contexts in knowledge and skill acquisition, and (c) quantitative and qualitative approaches to understanding and assessing a broader set of knowledge and skills in light of ongoing changes in the workplace. Overall, we encourage greater …

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Giving Back: Contributions Congruent To Library Dependency Changes In A Software Ecosystem, Supatsara Wattanakriengkrai, Dong Wang, Raula Gaikovina Kula, Christoph Treude, Patanamon Thongtanunam, Takashi Ishio, Kenichi Matsumoto Apr 2023

Giving Back: Contributions Congruent To Library Dependency Changes In A Software Ecosystem, Supatsara Wattanakriengkrai, Dong Wang, Raula Gaikovina Kula, Christoph Treude, Patanamon Thongtanunam, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The widespread adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. To understand these contributions, in this work, we leverage socio-technical techniques to introduce and formalise dependency-contribution congruence (DC congruence) at both ecosystem and library level, i.e., to understand the degree and origins of contributions congruent to dependency changes, analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate …

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Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu Apr 2023

Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu

Research Collection School Of Computing and Information Systems

Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, ., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text ., …

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Parsing-Conditioned Anime Translation: A New Dataset And Method, Zhansheng Li, Yangyang Xu, Nanxuan Zhao, Yang Zhou, Yongtuo Liu, Dahua Lin, Shengfeng He Apr 2023

Parsing-Conditioned Anime Translation: A New Dataset And Method, Zhansheng Li, Yangyang Xu, Nanxuan Zhao, Yang Zhou, Yongtuo Liu, Dahua Lin, Shengfeng He

Research Collection School Of Computing and Information Systems

Anime is an abstract art form that is substantially different from the human portrait, leading to a challenging misaligned image translation problem that is beyond the capability of existing methods. This can be boiled down to a highly ambiguous unconstrained translation between two domains. To this end, we design a new anime translation framework by deriving the prior knowledge of a pre-Trained StyleGAN model. We introduce disentangled encoders to separately embed structure and appearance information into the same latent code, governed by four tailored losses. Moreover, we develop a FaceBank aggregation method that leverages the generated data of the StyleGAN, …

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Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee Apr 2023

Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design …

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C-Wall: Conflict-Resistance In Privacy-Preserving Cloud Storage, Xiaoguo Li, Tao Xiang, Yi Mu, Fuchun Guo, Zhongyuan Yao Apr 2023

C-Wall: Conflict-Resistance In Privacy-Preserving Cloud Storage, Xiaoguo Li, Tao Xiang, Yi Mu, Fuchun Guo, Zhongyuan Yao

Research Collection School Of Computing and Information Systems

Following the success of cloud computing, it has been shown its importance to realize various access control models in the cloud storage setting. Chinese Wall is a traditional access control model in business for solving the conflict of interest (CoI) problem, and it would be very interesting to achieve conflict-resistant in cloud storage system. However, the access control model does not ensure the privacy of users, and it may reveal the user's interest, investment tendency, etc. Therefore, it raises a big challenge to implement the Chinese Wall without compromising the user's privacy. In this paper, we focus on the Chinese …

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A Learner-Verifier Framework For Neural Network Controllers And Certificates Of Stochastic Systems, Krishnendu Chatterjee, Thomas A. Henzinger, Dorde Zikelic, Dorde Zikelic Apr 2023

A Learner-Verifier Framework For Neural Network Controllers And Certificates Of Stochastic Systems, Krishnendu Chatterjee, Thomas A. Henzinger, Dorde Zikelic, Dorde Zikelic

Research Collection School Of Computing and Information Systems

Reinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifer framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, …

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Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara Apr 2023

Supporting Novices Author Audio Descriptions Via Automatic Feedback, Rosiana Natalie, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to …

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