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Full-Text Articles in Computer Sciences

Light Structure From Pin Motion: Geometric Point Light Source Calibration, Hiroaki Santo, Michael Waechter, Wen-Yan Lin, Yusuke Sugano, Yasuyuki Matsushita Mar 2020

Light Structure From Pin Motion: Geometric Point Light Source Calibration, Hiroaki Santo, Michael Waechter, Wen-Yan Lin, Yusuke Sugano, Yasuyuki Matsushita

Research Collection School Of Computing and Information Systems

We present a method for geometric point light source calibration. Unlike prior works that use Lambertian spheres, mirror spheres, or mirror planes, we use a calibration target consisting of a plane and small shadow casters at unknown positions above the plane. We show that shadow observations from a moving calibration target under a fixed light follow the principles of pinhole camera geometry and epipolar geometry, allowing joint recovery of the light position and 3D shadow caster positions, equivalent to how conventional structure from motion jointly recovers camera parameters and 3D feature positions from observed 2D features. Moreover, we devised a …

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Heartquake: Accurate Low-Cost Non-Invasive Ecg Monitoring Using Bed-Mounted Geophones, Jaeyeon Park, Hyeon Cho, Rajesh Krishna Balan, Jeonggil Ko Mar 2020

Heartquake: Accurate Low-Cost Non-Invasive Ecg Monitoring Using Bed-Mounted Geophones, Jaeyeon Park, Hyeon Cho, Rajesh Krishna Balan, Jeonggil Ko

Research Collection School Of Computing and Information Systems

This work presents HeartQuake, a low cost, accurate, non-intrusive, geophone-based sensing system for extracting accurate electrocardiogram (ECG) patterns using heartbeat vibrations that penetrate through a bed mattress. In HeartQuake, cardiac activity-originated vibration patterns are captured on a geophone and sent to a server, where the data is filtered to remove the sensor's internal noise and passed on to a bidirectional long short term memory (Bi-LSTM) deep learning model for ECG waveform estimation. To the best of our knowledge, this is the first solution that can non-intrusively provide accurate ECG waveform characteristics instead of more basic abstract features such as the …

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Vehicle Routing Problem For Multi-Product Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Benjamin Gan, Vincent F. Yu, Panca Jodiawan Mar 2020

Vehicle Routing Problem For Multi-Product Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Benjamin Gan, Vincent F. Yu, Panca Jodiawan

Research Collection School Of Computing and Information Systems

Cross-docking is a logistic technique that can reduce costs occurred in a supply chain network while increasing the flow of goods, thus shortening the shipping cycle. Inside a cross-dock facility, the goods are directly transferred from incoming vehicles to outgoing vehicles without storing them in-between. Our research extends and combines this cross-docking technique with a well-known logistic problem, the vehicle routing problem (VRP), for delivering multiple products and addresses it as the VRP for multi-product cross-docking (VRP-MPCD). We developed a mixed integer programming model and generated two sets of VRP-MPCD instances, which are based on VRPCD instances. The instances are …

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The Search For Optimal Oxygen Saturation Targets In Critically Ill: Patients Observational Data From Large Icu Databases, Willem Van Den Boom, Michael Hoy, Jagadish Sankaran, Mengru Liu, Haroun Chahed, Mengling Feng, Kay Choong See Mar 2020

The Search For Optimal Oxygen Saturation Targets In Critically Ill: Patients Observational Data From Large Icu Databases, Willem Van Den Boom, Michael Hoy, Jagadish Sankaran, Mengru Liu, Haroun Chahed, Mengling Feng, Kay Choong See

Research Collection School Of Computing and Information Systems

Background: Although low oxygen saturations are generally regarded as deleterious, recent studies in ICU patients have shown that a liberal oxygen strategy increases mortality. However, the optimal oxygen saturation target remains unclear. The goal of this study was to determine the optimal range by using real-world data. Methods: Replicate retrospective analyses were conducted of two electronic medical record databases: the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care III database (MIMIC). Only patients with at least 48 h of oxygen therapy were included. Nonlinear regression was used to analyze the association between median pulse oximetry-derived …

