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Full-Text Articles in Physical Sciences and Mathematics

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee Peng Lim, Aldy Gunawan Dec 2021

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a datadriven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

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Microservices Orchestration Vs. Choreography: A Decision Framework, Alan @ Ali Madjelisi Megargel, Christopher M. Poskitt, Shankararaman, Venky Dec 2021

Microservices Orchestration Vs. Choreography: A Decision Framework, Alan @ Ali Madjelisi Megargel, Christopher M. Poskitt, Shankararaman, Venky

Research Collection School Of Computing and Information Systems

Microservices-based applications consist of loosely coupled, independently deployable services that encapsulate units of functionality. To implement larger application processes, these microservices must communicate and collaborate. Typically, this follows one of two patterns: (1) choreography, in which communication is done via asynchronous message-passing; or (2) orchestration, in which a controller is used to synchronously manage the process flow. Choosing the right pattern requires the resolution of some trade-offs concerning coupling, chattiness, visibility, and design. To address this problem, we propose a decision framework for microservices collaboration patterns that helps solution architects to crystallize their goals, compare the key factors, and then …

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Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport Dec 2021

Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport

Research Collection School Of Computing and Information Systems

To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.

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Rmix: Learning Risk-Sensitive Policies For Cooperative Reinforcement Learning Agents, Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich Dec 2021

Rmix: Learning Risk-Sensitive Policies For Cooperative Reinforcement Learning Agents, Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Research Collection School Of Computing and Information Systems

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE). However, such expected, i.e., risk-neutral, Q value is not sufficient even with CTDE due to the randomness of rewards and the uncertainty in environments, which causes the failure of these methods to train coordinating agents in complex environments. To address these issues, we propose RMIX, a novel cooperative MARL method with the Conditional Value at Risk (CVaR) measure over the learned distributions of individuals' Q values. Specifically, we first learn the return distributions of individuals to analytically calculate CVaR …

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Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger Dec 2021

Infinite Time Horizon Safety Of Bayesian Neural Networks, Mathias Lechner, Dorde Zikelic, Krishnendu Chatterjee, Thomas A. Henzinger

Research Collection School Of Computing and Information Systems

Bayesian neural networks (BNNs) place distributions over the weights of a neural network to model uncertainty in the data and the network’s prediction. We consider the problem of verifying safety when running a Bayesian neural network policy in a feedback loop with infinite time horizon systems. Compared to the existing sampling-based approaches, which are inapplicable to the infinite time horizon setting, we train a separate deterministic neural network that serves as an infinite time horizon safety certificate. In particular, we show that the certificate network guarantees the safety of the system over a subset of the BNN weight posterior’s support. …

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Functional Signatures: New Definition And Constructions, Qingwen Guo, Qiong Huang, Sha Ma, Meiyan Xiao, Guomin Yang, Willy Susilo Dec 2021

Functional Signatures: New Definition And Constructions, Qingwen Guo, Qiong Huang, Sha Ma, Meiyan Xiao, Guomin Yang, Willy Susilo

Research Collection School Of Computing and Information Systems

Functional signatures (FS) enable a master authority to delegate its signing privilege to an assistant. Concretely, the master authority uses its secret key sk(F) to issue a signing key sk(f) for a designated function f is an element of F-FS and sends both f and sk(f) to the assistant E, which is then able to compute a signature sigma(f) with respect to pk(F) for a message y in the range of f. In this paper, we modify the syntax of FS slightly to support the application scenario where a certificate of authorization is necessary. Compared with the original FS, our …

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Adadeep: A Usage-Driven, Automated Deep Model Compression Framework For Enabling Ubiquitous Intelligent Mobiles, Sicong Liu, Junzhao Du, Kaiming Nan, Zimu Zhou, Hui Liu, Zhangyang Wang, Yingyan Lin Dec 2021

Adadeep: A Usage-Driven, Automated Deep Model Compression Framework For Enabling Ubiquitous Intelligent Mobiles, Sicong Liu, Junzhao Du, Kaiming Nan, Zimu Zhou, Hui Liu, Zhangyang Wang, Yingyan Lin

