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Articles 1261 - 1290 of 7453

Full-Text Articles in Physical Sciences and Mathematics

Prompt For Extraction? Paie: Prompting Argument Interaction For Event Argument Extraction, Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, Jing Shao May 2022

Prompt For Extraction? Paie: Prompting Argument Interaction For Event Argument Extraction, Yubo Ma, Zehao Wang, Yixin Cao, Mukai Li, Meiqi Chen, Kun Wang, Jing Shao

Research Collection School Of Computing and Information Systems

In this paper, we propose an effective yet efficient model PAIE for both sentence-level and document-level Event Argument Extraction (EAE), which also generalizes well when there is a lack of training data. On the one hand, PAIE utilizes prompt tuning for extractive objectives to take the best advantages of Pre-trained Language Models (PLMs). It introduces two span selectors based on the prompt to select start/end tokens among input texts for each role. On the other hand, it captures argument interactions via multi-role prompts and conducts joint optimization with optimal span assignments via a bipartite matching loss. Also, with a flexible …

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Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract), Zemin Liu, Yuan Fang, Yong Liu, Vincent W. Zheng May 2022

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

Research Collection School Of Computing and Information Systems

While graph neural networks (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 the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, …

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Static Inference Meets Deep Learning: A Hybrid Type Inference Approach For Python, Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, Michael R. Lyu May 2022

Static Inference Meets Deep Learning: A Hybrid Type Inference Approach For Python, Yun Peng, Cuiyun Gao, Zongjie Li, Bowei Gao, David Lo, Qirun Zhang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Type inference for dynamic programming languages such as Python is an important yet challenging task. Static type inference techniques can precisely infer variables with enough static constraints but are unable to handle variables with dynamic features. Deep learning (DL) based approaches are feature-agnostic, but they cannot guarantee the correctness of the predicted types. Their performance significantly depends on the quality of the training data (i.e., DL models perform poorly on some common types that rarely appear in the training dataset). It is interesting to note that the static and DL-based approaches offer complementary benefits. Unfortunately, to our knowledge, precise type …

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Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo May 2022

Automated Identification Of Libraries From Vulnerability Data: Can We Do Better?, Stefanus A. Haryono, Hong Jin Kang, Abhishek Sharma, Asankhaya Sharma, Andrew E. Santosa, Ming Yi Ang, David Lo

Research Collection School Of Computing and Information Systems

Software engineers depend heavily on software libraries and have to update their dependencies once vulnerabilities are found in them. Software Composition Analysis (SCA) helps developers identify vulnerable libraries used by an application. A key challenge is the identification of libraries related to a given reported vulnerability in the National Vulnerability Database (NVD), which may not explicitly indicate the affected libraries. Recently, researchers have tried to address the problem of identifying the libraries from an NVD report by treating it as an extreme multi-label learning (XML) problem, characterized by its large number of possible labels and severe data sparsity. As input, …

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Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Wu Aoyu, Huan Wei, Huamin Qu May 2022

Structure-Aware Visualization Retrieval, Haotian Li, Yong Wang, Wu Aoyu, Huan Wei, Huamin Qu

Research Collection School Of Computing and Information Systems

With the wide usage of data visualizations, a huge number of Scalable Vector Graphic (SVG)-based visualizations have been created and shared online. Accordingly, there has been an increasing interest in exploring how to retrieve perceptually similar visualizations from a large corpus, since it can beneft various downstream applications such as visualization recommendation. Existing methods mainly focus on the visual appearance of visualizations by regarding them as bitmap images. However, the structural information intrinsically existing in SVG-based visualizations is ignored. Such structural information can delineate the spatial and hierarchical relationship among visual elements, and characterize visualizations thoroughly from a new perspective. …

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Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi May 2022

Causality-Based Neural Network Repair, Bing Sun, Jun Sun, Long H. Pham, Jie Shi

Research Collection School Of Computing and Information Systems

Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose …

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Does This Apply To Me? An Empirical Study Of Technical Context In Stack Overflow, Akalanka Galappaththi, Sarah Nadi, Christoph Treude May 2022

Does This Apply To Me? An Empirical Study Of Technical Context In Stack Overflow, Akalanka Galappaththi, Sarah Nadi, Christoph Treude

Research Collection School Of Computing and Information Systems

Stack Overflow has become an essential technical resource for developers. However, given the vast amount of knowledge available on Stack Overflow, finding the right information that is relevant for a given task is still challenging, especially when a developer is looking for a solution that applies to their specific requirements or technology stack. Clearly marking answers with their technical context, i.e., the information that characterizes the technologies and assumptions needed for this answer, is potentially one way to improve navigation. However, there is no information about how often such context is mentioned, and what kind of information it might offer. …