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Predicting Student Performance In Interactive Online Question Pools Using Mouse Interaction Features, Huan Wei, Haotian Li, Meng Xia, Yong Wang, Huamin Qu Mar 2020

Predicting Student Performance In Interactive Online Question Pools Using Mouse Interaction Features, Huan Wei, Haotian Li, Meng Xia, Yong Wang, Huamin Qu

Research Collection School Of Computing and Information Systems

Modeling student learning and further predicting the performance is a well-established task in online learning and is crucial to personalized education by recommending different learning resources to different students based on their needs. Interactive online question pools (e.g., educational game platforms), an important component of online education, have become increasingly popular in recent years. However, most existing work on student performance prediction targets at online learning platforms with a well-structured curriculum, predefined question order and accurate knowledge tags provided by domain experts. It remains unclear how to conduct student performance prediction in interactive online question pools without such well-organized question …

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Singapore’S National Ai Strategy, Singapore Management University Feb 2020

Singapore’S National Ai Strategy, Singapore Management University

Perspectives@SMU

The island state is banking on industry-wide projects and building an AI ecosystem to transform its economy

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Interpretable Rumor Detection In Microblogs By Attending To User Interactions, Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang Feb 2020

Interpretable Rumor Detection In Microblogs By Attending To User Interactions, Ling Min Serena Khoo, Hai Leong Chieu, Zhong Qian, Jing Jiang

Research Collection School Of Computing and Information Systems

We address rumor detection by learning to differentiate between the community’s response to real and fake claims in microblogs. Existing state-of-the-art models are based on tree models that model conversational trees. However, in social media, a user posting a reply might be replying to the entire thread rather than to a specific user. We propose a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network. We investigated variants of this model: (1) a structure aware self-attention model (StA-PLAN) that incorporates tree structure information in the transformer network, and (2) …

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Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu Feb 2020

Multi-Level Head-Wise Match And Aggregation In Transformer For Textual Sequence Matching, Shuohang Wang, Yunshi Lan, Yi Tay, Jing Jiang, Jingjing Liu

Research Collection School Of Computing and Information Systems

Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vectorrepresentation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary

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Distinguishing Similar Design Pattern Instances Through Temporal Behavior Analysis, Renhao Xiong, David Lo, Bixin Li Feb 2020

Distinguishing Similar Design Pattern Instances Through Temporal Behavior Analysis, Renhao Xiong, David Lo, Bixin Li

Research Collection School Of Computing and Information Systems

Design patterns (DPs) encapsulate valuable design knowledge of object-oriented systems. Detecting DP instances helps to reveal the underlying rationale, thus facilitates the maintenance of legacy code. Resulting from the internal similarity of DPs, implementation variants, and missing roles, approaches based on static analysis are unable to well identify structurally similar instances. Existing approaches further employ dynamic techniques to test the runtime behaviors of candidate instances. Automatically verifying the runtime behaviors of DP instances is a challenging task in multiple aspects. This paper presents an approach to improve the verification process of existing approaches. To exercise the runtime behaviors of DP …

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Are The Code Snippets What We Are Searching For? A Benchmark And An Empirical Study On Code Search With Natural-Language Queries, Shuhan Yan, Hang Yu, Yuting Chen, Beijun Shen Feb 2020

Are The Code Snippets What We Are Searching For? A Benchmark And An Empirical Study On Code Search With Natural-Language Queries, Shuhan Yan, Hang Yu, Yuting Chen, Beijun Shen

Research Collection School Of Computing and Information Systems

Code search methods, especially those that allow programmers to raise queries in a natural language, plays an important role in software development. It helps to improve programmers' productivity by returning sample code snippets from the Internet and/or source-code repositories for their natural-language queries. Meanwhile, there are many code search methods in the literature that support natural-language queries. Difficulties exist in recognizing the strengths and weaknesses of each method and choosing the right one for different usage scenarios, because (1) the implementations of those methods and the datasets for evaluating them are usually not publicly available, and (2) some methods leverage …

