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Articles 49561 - 49590 of 294596

Full-Text Articles in Physical Sciences and Mathematics

Seagrass Soils Sequester The Equivalent Of Up To Half The Metal Emissions By One Of The World’S Largest Smelters, Anna Lafratta, Oscar Serrano, Pere Masque´, Milena Fernandes, Sam Gaylard, Paul S. Lavery Jan 2023

Seagrass Soils Sequester The Equivalent Of Up To Half The Metal Emissions By One Of The World’S Largest Smelters, Anna Lafratta, Oscar Serrano, Pere Masque´, Milena Fernandes, Sam Gaylard, Paul S. Lavery

Research Datasets

The database compiles published data (in Lafratta et al. 2023, Marine Pollution Bulletin) on physical, biogeochemical and chemical characteristics (coordinates, cm compressed and decompressed, dry weight (DW), dry bulk density (DBD), Pb, Zn and Cd concentrations analysed at Edith Cowan University (ECU), Pb, Zn and Cd concentrations analysed at University Autonoma of Barcelona (UAB), final Pb, Zn and Cd concentrations, organic carbon content (%), < 63 µm sediment particles content (%) and Pb isotopes ratio) of soils underneath Posidonia australis seagrass meadows in Port Pirie, South Australia. Cumulative dry mass, total and excess Pb-210 concentrations are also included. The dataset includes data for a total of twelve cores (PP1, PP2, PP3, PP4, PP5, …

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Seas Blue - The Iki Ses Project Se Asian Blue Carbon, Paul Lavery, Anna Lafratta Jan 2023

Seas Blue - The Iki Ses Project Se Asian Blue Carbon, Paul Lavery, Anna Lafratta

Research Datasets

The data set contains seagrass soil carbon (blue carbon) characteristics for four sites in each of Thailand (Trang), Malaysia (Johor and Penang) and The Philippines (Roxas), and for six sites in Indonesia (North Minahasa and Sangihe Island). For each site, four seagrass cores (100 cm long) were collected and then sliced at 1 cm intervals for the top 5 cm and for 5 cm intervals thereafter. Every slice was analysed for dry bulk density and Loss on Ignition (Organic Matter). Approximately 26 slices per core were analysed for Organic Carbon (elemental analysis). The data set provides the location coordinates for …

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Petrological And Geochemical Evaluation Of Rare-Earth Element Potential In The Sparta Granite Complex, Max J. Eshbaugh Jan 2023

Petrological And Geochemical Evaluation Of Rare-Earth Element Potential In The Sparta Granite Complex, Max J. Eshbaugh

Honors College Theses

Rare earth elements (REEs) are important resources with applications in the electronics, renewable energy, and automotive industries. REEs may be concentrated in the residual weathered portion of igneous parent rocks, typically granites. These residual deposits are mined in southeast Asia, but analogous climatic and geologic conditions suggest the existence of economically viable REE residual deposits in the southeastern United States (U.S.). The Sparta Granite Complex in east-central Georgia is a granitoid complex emplaced at the end of the Alleghanian orogeny, forming a suture between the Savannah River and Milledgeville terranes. Petrologic and geochemical assessments of the Sparta Granite and overlying …

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Children Tell Landscape-Lore Among Perceptions Of Place: Relating Ecocultural Digital Stories In A Conscientizing/Decolonizing Exploration, Meredith Jean Bird Miller Jan 2023

Children Tell Landscape-Lore Among Perceptions Of Place: Relating Ecocultural Digital Stories In A Conscientizing/Decolonizing Exploration, Meredith Jean Bird Miller

Antioch University Dissertations & Theses

We know that when children feel a sense-of-relation within local natural environments, they are more prone to feel concern for them, while nurturing well-being and resilience in themselves and in lands/waters they inhabit. Positive environmental behaviors often follow into adulthood. Our human capacities for creating sustainable solutions in response to growing repercussions of global warming and climate change may grow if more children feel a sense of belonging in the wild natural world. As educators, if we listen to and learn from students’ voices about how they engage in nature, we can create pedagogical experiences directly relevant to their lives. …