Research Collection School Of Computing and Information Systems

Recent breakthroughs in deep neural networks (DNNs) have fueled a tremendously growing demand for bringing DNN-powered intelligence into mobile platforms. While the potential of deploying DNNs on resource-constrained platforms has been demonstrated by DNN compression techniques, the current practice suffers from two limitations: 1) merely stand-alone compression schemes are investigated even though each compression technique only suit for certain types of DNN layers; and 2) mostly compression techniques are optimized for DNNs’ inference accuracy, without explicitly considering other application-driven system performance (e.g., latency and energy cost) and the varying resource availability across platforms (e.g., storage and processing capability). To this …

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Verification Assisted Gas Reduction For Smart Contracts, Bo Gao, Siyuan Shen, Ling Shi, Jiaying Li, Jun Sun, Lei Bu Dec 2021

Verification Assisted Gas Reduction For Smart Contracts, Bo Gao, Siyuan Shen, Ling Shi, Jiaying Li, Jun Sun, Lei Bu

Research Collection School Of Computing and Information Systems

Smart contracts are computerized transaction protocols built on top of blockchain networks. Users are charged with fees, a.k.a. gas in Ethereum, when they create, deploy or execute smart contracts. Since smart contracts may contain vulnerabilities which may result in huge financial loss, developers and smart contract compilers often insert codes for security checks. The trouble is that those codes consume gas every time they are executed. Many of the inserted codes are however redundant. In this work, we present sOptimize, a tool that optimizes smart contract gas consumption automatically without compromising functionality or security. sOptimize works on smart contract bytecode, …

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A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun Dec 2021

A Fine-Grained Attribute Based Data Retrieval With Proxy Re-Encryption Scheme For Data Outsourcing Systems, Hanshu Hong, Ximeng Liu, Zhixin Sun

Research Collection School Of Computing and Information Systems

Attribute based encryption is suitable for data protection in data outsourcing systems such as cloud computing. However, the leveraging of encryption technique may retrain some routine operations over the encrypted data, particularly in the field of data retrieval. This paper presents an attribute based date retrieval with proxy re-encryption (ABDR-PRE) to provide both fine-grained access control and retrieval over the ciphertexts. The proposed scheme achieves fine-grained data access management by adopting KP-ABE mechanism, a delegator can generate the re-encryption key and search indexes for the ciphertexts to be shared over the target delegatee’s attributes. Throughout the process of data sharing, …

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Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He Dec 2021

Video Snapshot: Single Image Motion Expansion Via Invertible Motion Embedding, Qianshu Zhu, Chu Han, Guoqiang Han, Tien-Tsin Wong, Shengfeng He

Research Collection School Of Computing and Information Systems

Unlike images, finding the desired video content in a large pool of videos is not easy due to the time cost of loading and watching. Most video streaming and sharing services provide the video preview function for a better browsing experience. In this paper, we aim to generate a video preview from a single image. To this end, we propose two cascaded networks, the motion embedding network and the motion expansion network. The motion embedding network aims to embed the spatio-temporal information into an embedded image, called video snapshot. On the other end, the motion expansion network is proposed to …

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A Theory-Driven Self-Labeling Refinement Method For Contrastive Representation Learning, Pan Zhou, Caiming Xiong, Xiao-Tong Yuan Dec 2021

A Theory-Driven Self-Labeling Refinement Method For Contrastive Representation Learning, Pan Zhou, Caiming Xiong, Xiao-Tong Yuan

Research Collection School Of Computing and Information Systems

For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives. Although intuitive, such a native label assignment strategy cannot reveal the underlying semantic similarity between a query and its positives and negatives, and impairs performance, since some negatives are semantically similar to the query or even share the same semantic class as the query. In this work, we first prove that for contrastive learning, inaccurate label assignment heavily impairs its generalization for semantic instance discrimination, while accurate labels benefit its generalization. Inspired by this theory, we propose a novel …

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Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong Dec 2021

Hierarchical Control Of Multi-Agent Reinforcement Learning Team In Real-Time Strategy (Rts) Games, Weigui Jair Zhou, Budhitama Subagdja, Ah-Hwee Tan, Darren Wee Sze Ong

Research Collection School Of Computing and Information Systems

Coordinated control of multi-agent teams is an important task in many real-time strategy (RTS) games. In most prior work, micromanagement is the commonly used strategy whereby individual agents operate independently and make their own combat decisions. On the other extreme, some employ a macromanagement strategy whereby all agents are controlled by a single decision model. In this paper, we propose a hierarchical command and control architecture, consisting of a single high-level and multiple low-level reinforcement learning agents operating in a dynamic environment. This hierarchical model enables the low-level unit agents to make individual decisions while taking commands from the high-level …