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Saga: Efficient And Large-Scale Detection Of Near-Miss Clones With Gpu Acceleration, Guanhua Li, Yijian Wu, Chanchal K. Roy, Jun Sun, Xin Peng, Nanjie Zhan, Bin Hu, Jingyi Ma Feb 2020

Saga: Efficient And Large-Scale Detection Of Near-Miss Clones With Gpu Acceleration, Guanhua Li, Yijian Wu, Chanchal K. Roy, Jun Sun, Xin Peng, Nanjie Zhan, Bin Hu, Jingyi Ma

Research Collection School Of Computing and Information Systems

Clone detection on large code repository is necessary for many big code analysis tasks. The goal is to provide rich information on identical and similar code across projects. Detecting near-miss code clones on big code is challenging since it requires intensive computing and memory resources as the scale of the source code increases. In this work, we propose SAGA, an efficient suffix-array based code clone detection tool designed with sophisticated GPU optimization. SAGA not only detects Type-l and Type-2 clones but also does so for cross-project large repositories and for the most computationally expensive Type-3 clones. Meanwhile, it also works …

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Neural Approximate Dynamic Programming For On-Demand Ride-Pooling, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham Feb 2020

Neural Approximate Dynamic Programming For On-Demand Ride-Pooling, Sanket Shah, Meghna Lowalekar, Pradeep Varakantham

Research Collection School Of Computing and Information Systems

On-demand ride-pooling (e.g., UberPool, LyftLine, GrabShare) has recently become popular because of its ability to lower costs for passengers while simultaneously increasing revenue for drivers and aggregation companies (e.g., Uber). Unlike in Taxi on Demand (ToD) services – where a vehicle is assigned one passenger at a time – in on-demand ride-pooling, each vehicle must simultaneously serve multiple passengers with heterogeneous origin and destination pairs without violating any quality constraints. To ensure near real-time response, existing solutions to the real-time ride-pooling problem are myopic in that they optimise the objective (e.g., maximise the number of passengers served) for the current …

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Social Influence Does Matter: User Action Prediction For In-Feed Advertising., Hongyang Wang, Qingfei Meng, Ju Fan, Yuchen Li, Laizhong Cui, Xiaoman Zhao, Chong Peng, Gong Chen Chen, Xiaoyong Du Feb 2020

Social Influence Does Matter: User Action Prediction For In-Feed Advertising., Hongyang Wang, Qingfei Meng, Ju Fan, Yuchen Li, Laizhong Cui, Xiaoman Zhao, Chong Peng, Gong Chen Chen, Xiaoyong Du

Research Collection School Of Computing and Information Systems

Social in-feed advertising delivers ads that seamlessly fit insidea user’s feed, and allows users to engage in social actions(likes or comments) with the ads. Many businesses payhigher attention to “engagement marketing” that maximizessocial actions, as social actions can effectively promote brandawareness. This paper studies social action prediction for infeedadvertising. Most existing works overlook the social influenceas a user’s action may be affected by her friends’actions. This paper introduces an end-to-end approach thatleverages social influence for action prediction, and focuseson addressing the high sparsity challenge for in-feed ads. Wepropose to learn influence structure that models who tendsto be influenced. We extract …

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Deepbindiff: Learning Program-Wide Code Representations For Binary Diffing, Yue Duan, Xuezixiang Li, Jinghan Wang, Wang, Heng Yin Feb 2020

Deepbindiff: Learning Program-Wide Code Representations For Binary Diffing, Yue Duan, Xuezixiang Li, Jinghan Wang, Wang, Heng Yin

Research Collection School Of Computing and Information Systems

Binary diffing analysis quantitatively measures the differences between two given binaries and produces fine-grained basic block matching. It has been widely used to enable different kinds of critical security analysis. However, all existing program analysis and machine learning based techniques suffer from low accuracy, poor scalability, coarse granularity, or require extensive labeled training data to function. In this paper, we propose an unsupervised program-wide code representation learning technique to solve the problem. We rely on both the code semantic information and the program-wide control flow information to generate block embeddings. Furthermore, we propose a k-hop greedy matching algorithm to find …

