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Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen Jan 2023

Learning Large Neighborhood Search For Vehicle Routing In Airport Ground Handling, Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen

Research Collection School Of Computing and Information Systems

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated …

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Neighbor-Anchoring Adversarial Graph Neural Networks, Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng Jan 2023

Neighbor-Anchoring Adversarial Graph Neural Networks, Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng

Research Collection School Of Computing and Information Systems

Graph neural networks (GNNs) have witnessed widespread adoption due to their ability to learn superior representations for graph data. While GNNs exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where …

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Monadic Second-Order Incorrectness Logic For Gp 2, Christopher M. Poskitt, Detlef Plump Jan 2023

Monadic Second-Order Incorrectness Logic For Gp 2, Christopher M. Poskitt, Detlef Plump

Research Collection School Of Computing and Information Systems

Program logics typically reason about an over-approximation of program behaviour to prove the absence of bugs. Recently, program logics have been proposed that instead prove the presence of bugs by means of under-approximate reasoning, which has the promise of better scalability. In this paper, we present an under-approximate program logic for GP 2, a rule-based programming language for manipulating graphs. We define the proof rules of this program logic extensionally, i.e. independently of fixed assertion languages, then instantiate them with a morphism-based assertion language able to specify monadic second-order properties on graphs (e.g. path properties). We show how these proof …

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Sleepmore: Inferring Sleep Duration At Scale Via Multi-Device Wifi Sensing, Camellia Zakaria, Gizem Yilmaz, Priyanka Mammen, Michael Chee, Prashant Shenoy, Rajesh Krishna Balan Jan 2023

Sleepmore: Inferring Sleep Duration At Scale Via Multi-Device Wifi Sensing, Camellia Zakaria, Gizem Yilmaz, Priyanka Mammen, Michael Chee, Prashant Shenoy, Rajesh Krishna Balan

Research Collection School Of Computing and Information Systems

The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user retention in sleep monitoring through these modalities. Since modern Internet users own multiple mobile devices, our work explores the possibility of employing ubiquitous mobile devices and passive WiFi sensing techniques to predict sleep duration as the fundamental measure for complementing long-term sleep monitoring initiatives. In this paper, we propose SleepMore, an accurate and easy-to-deploy sleep-tracking approach based on machine learning over the user's WiFi network …

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A Secure Emr Sharing System With Tamper Resistance And Expressive Access Control, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng Jan 2023

A Secure Emr Sharing System With Tamper Resistance And Expressive Access Control, Shengmin Xu, Jianting Ning, Yingjiu Li, Yinghui Zhang, Guowen Xu, Xinyi Huang, Robert H. Deng

Research Collection School Of Computing and Information Systems

To reduce the cost of human and material resources and improve the collaborations among medical systems, research laboratories and insurance companies for healthcare researches and commercial activities, electronic medical records (EMRs) have been proposed to shift from paperwork to friendly shareable electronic records. To take advantage of EMRs efficiently and reduce the cost of local storage, EMRs are usually outsourced to the remote cloud for sharing medical data with authorized users. However, cloud service providers are untrustworthy. In this paper, we propose an efficient, secure, and flexible EMR sharing system by introducing a novel cryptosystem called dual-policy revocable attribute-based encryption …

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Vacsen: A Visualization Approach For Noise Awareness In Quantum Computing, Shaolun Ruan, Yong Wang, Weiwen Jiang, Ying Mao, Qiang Guan Jan 2023

Vacsen: A Visualization Approach For Noise Awareness In Quantum Computing, Shaolun Ruan, Yong Wang, Weiwen Jiang, Ying Mao, Qiang Guan

Research Collection School Of Computing and Information Systems

Quantum computing has attracted considerable public attention due to its exponential speedup over classical computing. Despite its advantages, today's quantum computers intrinsically suffer from noise and are error-prone. To guarantee the high fidelity of the execution result of a quantum algorithm, it is crucial to inform users of the noises of the used quantum computer and the compiled physical circuits. However, an intuitive and systematic way to make users aware of the quantum computing noise is still missing. In this paper, we fill the gap by proposing a novel visualization approach to achieve noise-aware quantum computing. It provides a holistic …

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Executive Functions Predict The Trajectories Of Rumination In Middle-Aged And Older Adults: A Latent Growth Curve Analysis, Gilaine Rui Ng, Wee Qin Ng, Hwajin Yang Jan 2023

Executive Functions Predict The Trajectories Of Rumination In Middle-Aged And Older Adults: A Latent Growth Curve Analysis, Gilaine Rui Ng, Wee Qin Ng, Hwajin Yang