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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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Aspect Sentiment Triplet Extraction Incorporating Syntactic Constituency Parsing Tree And Commonsense Knowledge Graph, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan Jan 2023

Aspect Sentiment Triplet Extraction Incorporating Syntactic Constituency Parsing Tree And Commonsense Knowledge Graph, Zhenda Hu, Zhaoxia Wang, Yinglin Wang, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

The aspect sentiment triplet extraction (ASTE) task aims to extract the target term and the opinion term, and simultaneously identify the sentiment polarity of target-opinion pairs from the given sentences. While syntactic constituency information and commonsense knowledge are both important and valuable for the ASTE task, only a few studies have explored how to integrate them via flexible graph convolutional networks (GCNs) for this task. To address this gap, this paper proposes a novel end-to-end model, namely GCN-EGTS, which is an enhanced Grid Tagging Scheme (GTS) for ASTE leveraging syntactic constituency parsing tree and a commonsense knowledge graph based on …

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Is A Pretrained Model The Answer To Situational Awareness Detection On Social Media?, Siaw Ling Lo, Kahhe Lee, Yuhao Zhang Jan 2023

Is A Pretrained Model The Answer To Situational Awareness Detection On Social Media?, Siaw Ling Lo, Kahhe Lee, Yuhao Zhang

Research Collection School Of Computing and Information Systems

Social media can be valuable for extracting information about an event or incident on the ground. However, the vast amount of content shared, and the linguistic variants of languages used on social media make it challenging to identify important situational awareness content to aid in decision-making for first responders. In this study, we assess whether pretrained models can be used to address the aforementioned challenges on social media. Various pretrained models, including static word embedding (such as Word2Vec and GloVe) and contextualized word embedding (such as DistilBERT) are studied in detail. According to our findings, a vanilla DistilBERT pretrained language …

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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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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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Learning Analytics In Informal, Participatory Collaborative Learning, Michelle L. F. Cheong, Aditya Singh, Yun-Chen Chen, Bing Tian Dai Jan 2023

Learning Analytics In Informal, Participatory Collaborative Learning, Michelle L. F. Cheong, Aditya Singh, Yun-Chen Chen, Bing Tian Dai

Research Collection School Of Computing and Information Systems

Learning Analytics was recognized to be “the third wave of large-scale developments in instructional technology”. Learning Management Systems (LMSs) have been widely adopted as the learning analytics tools because the captured data represents how the learners’ interact with the system during formal learning. However, most LMSs’ analytics models do not capture learning activities outside the systems. We built an integrated Telegram mobile application and a web-based portal discussion forum, to enable informal, participatory and collaborative learning beyond the classroom. We analyzed student-initiated question-and-answer discussion posts where our machine learning algorithm will predict the quality of the posts, and the system …

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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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Taurus: Towards A Unified Force Representation And Universal Solver For Graph Layout, Mingliang Xue, Zhi Wang, Fahai Zhong, Yong Wang, Mingliang Xu, Oliver Deussen, Yunhai Wang Jan 2023

Taurus: Towards A Unified Force Representation And Universal Solver For Graph Layout, Mingliang Xue, Zhi Wang, Fahai Zhong, Yong Wang, Mingliang Xu, Oliver Deussen, Yunhai Wang

Research Collection School Of Computing and Information Systems

Over the past few decades, a large number of graph layout techniques have been proposed for visualizing graphs from various domains. In this paper, we present a general framework, Taurus, for unifying popular techniques such as the spring-electrical model, stress model, and maxent-stress model. It is based on a unified force representation, which formulates most existing techniques as a combination of quotient-based forces that combine power functions of graph-theoretical and Euclidean distances. This representation enables us to compare the strengths and weaknesses of existing techniques, while facilitating the development of new methods. Based on this, we propose a new balanced …

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Demonstrating Multi-Modal Human Instruction Comprehension With Ar Smart Glass, Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra Jan 2023