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Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo May 2022

Benchmarking Library Recognition In Tweets, Ting Zhang, Divya Prabha Chandrasekaran, Ferdian Thung, David Lo

Research Collection School Of Computing and Information Systems

Software developers often use social media (such as Twitter) to shareprogramming knowledge such as new tools, sample code snippets,and tips on programming. One of the topics they talk about is thesoftware library. The tweets may contain useful information abouta library. A good understanding of this information, e.g., on thedeveloper’s views regarding a library can be beneficial to weigh thepros and cons of using the library as well as the general sentimentstowards the library. However, it is not trivial to recognize whethera word actually refers to a library or other meanings. For example,a tweet mentioning the word “pandas" may refer to …

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On The Effectiveness Of Pretrained Models For Api Learning, Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Thung Ferdian, Gia Kien Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo May 2022

On The Effectiveness Of Pretrained Models For Api Learning, Mohammad Abdul Hadi, Imam Nur Bani Yusuf, Thung Ferdian, Gia Kien Luong, Lingxiao Jiang, Fatemeh H. Fard, David Lo

Research Collection School Of Computing and Information Systems

Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural language queries for building applications in a faster and cleaner manner. Existing approaches utilize information retrieval models to search for matching API sequences given a query or use RNN-based encoder-decoder to generate API sequences. As it stands, the first approach treats queries and API names as bags of words. It lacks deep comprehension of the semantics of the queries. The latter approach adapts a neural …

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Data Pricing In Machine Learning Pipelines, Zicun Cong, Xuan Luo, Jian Pei, Feida Zhu, Yong Zhang May 2022

Data Pricing In Machine Learning Pipelines, Zicun Cong, Xuan Luo, Jian Pei, Feida Zhu, Yong Zhang

Research Collection School Of Computing and Information Systems

Machine learning is disruptive. At the same time, machine learning can only succeed by collaboration among many parties in multiple steps naturally as pipelines in an eco-system, such as collecting data for possible machine learning applications, collaboratively training models by multiple parties and delivering machine learning services to end users. Data are critical and penetrating in the whole machine learning pipelines. As machine learning pipelines involve many parties and, in order to be successful, have to form a constructive and dynamic eco-system, marketplaces and data pricing are fundamental in connecting and facilitating those many parties. In this article, we survey …

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Efficient Encrypted Data Search With Expressive Queries And Flexible Update, Jianting Ning, Jiageng Chen, Kaitai Liang, Joseph K. Liu, Chunhua Su, Qianhong Wu May 2022

Efficient Encrypted Data Search With Expressive Queries And Flexible Update, Jianting Ning, Jiageng Chen, Kaitai Liang, Joseph K. Liu, Chunhua Su, Qianhong Wu

Research Collection School Of Computing and Information Systems

Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Liang et al. proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency, O(nd), where d is …

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Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li May 2022

Competition And Third-Party Platform-Integration In Ride-Sourcing Markets, Yaqian Zhou, Hai Yang, Jintao Ke, Hai Wang, Xinwei Li

Research Collection School Of Computing and Information Systems

Recently, some third-party integrators attempt to integrate the ride services offered by multiple independent ride-sourcing platforms. Accordingly, passengers can request ride through the integrators and receive ride service from any one of the ride-sourcing platforms. This novel business model, termed as third-party platform-integration in this work, has potentials to alleviate market fragmentation cost resulting from demand splitting among multiple platforms. Although most existing studies focus on operation strategies for one single monopolist platform, much less is known about the competition and platform-integration and their implications on operation strategy and system efficiency. In this work, we propose mathematical models to describe …

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Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan May 2022

Managing The Phaseout Of Coal Power: A Comparison Of Power Decarbonization Pathways In Jilin Province, Weirong Zhang, Zhixu Meng, Jiongjun Yang, Yan Song, Yiou Zhou, Changhong Zhao, Jiahai Yuan

Research Collection School Of Computing and Information Systems

With the periodic goals of reaching carbon emission peak before 2030 and achieving carbon neutrality before 2060 (“dual carbon” goals), China shows its unprecedented determination to coal power phaseout. This research takes Jilin Province to showcase possible pathways of coal power units’ phaseout on provincial level. We set up four different coal power phaseout scenarios, under which their transition cost and effectiveness would be calculated, respectively. In terms of natural resource endowment and electricity demand, Jilin Province would achieve a complete coal power phaseout by 2045 or even by 2040. However, after assessing the effectiveness of power transition under the …