Research Collection School of Social Sciences

Previous studies suggest that executive functions (EF)—a set of domain-general cognitive control processes that contribute to the regulation of emotion—are generally associated with ruminative tendencies. However, there is a dearth of research that examines how EF influences changes in rumination over time, especially in middle-aged and older adults who typically experience a decline in EF. To fill this gap in the literature, we analyzed a large-scale combined dataset from the MIDUS Refresher, Daily Diary, and Cognitive Projects. We examined the impact of EF on the trajectory of rumination across 8 days using latent growth curve analysis. We also examined age …

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Leading A Business School, Julie Davis, Howard Thomas, Eric Cornuel, Rolf D. Cremer Jan 2023

Leading A Business School, Julie Davis, Howard Thomas, Eric Cornuel, Rolf D. Cremer

Research Collection Lee Kong Chian School Of Business

Business schools are critical players in higher education, educating current and future leaders to make a difference in the world. Yet we know surprisingly little about the leaders of business schools. Leading a Business School demystifies this complex and dynamic role, offering international insights into deans’ dilemmas in different contexts and situations. It highlights the importance of deans creating challenging and supportive learning cultures to enhance business and management education, organizations and society more broadly. The book traces the historical evolution of the business school deanship, the current challenges and future sources of disruption. The leadership characteristics and styles of …

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Work Effort: A Conceptual And Meta-Analytic Review, Chad H. Van Iddekinge, John D. Arnold, Herman Aquinis, Jonas W. B. Lang, Filip Lievens Jan 2023

Work Effort: A Conceptual And Meta-Analytic Review, Chad H. Van Iddekinge, John D. Arnold, Herman Aquinis, Jonas W. B. Lang, Filip Lievens

Research Collection Lee Kong Chian School Of Business

Work effort has been a key concept in management theories and research for more than a century. Maintaining and increasing employee effort also is a persistent concern to managers. The goal of the present conceptual and meta-analytic review was to increase clarity and consensus regarding what effort is and how to measure it. First, we reviewed conceptualizations of effort and provided an integrated definition that views effort as a direct outcome of motivation that captures (a) what employees work on, (b) how hard they work, and (c) how long they persist in that work. Second, we identified four main ways …

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Personnel Selection: A Review Of Ways To Maximize Validity, Diversity, And The Applicant Experience, Chad H. Van Iddekinge, Filip Lievens, Paul R. Sackett Jan 2023

Personnel Selection: A Review Of Ways To Maximize Validity, Diversity, And The Applicant Experience, Chad H. Van Iddekinge, Filip Lievens, Paul R. Sackett

Research Collection Lee Kong Chian School Of Business

Personnel Psychology has a long tradition of publishing important research on personnel selection. In this article, we review some of the key questions and findings from studies published in the journal and in the selection literature more broadly. In doing so, we focus on the various decisions organizations face regarding selection procedure development (e.g., use multiple selection procedures, contextualize procedure content), administration (e.g., provide pre-test explanations, reveal target knowledge, skills, abilities, and other characteristics [KSAOs]), and scoring (e.g., weight predictors and criteria, use artificial intelligence). Further, we focus on how these decisions affect the validity of inferences drawn from the …

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Achieving High Map-Coverage Through Pattern Constraint Reduction, Yingquan Zhao, Zan Wang, Shuang Liu, Jun Sun, Junjie Chen, Xiang Chen Jan 2023

Achieving High Map-Coverage Through Pattern Constraint Reduction, Yingquan Zhao, Zan Wang, Shuang Liu, Jun Sun, Junjie Chen, Xiang Chen

Research Collection School Of Computing and Information Systems

Testing multi-threaded programs is challenging due to the enormous space of thread interleavings. Recently, a code coverage criterion for multi-threaded programs called MAP-coverage has been proposed and shown to be effective for testing concurrent programs. Existing approaches for achieving high MAP-coverage are based on random testing with simple heuristics, which is ineffective in systematically triggering rare thread interleavings. In this study, we propose a novel approach called pattern constraint reduction (PCR), which employs optimized constraint solving to generate thread interleavings for high MAP-coverage. The idea is to iteratively encode and solve path conditions to generate thread interleavings which are guaranteed …

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Postmaterialism And Corporate Tax Avoidance, Yujia Cui, Jiwei Wang, Kangtao Ye Jan 2023