Demonstrating Multi-Modal Human Instruction Comprehension With Ar Smart Glass, Mudiyanselage Dulanga Kaveesha Weerakoon, Vigneshwaran Subbaraju, Tuan Tran, Archan Misra

Research Collection School Of Computing and Information Systems

We present a multi-modal human instruction comprehension prototype for object acquisition tasks that involve verbal, visual and pointing gesture cues. Our prototype includes an AR smart-glass for issuing the instructions and a Jetson TX2 pervasive device for executing comprehension algorithms. With this setup, we enable on-device, computationally efficient object acquisition task comprehension with an average latency in the range of 150-330msec.

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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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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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Reinforcement Learning Enhanced Pichunter For Interactive Search, Zhixin Ma, Jiaxin Wu, Weixiong Loo, Chong-Wah Ngo Jan 2023

Reinforcement Learning Enhanced Pichunter For Interactive Search, Zhixin Ma, Jiaxin Wu, Weixiong Loo, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

With the tremendous increase in video data size, search performance could be impacted significantly. Specifically, in an interactive system, a real-time system allows a user to browse, search and refine a query. Without a speedy system quickly, the main ingredient to engage a user to stay focused, an interactive system becomes less effective even with a sophisticated deep learning system. This paper addresses this challenge by leveraging approximate search, Bayesian inference, and reinforcement learning. For approximate search, we apply a hierarchical navigable small world, which is an efficient approximate nearest neighbor search algorithm. To quickly prune the search scope, we …

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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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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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Crowdfa: A Privacy-Preserving Mobile Crowdsensing Paradigm Via Federated Analytics, Bowen Zhao, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Yingjiu Li, Robert H. Deng Jan 2023

Crowdfa: A Privacy-Preserving Mobile Crowdsensing Paradigm Via Federated Analytics, Bowen Zhao, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Yingjiu Li, Robert H. Deng

Research Collection School Of Computing and Information Systems

Mobile crowdsensing (MCS) systems typically struggle to address the challenge of data aggregation, incentive design, and privacy protection, simultaneously. However, existing solutions usually focus on one or, at most, two of these issues. To this end, this paper presents CROWD FA, a novel paradigm for privacy-preserving MCS through federated analytics (FA), which aims to achieve a well-rounded solution encompassing data aggregation, incentive design, and privacy protection. Specifically, inspired by FA, CROWD FA initiates an MCS computing paradigm that enables data aggregation and incentive design. Participants can perform aggregation operations on their local data, facilitated by CROWD FA, which supports various …

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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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Intelligent Adaptive Gossip-Based Broadcast Protocol For Uav-Mec Using Multi-Agent Deep Reinforcement Learning, Zen Ren, Xinghua Li, Yinbin Miao, Zhuowen Li, Zihao Wang, Mengyao Zhu, Ximeng Liu, Deng, Robert H. Jan 2023

Intelligent Adaptive Gossip-Based Broadcast Protocol For Uav-Mec Using Multi-Agent Deep Reinforcement Learning, Zen Ren, Xinghua Li, Yinbin Miao, Zhuowen Li, Zihao Wang, Mengyao Zhu, Ximeng Liu, Deng, Robert H.

Research Collection School Of Computing and Information Systems

UAV-assisted mobile edge computing (UAV-MEC) has been proposed to offer computing resources for smart devices and user equipment. UAV cluster aided MEC rather than one UAV-aided MEC as edge pool is the newest edge computing architecture. Unfortunately, the data packet exchange during edge computing within the UAV cluster hasn't received enough attention. UAVs need to collaborate for the wide implementation of MEC, relying on the gossip-based broadcast protocol. However, gossip has the problem of long propagation delay, where the forwarding probability and neighbors are two factors that are difficult to balance. The existing works improve gossip from only one factor, …