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Vireo @ Trecvid 2021 Ad-Hoc Video Search, Jiaxin Wu, Phuong Anh Nguyen, Chong-Wah Ngo Dec 2021

Vireo @ Trecvid 2021 Ad-Hoc Video Search, Jiaxin Wu, Phuong Anh Nguyen, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

In this paper, we summarize our submitted runs and results for Ad-hoc Video Search (AVS) task at TRECVid 2020

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Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko Dec 2021

Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …

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Spurring Digital Transformation In Singapore's Legal Industry, Xin Juan Chua, Steven M. Miller Dec 2021

Spurring Digital Transformation In Singapore's Legal Industry, Xin Juan Chua, Steven M. Miller

Research Collection School Of Computing and Information Systems

COVID-19 has transformed the way we live and work. It has caused the processes and operations of businesses and organisations to be restructured, as well as transformed business models. A 2020 McKinsey Global survey reported that companies all over the world claim they have accelerated the digitalisation of their customer and supply-chain interactions, as well as their internal operations, by three to four years. They also said they thought the share of digital or digitally enabled products in their portfolios has advanced by seven years. While technology transformation is not new to the legal profession, COVID-19 has cemented the importance …

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Hrpdf: A Software-Based Heterogeneous Redundant Proactive Defense Framework For Programmable Logic Controller, Ke Liu, Jing-Yi Wang, Qiang Wei, Zhen-Yong Zhang, Jun Sun, Rong-Kuan Ma, Rui-Long Deng Dec 2021

Hrpdf: A Software-Based Heterogeneous Redundant Proactive Defense Framework For Programmable Logic Controller, Ke Liu, Jing-Yi Wang, Qiang Wei, Zhen-Yong Zhang, Jun Sun, Rong-Kuan Ma, Rui-Long Deng

Research Collection School Of Computing and Information Systems

Programmable logic controllers (PLCs) play a critical role in many industrial control systems, yet face increasingly serious cyber threats. In this paper, we propose a novel PLC-compatible software-based defense mechanism, called Heterogeneous Redundant Proactive Defense Framework (HRPDF). We propose a heterogeneous PLC architecture in HRPDF, including multiple heterogeneous, equivalent, and synchronous runtimes, which can thwart multiple types of attacks against PLC without the need of external devices. To ensure the availability of PLC, we also design an inter-process communication algorithm that minimizes the overhead of HRPDF. We implement a prototype system of HRPDF and test it in a real-world PLC …

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Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin Dec 2021

Graph Learning Assisted Multi-Objective Integer Programming, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Abhishek Gupta, Mingyan Simon Lin

Research Collection School Of Computing and Information Systems

Objective-space decomposition algorithms (ODAs) are widely studied for solvingmulti-objective integer programs. However, they often encounter difficulties inhandling scalarized problems, which could cause infeasibility or repetitive nondominatedpoints and thus induce redundant runtime. To mitigate the issue, we presenta graph neural network (GNN) based method to learn the reduction rule in the ODA.We formulate the algorithmic procedure of generic ODAs as a Markov decisionprocess, and parameterize the policy (reduction rule) with a novel two-stage GNNto fuse information from variables, constraints and especially objectives for betterstate representation. We train our model with imitation learning and deploy it ona state-of-the-art ODA. Results show that …

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Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang Dec 2021

Neurolkh: Combining Deep Learning Model With Lin-Kernighan-Helsgaun Heuristic For Solving The Traveling Salesman Problem, Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem. Specifically, we train a Sparse Graph Network (SGN) with supervised learning for edge scores and unsupervised learning for node penalties, both of which are critical for improving the performance of LKH. Based on the output of SGN, NeuroLKH creates the edge candidate set and transforms edge distances to guide the searching process of LKH. Extensive experiments firmly demonstrate that, by training one model on a wide range of problem sizes, NeuroLKH significantly outperforms LKH and generalizes well to …

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Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang Dec 2021

Learning To Iteratively Solve Routing Problems With Dual-Aspect Collaborative Transformer, Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang

Research Collection School Of Computing and Information Systems

Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP because its positional encoding (PE) method is not suitable in representing VRP solutions. This paper presents a novel Dual-Aspect Collaborative Transformer (DACT) to learn embeddings for the node and positional features separately, instead of fusing them together as done in existing ones, so as to avoid potential noises and incompatible correlations. Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry …