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Example-Based Colourization Via Dense Encoding Pyramids, Chufeng Xiao, Chu Han, Zhuming Zhang, Jing Qin, Tien-Tsin Wong, Guoqiang Han, Shengfeng He Feb 2020

Example-Based Colourization Via Dense Encoding Pyramids, Chufeng Xiao, Chu Han, Zhuming Zhang, Jing Qin, Tien-Tsin Wong, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

We propose a novel deep example-based image colourization method called dense encoding pyramid network. In our study, we define the colourization as a multinomial classification problem. Given a greyscale image and a reference image, the proposed network leverages large-scale data and then predicts colours by analysing the colour distribution of the reference image. We design the network as a pyramid structure in order to exploit the inherent multi-scale, pyramidal hierarchy of colour representations. Between two adjacent levels, we propose a hierarchical decoder–encoder filter to pass the colour distributions from the lower level to higher level in order to take both …

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Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He Feb 2020

Gdface: Gated Deformation For Multi-View Face Image Synthesis, Xuemiao Xu, Keke Li, Cheng Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Photorealistic multi-view face synthesis from a single image is an important but challenging problem. Existing methods mainly learn a texture mapping model from the source face to the target face. However, they fail to consider the internal deformation caused by the change of poses, leading to the unsatisfactory synthesized results for large pose variations. In this paper, we propose a Gated Deformable Face Synthesis Network to model the deformation of faces that aids the synthesis of the target face image. Specifically, we propose a dual network that consists of two modules. The first module estimates the deformation of two views …

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Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua Feb 2020

Zero-Shot Ingredient Recognition By Multi-Relational Graph Convolutional Network, Jingjing Chen, Liangming Pan, Zhipeng Wei, Xiang Wang, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Recognizing ingredients for a given dish image is at the core of automatic dietary assessment, attracting increasing attention from both industry and academia. Nevertheless, the task is challenging due to the difficulty of collecting and labeling sufficient training data. On one hand, there are hundred thousands of food ingredients in the world, ranging from the common to rare. Collecting training samples for all of the ingredient categories is difficult. On the other hand, as the ingredient appearances exhibit huge visual variance during the food preparation, it requires to collect the training samples under different cooking and cutting methods for robust …

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Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen Feb 2020

Five Challenges In Cloud-Enabled Intelligence And Control, Tarek Abdelzaher, Yifan Hao, Kasthuri Jayarajah, Archan Misra, Per Skarin, Shuochao Yao, Dulanga Kaveesha Weerakoon Weerakoon Mudiyanselage, Karl-Erik Arzen

Research Collection School Of Computing and Information Systems

The proliferation of connected embedded devices, or the Internet of Things (IoT), together with recent advances in machine intelligence, will change the profile of future cloud services and introduce a variety of new research problems, both in cloud applications and infrastructure layers. These problems are centered around empowering individually resource-limited devices to exhibit intelligent behavior, both in sensing and control, thanks to a judicious utilization of cloud resources. Cloud services will enable learning from data, performing inference, and executing control, all with assurances on outcomes. The paper discusses such emerging services and outlines five resulting new research directions towards enabling …

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Does Reputational Sanctions Deter Negligence In Information Security Management? A Field Quasi-Experiment, Qian Tang, Andrew B. Whinston Feb 2020

Does Reputational Sanctions Deter Negligence In Information Security Management? A Field Quasi-Experiment, Qian Tang, Andrew B. Whinston

Research Collection School Of Computing and Information Systems

Security negligence, a major cause of data breaches, occurs when an organization’s information technology management fails to adequately address security vulnerabilities. By conducting a field quasi-experiment using outgoing spam as a focal security issue, this study investigates the effectiveness of reputational sanctions in reducing security negligence in a global context. In the quasi-experiment, a reputational sanction mechanism based on outgoing spam was established for four countries, and for each country, reputational sanctions were imposed on the 10 organizations with the largest outgoing spam volumes—that is, these organizations were listed publicly. We find that because of our reputational sanction mechanism, organizations …

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Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh Feb 2020

Bounding Regret In Empirical Games, Steven Jecmen, Arunesh Sinha, Zun Li, Long Tran-Thanh