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Itss: Interactive Web-Based Authoring And Playback Integrated Environment For Programming Tutorials, Eng Lieh Ouh, Benjamin Gan, David Lo May 2022

Itss: Interactive Web-Based Authoring And Playback Integrated Environment For Programming Tutorials, Eng Lieh Ouh, Benjamin Gan, David Lo

Research Collection School Of Computing and Information Systems

Video-based programming tutorials are a popular form of tutorial used by authors to guide learners to code. Still, the interactivity of these videos is limited primarily to control video flow. There are existing works with increased interactivity that are shown to improve the learning experience. Still, these solutions require setting up a custom recording environment and are not well-integrated with the playback environment. This paper describes our integrated ITSS environment and evaluates the ease of authoring and playback of our interactive programming tutorials. Our environment is designed to run within the browser sandbox and is less intrusive to record interactivity …

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Adaptive Task Planning For Large-Scale Robotized Warehouses, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Ke Xu, Wenzhe Tan, Hongbo Li May 2022

Adaptive Task Planning For Large-Scale Robotized Warehouses, Dingyuan Shi, Yongxin Tong, Zimu Zhou, Ke Xu, Wenzhe Tan, Hongbo Li

Research Collection School Of Computing and Information Systems

Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire …

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Understanding Crowdsourcing Requesters’ Wage Setting Behaviors, Kotaro Hara, Yudai Tanaka May 2022

Understanding Crowdsourcing Requesters’ Wage Setting Behaviors, Kotaro Hara, Yudai Tanaka

Research Collection School Of Computing and Information Systems

Requesters on crowdsourcing platforms like Amazon Mechanical Turk (AMT) compensate workers inadequately. One potential reason for the underpayment is that the AMT’s requester interface provides limited information about estimated wages, preventing requesters from knowing if they are offering a fair piece-rate reward. To assess if presenting wage information affects requesters’ reward setting behaviors, we conducted a controlled study with 63 participants. We had three levels for a between-subjects factor in a mixed design study, where we provided participants with: no wage information, wage point estimate, and wage distribution. Each participant had three stages of adjusting the reward and controlling the …

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Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi May 2022

Exploring And Adapting Chinese Gpt To Pinyin Input Method, Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

Research Collection School Of Computing and Information Systems

While GPT has become the de-facto method for text generation tasks, its application to pinyin input method remains unexplored. In this work, we make the first exploration to leverage Chinese GPT for pinyin input method. We find that a frozen GPT achieves state-of-the-art performance on perfect pinyin. However, the performance drops dramatically when the input includes abbreviated pinyin. A reason is that an abbreviated pinyin can be mapped to many perfect pinyin, which links to even larger number of Chinese characters. We mitigate this issue with two strategies, including enriching the context with pinyin and optimizing the training process to …

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Devops Education: An Interview Study Of Challenges And Recommendations, Marcelo Fernandes, Samuel Ferino, Anny K. Fernandes, Uirá Kulesza, Eduardo Aranha, Christoph Treude May 2022

Devops Education: An Interview Study Of Challenges And Recommendations, Marcelo Fernandes, Samuel Ferino, Anny K. Fernandes, Uirá Kulesza, Eduardo Aranha, Christoph Treude

Research Collection School Of Computing and Information Systems

Over the last years, the software industry has adopted several DevOps technologies related to practices such as continuous integration and continuous delivery. The high demand for DevOps practitioners requires non-trivial adjustments in traditional software engineering courses and educational methodologies. This work presents an interview study with 14 DevOps educators from different universities and countries, aiming to identify the main challenges and recommendations for DevOps teaching. Our study identified 83 challenges, 185 recommendations, and several association links and conflicts between them. Our findings can help educators plan, execute and evaluate DevOps courses. They also highlight several opportunities for researchers to propose …

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A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin Apr 2022

A Survey On Modern Deep Neural Network For Traffic Prediction: Trends, Methods And Challenges, David Alexander Tedjopumomo, Zhifeng Bao, Baihua Zheng, Farhana Murtaza Choudhury, Kai Qin

Research Collection School Of Computing and Information Systems

In this modern era, traffic congestion has become a major source of negative economic and environmental impact for urban areas worldwide. One of the most efficient ways to mitigate traffic congestion is through future traffic prediction. The field of traffic prediction has evolved greatly ever since its inception in the late 70s. Earlier studies mainly use classical statistical models such as ARIMA and its variants. Then, researchers started to focus on machine learning models due to their power and flexibility. As theoretical and technological advances emerge, we enter the era of deep neural network, which gained popularity due to its …