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Integration Of Information Technology Certifications Into Undergraduate Computing Curriculum, Eng Lieh Ouh, Kyong Jin Shim Dec 2021

Integration Of Information Technology Certifications Into Undergraduate Computing Curriculum, Eng Lieh Ouh, Kyong Jin Shim

Research Collection School Of Computing and Information Systems

This innovative practice full paper describes our experiences of integrating information technology certifications into an undergraduate computing curriculum. As the technology landscape evolves, a common challenge for educators in computing programs is designing an industry-relevant curriculum. Over the years, industry practitioners have taken technology certifications to validate themselves against a base level of technical knowledge currently in demand in industry. Information technology (IT) certifications can also offer paths for academic computing programs to stay relevant to industry needs. However, identifying relevant IT certifications and integrating it into an academic curriculum requires a careful design approach as substantial efforts are needed …

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Towards Non-Intrusive Camera-Based Heart Rate Variability Estimation In The Car Under Naturalistic Condition, Shu Liu, Kevin Koch, Zimu Zhou, Martin Maritsch, Xiaoxi He, Elgar Fleisch, Felix Wortmann Dec 2021

Towards Non-Intrusive Camera-Based Heart Rate Variability Estimation In The Car Under Naturalistic Condition, Shu Liu, Kevin Koch, Zimu Zhou, Martin Maritsch, Xiaoxi He, Elgar Fleisch, Felix Wortmann

Research Collection School Of Computing and Information Systems

Driver status monitoring systems are a vital component of smart cars in the future, especially in the era when an increasing amount of time is spent in the vehicle. The heart rate (HR) is one of the most important physiological signals of driver status. To infer HR of drivers, the mainstream of existing research focused on capturing subtle heartbeat-induced vibration of the torso or leveraged photoplethysmography (PPG) that detects cardiac cycle-related blood volume changes in the microvascular. However, existing approaches rely on dedicated sensors that are expensive and cumbersome to be integrated or are vulnerable to ambient noise. Moreover, their …

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On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan Dec 2021

On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan

Research Collection School Of Computing and Information Systems

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a data-driven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …

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Empirical Evaluation Of Minority Oversampling Techniques In The Context Of Android Malware Detection, Lwin Khin Shar, Nguyen Binh Duong Ta, David Lo Dec 2021

Empirical Evaluation Of Minority Oversampling Techniques In The Context Of Android Malware Detection, Lwin Khin Shar, Nguyen Binh Duong Ta, David Lo

Research Collection School Of Computing and Information Systems

In Android malware classification, the distribution of training data among classes is often imbalanced. This causes the learning algorithm to bias towards the dominant classes, resulting in mis-classification of minority classes. One effective way to improve the performance of classifiers is the synthetic generation of minority instances. One pioneer technique in this area is Synthetic Minority Oversampling Technique (SMOTE) and since its publication in 2002, several variants of SMOTE have been proposed and evaluated on various imbalanced datasets. However, these techniques have not been evaluated in the context of Android malware detection. Studies have shown that the performance of SMOTE …

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Statistical Moderation: A Case Study In Grading On A Curve, Manoj Thulasidas Dec 2021

Statistical Moderation: A Case Study In Grading On A Curve, Manoj Thulasidas

Research Collection School Of Computing and Information Systems

There is a negative perception about “grading on a curve,” because of the feeling that the cohort strength may skew the final grades one way or another. However, given the difficulties in ensuring absolute uniformity in assessment across the years, especially when taught and assessed by different instructors under different settings, grading on a curve may be a necessary evil. Once we accept this type of statistical moderation as the last line of defense in standardizing the final scores so that student cohorts from different terms or sections or schools may be compared, we have to implement it well. In …

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Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng Dec 2021

Context-Aware Graph Convolutional Network For Dynamic Origin-Destination Prediction, Juan Nathaniel, Baihua Zheng

Research Collection School Of Computing and Information Systems

A robust Origin-Destination (OD) prediction is key to urban mobility. A good forecasting model can reduce operational risks and improve service availability, among many other upsides. Here, we examine the use of Graph Convolutional Net-work (GCN) and its hybrid Markov-Chain (GCN-MC) variant to perform a context-aware OD prediction based on a large-scale public transportation dataset in Singapore. Compared with the baseline Markov-Chain algorithm and GCN, the proposed hybrid GCN-MC model improves the prediction accuracy by 37% and 12% respectively. Lastly, the addition of temporal and historical contextual information further improves the performance of the proposed hybrid model by 4 –12%.