Research Collection School Of Computing and Information Systems

Empirical game-theoretic analysis refers to a set of models and techniques for solving large-scale games. However, there is a lack of a quantitative guarantee about the quality of output approximate Nash equilibria (NE). A natural quantitative guarantee for such an approximate NE is the regret in the game (i.e. the best deviation gain). We formulate this deviation gain computation as a multi-armed bandit problem, with a new optimization goal unlike those studied in prior work. We propose an efficient algorithm Super-Arm UCB (SAUCB) for the problem and a number of variants. We present sample complexity results as well as extensive …

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Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman Feb 2020

Generating Realistic Stock Market Order Streams, Junyi Li, Xintong Wang, Yaoyang Lin, Arunesh Sinha, Michael P. Wellman

Research Collection School Of Computing and Information Systems

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks. We model the order stream as a stochastic process with finite history dependence, and employ a conditional Wasserstein GAN to capture history dependence of orders in a stock market. We test our approach with actual market and synthetic data on a number of different statistics, and find the generated data to be close to real data.

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Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe Feb 2020

Solving Online Threat Screening Games Using Constrained Action Space Reinforcement Learning, Sanket Shah, Arunesh Sinha, Pradeep Varakantham, Andrew Perrault, Millind Tambe

Research Collection School Of Computing and Information Systems

Large-scale screening for potential threats with limited resources and capacity for screening is a problem of interest at airports, seaports, and other ports of entry. Adversaries can observe screening procedures and arrive at a time when there will be gaps in screening due to limited resource capacities. To capture this game between ports and adversaries, this problem has been previously represented as a Stackelberg game, referred to as a Threat Screening Game (TSG). Given the significant complexity associated with solving TSGs and uncertainty in arrivals of customers, existing work has assumed that screenees arrive and are allocated security resources at …

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Privacy-Preserving Network Path Validation, Binanda Sengupta, Yingjiu Li, Kai Bu, Robert H. Deng Feb 2020

Privacy-Preserving Network Path Validation, Binanda Sengupta, Yingjiu Li, Kai Bu, Robert H. Deng

Research Collection School Of Computing and Information Systems

The end-users communicating over a network path currently have no control over the path. For a better quality of service, the source node often opts for a superior (or premium) network path to send packets to the destination node. However, the current Internet architecture provides no assurance that the packets indeed follow the designated path. Network path validation schemes address this issue and enable each node present on a network path to validate whether each packet has followed the specific path so far. In this work, we introduce two notions of privacy—path privacy and index privacy—in the context of network …

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Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw Feb 2020

Stochastically Robust Personalized Ranking For Lsh Recommendation Retrieval, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Locality Sensitive Hashing (LSH) has become one of the most commonly used approximate nearest neighbor search techniques to avoid the prohibitive cost of scanning through all data points. For recommender systems, LSH achieves efficient recommendation retrieval by encoding user and item vectors into binary hash codes, reducing the cost of exhaustively examining all the item vectors to identify the topk items. However, conventional matrix factorization models may suffer from performance degeneration caused by randomly-drawn LSH hash functions, directly affecting the ultimate quality of the recommendations. In this paper, we propose a framework named SRPR, which factors in the stochasticity of …

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Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw Feb 2020

Topic Modeling On Document Networks With Adjacent-Encoder, Ce Zhang, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Oftentimes documents are linked to one another in a network structure,e.g., academic papers cite other papers, Web pages link to other pages. In this paper we propose a holistic topic model to learn meaningful and unified low-dimensional representations for networked documents that seek to preserve both textual content and network structure. On the basis of reconstructing not only the input document but also its adjacent neighbors, we develop two neural encoder architectures. Adjacent-Encoder, or AdjEnc, induces competition among documents for topic propagation, and reconstruction among neighbors for semantic capture. Adjacent-Encoder-X, or AdjEnc-X, extends this to also encode the network structure …

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Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang Feb 2020