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Cost: Contrastive Learning Of Disentangled Seasonal-Trend Representations For Time Series Forecasting, Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi Apr 2022

Cost: Contrastive Learning Of Disentangled Seasonal-Trend Representations For Time Series Forecasting, Gerald Woo, Chenghao Liu, Doyen Sahoo, Akshat Kumar, Steven Hoi

Research Collection School Of Computing and Information Systems

Deep learning has been actively studied for time series forecasting, and the mainstream paradigm is based on the end-to-end training of neural network architectures, ranging from classical LSTM/RNNs to more recent TCNs and Transformers. Motivated by the recent success of representation learning in computer vision and natural language processing, we argue that a more promising paradigm for time series forecasting, is to first learn disentangled feature representations, followed by a simple regression fine-tuning step – we justify such a paradigm from a causal perspective. Following this principle, we propose a new time series representation learning framework for long sequence time …

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Rescuecastr: Exploring Photos And Live Streaming To Support Contextual Awareness In The Wilderness Search And Rescue Command Post, Brennon Jones, Anthony Tang, Carman Neustaedter Apr 2022

Rescuecastr: Exploring Photos And Live Streaming To Support Contextual Awareness In The Wilderness Search And Rescue Command Post, Brennon Jones, Anthony Tang, Carman Neustaedter

Research Collection School Of Computing and Information Systems

Wilderness search and rescue (WSAR) is a command-and-control activity where a Command team manages field teams scattered across a large area looking for a lost person. The challenge is that it can be difficult for Command to maintain awareness of field teams and the conditions of the field. We designed RescueCASTR, an interface that explores the idea of deploying field teams with wearable cameras that stream live video or sequential photos periodically to Command that aid contextual awareness. We ran a remote user study with WSAR managers to understand the opportunities and challenges of such a system. We found that …

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Asteroids: Exploring Swarms Of Mini-Telepresence Robots For Physical Skill Demonstration, Jiannan Li, Maurício Sousa, Chu Li, Jessie Liu, Yan Chen, Ravin Balakrishnan, Tovi Grossman Apr 2022

Asteroids: Exploring Swarms Of Mini-Telepresence Robots For Physical Skill Demonstration, Jiannan Li, Maurício Sousa, Chu Li, Jessie Liu, Yan Chen, Ravin Balakrishnan, Tovi Grossman

Research Collection School Of Computing and Information Systems

Online synchronous tutoring allows for immediate engagement between instructors and audiences over distance. However, tutoring physical skills remains challenging because current telepresence approaches may not allow for adequate spatial awareness, viewpoint control of the demonstration activities scattered across an entire work area, and the instructor’s sufficient awareness of the audience. We present Asteroids, a novel approach for tangible robotic telepresence, to enable workbench-scale physical embodiments of remote people and tangible interactions by the instructor. With Asteroids, the audience can actively control a swarm of mini-telepresence robots, change camera positions, and switch to other robots’ viewpoints. Demonstrators can perceive the audiences’ …

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Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang Apr 2022

Learning Scenario Representation For Solving Two-Stage Stochastic Integer Programs, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang

Research Collection School Of Computing and Information Systems

Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity. To solve two-stage SIPs efficiently, we propose a conditional variational autoencoder (CVAE) based method to learn scenario representation for a class of SIP instances. Specifically, we design a graph convolutional network based encoder to embed each scenario with the deterministic part of its instance (i.e. context) into a low-dimensional latent space, from which a decoder reconstructs the scenario from its latent representation conditioned on the context. Such a design effectively captures the dependencies of …

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On Explaining Multimodal Hateful Meme Detection Models, Ming Shan Hee, Roy Ka-Wei Lee, Wen Haw Chong Apr 2022

On Explaining Multimodal Hateful Meme Detection Models, Ming Shan Hee, Roy Ka-Wei Lee, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful meme detection is a new multimodal task that has gained significant traction in academic and industry research communities. Recently, researchers have applied pre-trained visual-linguistic models to perform the multimodal classification task, and some of these solutions have yielded promising results. However, what these visual-linguistic models learn for the hateful meme classification task remains unclear. For instance, it is unclear if these models are able to capture the derogatory or slurs references in multimodality (i.e., image and text) of the hateful memes. To fill this research gap, this paper propose three research questions to improve our understanding of these visual-linguistic …

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Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen Apr 2022

Learning For Amalgamation: A Multi-Source Transfer Learning Framework For Sentiment Classification, Cuong V. Nguyen, Khiem H. Le, Hong Quang Pham, Quang H. Pham, Binh T. Nguyen