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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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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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T-Counter: Trustworthy And Efficient Cpu Resource Measurement Using Sgx In The Cloud, Chuntao Dong, Qingni Shen, Xuhua Ding, Daoqing Yu, Wu Luo, Pengfei Wu, Zhonghai Wu Jan 2023

T-Counter: Trustworthy And Efficient Cpu Resource Measurement Using Sgx In The Cloud, Chuntao Dong, Qingni Shen, Xuhua Ding, Daoqing Yu, Wu Luo, Pengfei Wu, Zhonghai Wu

Research Collection School Of Computing and Information Systems

As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this paper, …

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Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan Jan 2023

Quantifying Stranded Assets Of The Coal-Fired Power In China Under The Paris Agreement Target, Weirong Zhang, Yiou Zhou, Zhen Gong, Junjie Kang, Changhong Zhao, Zhixu Meng, Jian Zhang, Tao Zhang, Jiahai Yuan

Research Collection School Of Computing and Information Systems

Coal-fired power plays a critical role in China's compliance with the Paris Agreement. This research quantifies China's stranded coal assets under different coal capacity expansion scenarios with an integrated approach and high-precision coal-fired power database. From a top-down perspective, firstly, the pathway of China's coal-fired power capacity consistent with the global 2 degrees C scenario is outlined and then those stranded coal-fired power plants are identified with a bottom-up perspective. Stranded value is estimated based upon a cash flow algorithm. Results show that if coal capacity stabilizes during 2020-2030, China will only incur a sizeable yet manageable stranded asset loss …

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Neighborhood Retail Amenities And Taxi Trip Behavior: A Natural Experiment In Singapore, Kwan Ok Lee, Shih-Fen Cheng Jan 2023

Neighborhood Retail Amenities And Taxi Trip Behavior: A Natural Experiment In Singapore, Kwan Ok Lee, Shih-Fen Cheng

Research Collection School Of Computing and Information Systems

While a small change in land use planning in existing neighborhoods may significantly reduce private vehicle trips, we do not have a great understanding of the magnitude of the project- and shock-based causal change in travel behaviors, especially for the retail purpose. We analyze the impact of newly developed malls on the retail trip behavior of nearby residents for shopping, dining or services. Using the difference-in-differences approach and big data from a major taxi company in Singapore, we find that households residing within 800 m from a new mall are significantly less likely to take taxis to other retail destinations …

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Efficient Approximate Range Aggregation Over Large-Scale Spatial Data Federation, Yexuan Shi, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Bolin Ding, Lei Chen Jan 2023

Efficient Approximate Range Aggregation Over Large-Scale Spatial Data Federation, Yexuan Shi, Yongxin Tong, Yuxiang Zeng, Zimu Zhou, Bolin Ding, Lei Chen

Research Collection School Of Computing and Information Systems

Range aggregation is a primitive operation in spatial data applications and there is a growing demand to support such operations over a data federation, where the entire spatial data are separately held by multiple data providers (a.k.a., data silos). Data federations notably increase the amount of data available for data-intensive applications such as smart mobility planning and public health emergency responses. Yet they also challenge the conventional implementation of range aggregation queries because the raw data cannot be shared within the federation and the data partition at each data silo is fixed during query processing. These constraints limit the design …

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Causal Interventional Training For Image Recognition, Wei Qin, Hanwang Zhang, Richang Hong, Ee-Peng Lim, Qianru Sun Jan 2023

Causal Interventional Training For Image Recognition, Wei Qin, Hanwang Zhang, Richang Hong, Ee-Peng Lim, Qianru Sun

Research Collection School Of Computing and Information Systems

Deep learning models often fit undesired dataset bias in training. In this paper, we formulate the bias using causal inference, which helps us uncover the ever-elusive causalities among the key factors in training, and thus pursue the desired causal effect without the bias. We start from revisiting the process of building a visual recognition system, and then propose a structural causal model (SCM) for the key variables involved in dataset collection and recognition model: object, common sense, bias, context, and label prediction. Based on the SCM, one can observe that there are “good” and “bad” biases. Intuitively, in the image …

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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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