Deepdualmapper: A Gated Fusion Network For Automatic Map Extraction Using Aerial Images And Trajectories, Hao Wu, Hanyuan Zhang, Xinyu Zhang, Weiwei Sun, Baihua Zheng, Yuning Jiang

Research Collection School Of Computing and Information Systems

Automatic map extraction is of great importance to urban computing and location-based services. Aerial image and GPS trajectory data refer to two different data sources that could be leveraged to generate the map, although they carry different types of information. Most previous works on data fusion between aerial images and data from auxiliary sensors do not fully utilize the information of both modalities and hence suffer from the issue of information loss. We propose a deep convolutional neural network called DeepDualMapper which fuses the aerial image and trajectory data in a more seamless manner to extract the digital map. We …

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Mcdpc: Multi‐Center Density Peak Clustering, Yizhang Wang, Di Wang, Xiaofeng Zhang, Wei Pang, Chunyan Miao, Ah-Hwee Tan, You Zhou Feb 2020

Mcdpc: Multi‐Center Density Peak Clustering, Yizhang Wang, Di Wang, Xiaofeng Zhang, Wei Pang, Chunyan Miao, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

Density peak clustering (DPC) is a recently developed density-based clustering algorithm that achieves competitive performance in a non-iterative manner. DPC is capable of effectively handling clusters with single density peak (single center), i.e., based on DPC’s hypothesis, one and only one data point is chosen as the center of any cluster. However, DPC may fail to identify clusters with multiple density peaks (multi-centers) and may not be able to identify natural clusters whose centers have relatively lower local density. To address these limitations, we propose a novel clustering algorithm based on a hierarchical approach, named multi-center density peak clustering (McDPC). …

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Image Enhanced Event Detection In News Articles, Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juaizi Li, Lei Hou, Tat-Seng Chua Feb 2020

Image Enhanced Event Detection In News Articles, Meihan Tong, Shuai Wang, Yixin Cao, Bin Xu, Juaizi Li, Lei Hou, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Event detection is a crucial and challenging sub-task of event extraction, which suffers from a severe ambiguity issue of trigger words. Existing works mainly focus on using textual context information, while there naturally exist many images accompanied by news articles that are yet to be explored. We believe that images not only reflect the core events of the text, but are also helpful for the disambiguation of trigger words. In this paper, we first contribute an image dataset supplement to ED benchmarks (i.e., ACE2005) for training and evaluation. We then propose a novel Dual Recurrent Multimodal Model, DRMM, to conduct …

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Example-Based Colourization Via Dense Encoding Pyramids, Chufeng Xiao, Chu Han, Zhuming Zhang, Jing Qin, Tien-Tsin Wong, Guoqiang Han, Shengfeng He Feb 2020

Example-Based Colourization Via Dense Encoding Pyramids, Chufeng Xiao, Chu Han, Zhuming Zhang, Jing Qin, Tien-Tsin Wong, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

We propose a novel deep example-based image colourization method called dense encoding pyramid network. In our study, we define the colourization as a multinomial classification problem. Given a greyscale image and a reference image, the proposed network leverages large-scale data and then predicts colours by analysing the colour distribution of the reference image. We design the network as a pyramid structure in order to exploit the inherent multi-scale, pyramidal hierarchy of colour representations. Between two adjacent levels, we propose a hierarchical decoder-encoder filter to pass the colour distributions from the lower level to higher level in order to take both …

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Multi-Level Fine-Scaled Sentiment Sensing With Ambivalence Handling, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria Feb 2020

Multi-Level Fine-Scaled Sentiment Sensing With Ambivalence Handling, Zhaoxia Wang, Seng-Beng Ho, Erik Cambria

Research Collection School Of Computing and Information Systems

Social media represent a rich source of information, such as critiques, feedback, and other opinions posted online by Internet users. Such information is typically a good reflection of users’ sentiments and attitudes towards various services, topics, or products. Sentiment analysis has become an increasingly important natural language processing (NLP) task to help users make sense of what is happening in the Internet blogosphere and it can be useful for companies as well as public organizations. However, most existing sentiment analysis techniques are only able to analyze data at the aggregate level, merely providing a binary classification (positive vs. negative), and …

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