Research Collection School Of Computing and Information Systems

Transfer learning plays an essential role in Deep Learning, which can remarkably improve the performance of the target domain, whose training data is not sufficient. Our work explores beyond the common practice of transfer learning with a single pre-trained model. We focus on the task of Vietnamese sentiment classification and propose LIFA, a framework to learn a unified embedding from several pre-trained models. We further propose two more LIFA variants that encourage the pre-trained models to either cooperate or compete with one another. Studying these variants sheds light on the success of LIFA by showing that sharing knowledge among the …

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Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan Apr 2022

Estimating Stranded Coal Assets In China's Power Sector, Weirong Zhang, Mengjia Ren, Junjie Kang, Yiou Zhou, Jiahai Yuan

Research Collection School Of Computing and Information Systems

China has suffered overcapacity in coal power since 2016. With growing electricity demand and an economic crisis due to the Covid-19 pandemic, China faces a dilemma between easing restrictive policies for short-term growth in coal-fired power production and keeping restrictions in place for long-term sustainability. In this paper, we measure the risks faced by China's coal power units to become stranded in the next decade and estimate the associated economic costs for different shareholders. By implementing restrictive policies on coal power expansion, China can avoid 90% of stranded coal assets by 2025.

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Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa Apr 2022

Fine-Grained Detection Of Academic Emotions With Spatial Temporal Graph Attention Networks Using Facial Landmarks, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

With the incidence of the Covid-19 pandemic, institutions have adopted online learning as the main lessondelivery channel. A common criticism of online learning is that sensing of learners’ affective states such asengagement is lacking which degrades the quality of teaching. In this study, we propose automatic sensing of learners’ affective states in an online setting with web cameras capturing their facial landmarks and head poses. We postulate that the sparsely connected facial landmarks can be modelled using a Graph Neural Network. Using the publicly available in the wild DAiSEE dataset, we modelled both the spatial and temporal dimensions of the …

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Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li Apr 2022

Pre-Training Graph Neural Networks For Link Prediction In Biomedical Networks, Yahui Long, Min Wu, Yong Liu, Yuan Fang, Chee Kong Kwoh, Jiawei Luo, Xiaoli Li

Research Collection School Of Computing and Information Systems

Motivation: Graphs or networks are widely utilized to model the interactions between different entities (e.g., proteins, drugs, etc) for biomedical applications. Predicting potential links in biomedical networks is important for understanding the pathological mechanisms of various complex human diseases, as well as screening compound targets for drug discovery. Graph neural networks (GNNs) have been designed for link prediction in various biomedical networks, which rely on the node features extracted from different data sources, e.g., sequence, structure and network data. However, it is challenging to effectively integrate these data sources and automatically extract features for different link prediction tasks. Results: In …

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Algorithm Selection For The Team Orienteering Problem, Mustafa Misir, Aldy Gunawan, Pieter Vansteenwegen Apr 2022

Algorithm Selection For The Team Orienteering Problem, Mustafa Misir, Aldy Gunawan, Pieter Vansteenwegen

Research Collection School Of Computing and Information Systems

This work utilizes Algorithm Selection for solving the Team Orienteering Problem (TOP). The TOP is an NP-hard combinatorial optimization problem in the routing domain. This problem has been modelled with various extensions to address different real-world problems like tourist trip planning. The complexity of the problem motivated to devise new algorithms. However, none of the existing algorithms came with the best performance across all the widely used benchmark instances. This fact suggests that there is a performance gap to fill. This gap can be targeted by developing more new algorithms as attempted by many researchers before. An alternative strategy is …

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Data Source Selection In Federated Learning: A Submodular Optimization Approach, Ruisheng Zhang, Yansheng Wang, Zimu Zhou, Ziyao Ren, Yongxin Tong, Ke Xu Apr 2022

Data Source Selection In Federated Learning: A Submodular Optimization Approach, Ruisheng Zhang, Yansheng Wang, Zimu Zhou, Ziyao Ren, Yongxin Tong, Ke Xu

Research Collection School Of Computing and Information Systems

Federated learning is a new learning paradigm that jointly trains a model from multiple data sources without sharing raw data. For the practical deployment of federated learning, data source selection is compulsory due to the limited communication cost and budget in real-world applications. The necessity of data source selection is further amplified in presence of data heterogeneity among clients. Prior solutions are either low in efficiency with exponential time cost or lack theoretical guarantees. Inspired by the diminishing marginal accuracy phenomenon in federated learning, we study the problem from the perspective of submodular optimization. In this paper, we aim at …

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