Postmaterialism And Corporate Tax Avoidance, Yujia Cui, Jiwei Wang, Kangtao Ye

Research Collection School Of Accountancy

Using a proprietary dataset of China tax audits, we found that firms owned by investors from countries with higher postmaterialism values were less likely to engage in tax-avoidance behavior in China. In addition, we found some evidence that the negative association between postmaterialism and tax avoidance is more pronounced when tax enforcement is stronger, indicating that national culture and formal institutions act as complements. To check the external validity of our main results, we further used a cross-country sample from 21 countries over 22 years. The evidence from the cross-country sample was consistent with the findings obtained from the China …

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Bilingual Interactional Contexts Predict Executive Functions In Older Adults, Hwajin Yang, Yue Qi Germaine Tng, Gilaine Rui Ng, Wee Qin Ng Jan 2023

Bilingual Interactional Contexts Predict Executive Functions In Older Adults, Hwajin Yang, Yue Qi Germaine Tng, Gilaine Rui Ng, Wee Qin Ng

Research Collection School of Social Sciences

Drawing on the adaptive control hypothesis, we examined whether older adults' bilingual interactional contexts of conversational exchanges would predict important indices of executive functions (EF). We assessed participants' engagement in each bilingual interactional context - single-language, dual-language, and dense code-switching - and their performance on a series of nonverbal EF measures. Sixty-nine healthy older adults (M-age = 70.39 years; ages 60-93) were recruited from local community centers. We found that the dense code-switching context was associated with enhanced overall EF, but not individual facets of EF (inhibitory control, shifting, and updating). These findings held true when we controlled for a …

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The Demands Of Displacement, The Micro-Aggressions Of Multiculturalism: Performing An Idea Of "Indianness" In Singapore, Orlando Woods, Lily Kong Jan 2023

The Demands Of Displacement, The Micro-Aggressions Of Multiculturalism: Performing An Idea Of "Indianness" In Singapore, Orlando Woods, Lily Kong

Research Collection College of Integrative Studies

This paper explores the ways in which state-defined discourses of multiculturalism can unintentionally create a framework through which micro-aggressions are enacted against those interpreted as "other". These definitions cascade down from the state to majority and then minority ethno-national groups, who leverage positions of relative dominance to establish the terms of acceptance and integration into society. By negotiating these terms, ethnicity becomes a performative construct through which difference is asserted and reified. We illustrate these ideas through an empirical analysis of Singapore's minority Indian community, and how Singaporean Indians perform an idea of "Indianness" in response to their Singaporean Chinese …

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Natural Language Processing In The Legal Domain, Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Ii Bommarito Jan 2023

Natural Language Processing In The Legal Domain, Daniel Martin Katz, Dirk Hartung, Lauritz Gerlach, Abhik Jana, Michael J. Ii Bommarito

Research Collection Yong Pung How School Of Law

In this paper, we summarize the current state of the field of NLP and Law with a specific focus on recent technical and substantive developments. To support our analysis, we construct and analyze a corpus of more than six hundred NLP and Law related papers published over the past decade. Our analysis highlights several major trends. Namely, we document an increasing number of papers written, tasks undertaken, and languages covered over the course of the past decade. We observe an increase in the sophistication of the methods which researchers deployed in this applied context. Slowly but surely, Legal NLP is …

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Just-In-Time Obsolete Comment Detection And Update, Zhongxin Liu, Xin Xia, David Lo, Meng Yan, Shangping Li Jan 2023

Just-In-Time Obsolete Comment Detection And Update, Zhongxin Liu, Xin Xia, David Lo, Meng Yan, Shangping Li

Research Collection School Of Computing and Information Systems

Comments are valuable resources for the development, comprehension and maintenance of software. However, while changing code, developers sometimes neglect the evolution of the corresponding comments, resulting in obsolete comments. Such obsolete comments can mislead developers and introduce bugs in the future, and are therefore detrimental. We notice that by detecting and updating obsolete comments in time with code changes, obsolete comments can be effectively reduced and even avoided. We refer to this task as Just-In-Time (JIT) Obsolete Comment Detection and Update. In this work, we propose a two-stage framework named CUP2 (Two-stage Comment UPdater) to automate this task. CUP2 consists …

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Decentralizability Of Efficient Allocations With Heterogenous Forecasts, Shurojit Chatterji, Atsushi Kajii Jan 2023

Decentralizability Of Efficient Allocations With Heterogenous Forecasts, Shurojit Chatterji, Atsushi Kajii

Research Collection School Of Economics

Do price forecasts of rational economic agents need to coincide in perfectly competitive complete markets? To address this question, we define an efficient temporary equilibrium (ETE) within the framework of a two period economy. Although an ETE allocation is intertemporally efficient and is obtained by perfect competition, it can arise without the agents forecasts being completely coordinated on a perfect foresight price. Nevertheless, it entails price forecasts delicately related with each other: we show that regardless of the number of agents, there is a one dimensional set of such Pareto efficient allocations for generic endowments.