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Linear Algebra For Computer Science, M. Thulasidas Dec 2021

Linear Algebra For Computer Science, M. Thulasidas

Research Collection School Of Computing and Information Systems

This textbook introduces the essential concepts and practice of Linear Algebra to the undergraduate student of computer science. The focus of this book is on the elegance and beauty of the numerical techniques and algorithms originating from Linear Algebra. As a practical handbook for computer and data scientists, LA4CS restricts itself mostly to real fields and tractable discourses, rather than deep and theoretical mathematics.

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Beyond Smoothness : Incorporating Low-Rank Analysis Into Nonparametric Density Estimation, Rob Vandermeulen, Antoine Ledent Dec 2021

Beyond Smoothness : Incorporating Low-Rank Analysis Into Nonparametric Density Estimation, Rob Vandermeulen, Antoine Ledent

Research Collection School Of Computing and Information Systems

The construction and theoretical analysis of the most popular universally consistent nonparametric density estimators hinge on one functional property: smoothness. In this paper we investigate the theoretical implications of incorporating a multi-view latent variable model, a type of low-rank model, into nonparametric density estimation. To do this we perform extensive analysis on histogram-style estimators that integrate a multi-view model. Our analysis culminates in showing that there exists a universally consistent histogram-style estimator that converges to any multi-view model with a finite number of Lipschitz continuous components at a rate of ˜O(1/3√n) in L1 error. In contrast, the standard histogram estimator …

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Concise Mercurial Subvector Commitments: Definitions And Constructions, Yannan Li, Willy Susilo, Guomin Yang, Tran Viet Xuan Phuong, Yong Yu, Dongxi Liu Dec 2021

Concise Mercurial Subvector Commitments: Definitions And Constructions, Yannan Li, Willy Susilo, Guomin Yang, Tran Viet Xuan Phuong, Yong Yu, Dongxi Liu

Research Collection School Of Computing and Information Systems

Vector commitment and its variants have attracted a lot of attention recently as they have been exposed to a wide range of applications in blockchain. Two special extensions of vector commitments, namely subvector commitments and mercurial commitments, have been proposed with attractive features that are desirable in many applications. Nevertheless, to the best of our knowledge, a single construction satisfying all those attractive features is still missing. In this work, we analyze those important properties and propose a new primitive called mercurial subvector commitments, which are efficiently updatable, mercurial hiding, position binding, and aggregatable. We formalize the system model and …

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Deriving Invariant Checkers For Critical Infrastructure Using Axiomatic Design Principles, Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, Arlindo Silva, Christopher M. Poskitt Dec 2021

Deriving Invariant Checkers For Critical Infrastructure Using Axiomatic Design Principles, Cheah Huei Yoong, Venkata Reddy Palleti, Rajib Ranjan Maiti, Arlindo Silva, Christopher M. Poskitt

Research Collection School Of Computing and Information Systems

Cyber-physical systems (CPSs) in critical infrastructure face serious threats of attack, motivating research into a wide variety of defence mechanisms such as those that monitor for violations of invariants, i.e. logical properties over sensor and actuator states that should always be true. Many approaches for identifying invariants attempt to do so automatically, typically using data logs, but these can miss valid system properties if relevant behaviours are not well-represented in the data. Furthermore, as the CPS is already built, resolving any design flaws or weak points identified through this process is costly. In this paper, we propose a systematic …

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Data Fusion For Trust Evaluation, Zheng Yan, Qinghua Zheng, Laurence T. Yang, Robert H. Deng Dec 2021

Data Fusion For Trust Evaluation, Zheng Yan, Qinghua Zheng, Laurence T. Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Trust evaluation is a process to quantify trust by analyzing the data related to the factors that affect trust. It has been widely applied in many fields to facilitate decision making, system entity collaboration and security establishment. For example, in social networking, trust evaluation helps users make a social decision, reduce the risk of social interactions, and ensure the quality of a social networking environment. In digital communications, trust evaluation can be applied to detect malicious nodes, filter unwanted traffic and improve communication security. In e-commerce and cloud services, trust evaluation helps users selecting an appropriate product or service from …

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