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A Mixture Autoregressive Model Based On Student’S T–Distribution, Mika Meitz, Daniel Preve, Pentti Saikkonen Jan 2023

A Mixture Autoregressive Model Based On Student’S T–Distribution, Mika Meitz, Daniel Preve, Pentti Saikkonen

Research Collection School Of Economics

A new mixture autoregressive model based on Student’s t–distribution is proposed. A key feature of our model is that the conditional t–distributions of the component models are based on autoregressions that have multivariate t–distributions as their (low-dimensional) stationary distributions. That autoregressions with such stationary distributions exist is not immediate. Our formulation implies that the conditional mean of each component model is a linear function of past observations and the conditional variance is also time-varying. Compared to previous mixture autoregressive models our model may therefore be useful in applications where the data exhibits rather strong conditional heteroskedasticity. Our formulation also has …

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Is Democracy Good For Growth? Development At Political Transition Time Matters, Di Sima, Fali Huang Jan 2023

Is Democracy Good For Growth? Development At Political Transition Time Matters, Di Sima, Fali Huang

Research Collection School Of Economics

Is democracy a better political regime for economic prosperity than autocracy? This paper shows that the answer depends on the initial economic development level during the democratic transition when the foundation of institutions was laid. Democracy facilitates growth only in countries that already have adequate development at transition time. These countries are more likely to create and sustain growth-enhancing institutions than others. Without appropriate development, democracy does not improve growth; this applies to about 40% of the third-wave democratized countries. These results are based on a sample of 153 countries in 1960–2010 and robust to various specifications and endogeneity issues.

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School Attendance Information Or Conditional Cash Transfer? Evidence From A Randomized Field Experiment In Rural Bangladesh, Tomoki Fujii, Christine Ho, Rohan Ray, Abu S. Shonchoy Jan 2023

School Attendance Information Or Conditional Cash Transfer? Evidence From A Randomized Field Experiment In Rural Bangladesh, Tomoki Fujii, Christine Ho, Rohan Ray, Abu S. Shonchoy

Research Collection School Of Economics

Low school attendance remains an important challenge in resource-poor settings with cash and information constraints. We compare conditional cash transfer (CCT) treatments with framing variations (gain and loss) against attendance information treatment as interventions to address these constraints in a unified framework. Our randomized evaluation shows CCT treatments increase attendance by 11 percentage points, about half of which is attributable to attendance information. These treatments improve girls’ academic aspirations and reduce early marriage. Daily CCT set at a quarter of local child wage maximizes attendance impact. We highlight the importance of low-cost information technology to boost attendance sustainably and cost-effectively.

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Retail Pharmacies And Drug Diversion During The Opioid Epidemic, Aljoscha Janssen, Xuan Zhang Jan 2023

Retail Pharmacies And Drug Diversion During The Opioid Epidemic, Aljoscha Janssen, Xuan Zhang

Research Collection School Of Economics

This study investigates the role of retail pharmacy ownership in the opioid epidemic. Using data of prescription opioid orders, we show that compared with chain pharmacies, independent pharmacies dispense 39.1% more opioids and 60.5% more OxyContin. After an independent pharmacy becomes a chain pharmacy, opioid dispensing decreases. Using the OxyContin reformulation, which reduced non-medical demand but not the legitimate medical demand, we show that at least a third of the difference in the amount of OxyContin dispensed can be attributed to non-medical demand. We show that differences in competitive pressure and whether pharmacists own the pharmacy drive our estimates.

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Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu Jan 2023

Volatility Puzzle: Long Memory Or Antipersistency, Shuping Shi, Jun Yu

Research Collection School Of Economics

The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1, d, 0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d

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Reks: Role-Based Encrypted Keyword Search With Enhanced Access Control For Outsourced Cloud Data, Yibin Miao, Feng Li, Xiaohua Jia, Huaxiong Wang, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng Jan 2023

Reks: Role-Based Encrypted Keyword Search With Enhanced Access Control For Outsourced Cloud Data, Yibin Miao, Feng Li, Xiaohua Jia, Huaxiong Wang, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Keyword-based search over encrypted data is an important technique to achieve both data confidentiality and utilization in cloud outsourcing services. While commonly used access control mechanisms, such as identity-based encryption and attribute-based encryption, do not generally scale well for hierarchical access permissions. To solve this problem, we propose a Role-based Encrypted Keyword Search (REKS) scheme by using the role-based access control and broadcast encryption. Specifically, REKS allows owners to deploy hierarchical access control by allowing users with parent roles to have access permissions from child roles. Using REKS, we further facilitate token generation preprocessing and efficient user management, thereby significantly …

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Seven Pillars For The Future Of Artificial Intelligence, Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho Jan 2023

Seven Pillars For The Future Of Artificial Intelligence, Erik Cambria, Rui Mao, Melvin Chen, Zhaoxia Wang, Seng-Beng Ho

Research Collection School Of Computing and Information Systems

In recent years, AI research has showcased tremendous potential to impact positively humanity and society. Although AI frequently outperforms humans in tasks related to classification and pattern recognition, it continues to face challenges when dealing with complex tasks such as intuitive decision-making, sense disambiguation, sarcasm detection, and narrative understanding, as these require advanced kinds of reasoning, e.g., commonsense reasoning and causal reasoning, which have not been emulated satisfactorily yet. To address these shortcomings, we propose seven pillars that we believe represent the key hallmark features for the future of AI, namely: Multidisciplinarity, Task Decomposition, Parallel Analogy, Symbol Grounding, Similarity Measure, …

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I Know What You Are Searching For: Code Snippet Recommendation From Stack Overflow Posts, Zhipeng Gao, Xin Xia, David Lo, John C. Grundy, Xindong Zhang, Zhenchang Xing Jan 2023

I Know What You Are Searching For: Code Snippet Recommendation From Stack Overflow Posts, Zhipeng Gao, Xin Xia, David Lo, John C. Grundy, Xindong Zhang, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Stack Overflow has been heavily used by software developers to seek programming-related information. More and more developers use Community Question and Answer forums, such as Stack Overflow, to search for code examples of how to accomplish a certain coding task. This is often considered to be more efficient than working from source documentation, tutorials, or full worked examples. However, due to the complexity of these online Question and Answer forums and the very large volume of information they contain, developers can be overwhelmed by the sheer volume of available information. This makes it hard to find and/or even be aware …

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Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?, Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li Jan 2023

Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?, Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li

Research Collection School Of Computing and Information Systems

Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory’s [25] hierarchical generative process and non-semantic factors by …

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Contextual Path Retrieval: A Contextual Entity Relation Embedding-Based Approach, Pei-Chi Lo, Ee-Peng Lim Jan 2023

Contextual Path Retrieval: A Contextual Entity Relation Embedding-Based Approach, Pei-Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Contextual path retrieval (CPR) refers to the task of finding contextual path(s) between a pair of entities in a knowledge graph that explains the connection between them in a given context. For this novel retrieval task, we propose the Embedding-based Contextual Path Retrieval (ECPR) framework. ECPR is based on a three-component structure that includes a context encoder and path encoder that encode query context and path, respectively, and a path ranker that assigns a ranking score to each candidate path to determine the one that should be the contextual path. For context encoding, we propose two novel context encoding methods, …

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Relation Preserving Triplet Mining For Stabilising The Triplet Loss In Re-Identification Systems, Adhiraj Ghosh, Kuruparan Shanmugalingam, Wen-Yan Lin Jan 2023

Relation Preserving Triplet Mining For Stabilising The Triplet Loss In Re-Identification Systems, Adhiraj Ghosh, Kuruparan Shanmugalingam, Wen-Yan Lin

Research Collection School Of Computing and Information Systems

Object appearances change dramatically with pose variations. This creates a challenge for embedding schemes that seek to map instances with the same object ID to locations that are as close as possible. This issue becomes significantly heightened in complex computer vision tasks such as re-identification(reID). In this paper, we suggest that these dramatic appearance changes are indications that an object ID is composed of multiple natural groups, and it is counterproductive to forcefully map instances from different groups to a common location. This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature matching guided triplet mining scheme, that …

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Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel Jan 2023

Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel

Research Collection School Of Computing and Information Systems

Changes in technology have shaped how corporate and retail businesses have evolved, alongside the customers’ preferences. The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions over the past two decades. With the rapid evolution of new technologies and customers' growing preference for digital engagement with financial institutions, organizations need to adopt and align with emerging technologies that support speed, accuracy, efficiency, and security in a user-friendly manner. Today, consumers want hyper-personalized interactions that are more frequent and proactive. Moreover, financial institutions have a growing need to cater to consumers' …

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Ranked Keyword Search Over Encrypted Cloud Data Through Machine Learning Method, Yinbin Miao, Wei Zheng, Xiaohua Jia, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng Jan 2023

Ranked Keyword Search Over Encrypted Cloud Data Through Machine Learning Method, Yinbin Miao, Wei Zheng, Xiaohua Jia, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, …

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