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Research Collection School Of Computing and Information Systems

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Geoprune: Efficiently Matching Trips In Ride-Sharing Through Geometric Properties, Yixin Xu, Jianzhong Qi, Renata Borovica-Gajic Jul 2020

Geoprune: Efficiently Matching Trips In Ride-Sharing Through Geometric Properties, Yixin Xu, Jianzhong Qi, Renata Borovica-Gajic

Research Collection School Of Computing and Information Systems

On-demand ride-sharing is rapidly growing. Matching trip requests to vehicles efficiently is critical for the service quality of ride-sharing. To match trip requests with vehicles, a prune-And-select scheme is commonly used. The pruning stage identifies feasible vehicles that can satisfy the trip constraints (e.g., trip time). The selection stage selects the optimal one(s) from the feasible vehicles. The pruning stage is crucial to lowering the complexity of the selection stage and to achieve efficient matching. We propose an effective and efficient pruning algorithm called GeoPrune. GeoPrune represents the time constraints of trip requests using circles and ellipses, which can be …

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Acceleration For Compressed Gradient Descent In Distributed And Federated Optimization, Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik Jul 2020

Acceleration For Compressed Gradient Descent In Distributed And Federated Optimization, Zhize Li, Dmitry Kovalev, Xun Qian, Peter Richtarik

Research Collection School Of Computing and Information Systems

Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While in other contexts the best performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of iterations, there are no methods which combine the benefits of both gradient compression and acceleration. In this paper, we remedy this situation and propose the first accelerated compressed gradient descent (ACGD) methods. In the single machine regime, we prove that ACGD enjoys the rate $O\Big((1+\omega)\sqrt{\frac{L}{\mu}}\log \frac{1}{\epsilon}\Big)$ for $\mu$-strongly convex problems and $O\Big((1+\omega)\sqrt{\frac{L}{\epsilon}}\Big)$ for convex problems, respectively, …

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Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa Jul 2020

Deep Learning Of Facial Embeddings And Facial Landmark Points For The Detection Of Academic Emotions, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Automatic emotion recognition is an actively researched area as emotion plays a pivotal role in effective human communications. Equipping a computer to understand and respond to human emotions has potential applications in many fields including education, medicine, transport and hospitality. In a classroom or online learning context, the basic emotions do not occur frequently and do not influence the learning process itself. The academic emotions such as engagement, frustration, confusion and boredom are the ones which are pivotal to sustaining the motivation of learners. In this study, we evaluated the use of deep learning on FaceNet embeddings and facial landmark …

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Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization With Nearly Optimal Generalization, Pan Zhou, Xiaotong Yuan Jul 2020

Hybrid Stochastic-Deterministic Minibatch Proximal Gradient: Less-Than-Single-Pass Optimization With Nearly Optimal Generalization, Pan Zhou, Xiaotong Yuan

Research Collection School Of Computing and Information Systems

Stochastic variance-reduced gradient (SVRG) algorithms have been shown to work favorably in solving large-scale learning problems. Despite the remarkable success, the stochastic gradient complexity of SVRG-type algorithms usually scales linearly with data size and thus could still be expensive for huge data. To address this deficiency, we propose a hybrid stochastic-deterministic minibatch proximal gradient (HSDMPG) algorithm for strongly-convex problems that enjoys provably improved data-size-independent complexity guarantees.

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Recent Advances In Deep Learning For Object Detection, Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi Jul 2020

Recent Advances In Deep Learning For Object Detection, Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Object detection is a fundamental visual recognition problem in computer vision and has been widely studied in the past decades. Visual object detection aims to find objects of certain target classes with precise localization in a given image and assign each object instance a corresponding class label. Due to the tremendous successes of deep learning based image classification, object detection techniques using deep learning have been actively studied in recent years. In this paper, we give a comprehensive survey of recent advances in visual object detection with deep learning. By reviewing a large body of recent related work in literature, …

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Probabilistic Value Selection For Space Efficient Model, Gunarto Sindoro Njoo, Baihua Zheng, Kuo-Wei Hsu, Wen-Chih Peng Jul 2020

Probabilistic Value Selection For Space Efficient Model, Gunarto Sindoro Njoo, Baihua Zheng, Kuo-Wei Hsu, Wen-Chih Peng

Research Collection School Of Computing and Information Systems

An alternative to current mainstream preprocessing methods is proposed: Value Selection (VS). Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy. Two probabilistic methods based on information theory's metric are proposed: PVS and P + VS. Extensive experiments on the benchmark datasets with various sizes are elaborated. Those results are compared with the existing preprocessing methods such as feature selection, feature transformation, and instance selection methods. Experiment results …

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Next-Term Grade Prediction: A Machine Learning Approach, Audrey Tedja Widjaja, Lei Wang, Nghia Truong Trong, Aldy Gunawan, Ee-Peng Lim Jul 2020

Next-Term Grade Prediction: A Machine Learning Approach, Audrey Tedja Widjaja, Lei Wang, Nghia Truong Trong, Aldy Gunawan, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

As students progress in their university programs, they have to face many course choices. It is important for them to receive guidance based on not only their interest, but also the "predicted" course performance so as to improve learning experience and optimise academic performance. In this paper, we propose the next-term grade prediction task as a useful course selection guidance. We propose a machine learning framework to predict course grades in a specific program term using the historical student-course data. In this framework, we develop the prediction model using Factorization Machine (FM) and Long Short Term Memory combined with FM …

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Improving Multimodal Named Entity Recognition Via Entity Span Detection With Unified Multimodal Transformer, Jianfei Yu, Jing Jiang, Li Yang, Rui Xia Jul 2020

Improving Multimodal Named Entity Recognition Via Entity Span Detection With Unified Multimodal Transformer, Jianfei Yu, Jing Jiang, Li Yang, Rui Xia

Research Collection School Of Computing and Information Systems

In this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their word representations are insensitive to the visual context; (2) most of them ignore the bias brought by the visual context. To tackle the first issue, we propose a multimodal interaction module to obtain both image-aware word representations and word-aware visual representations. To alleviate the visual bias, we further propose to leverage purely text-based entity span detection as an auxiliary module, and design a Unified Multimodal Transformer to guide the final …

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Biane: Bipartite Attributed Network Embedding, Wentao Huang, Yuchen Li, Yuan Fang, Ju Fan, Hongxia Yang Jul 2020

Biane: Bipartite Attributed Network Embedding, Wentao Huang, Yuchen Li, Yuan Fang, Ju Fan, Hongxia Yang

Research Collection School Of Computing and Information Systems

Network embedding effectively transforms complex network data into a low-dimensional vector space and has shown great performance in many real-world scenarios, such as link prediction, node classification, and similarity search. A plethora of methods have been proposed to learn node representations and achieve encouraging results. Nevertheless, little attention has been paid on the embedding technique for bipartite attributed networks, which is a typical data structure for modeling nodes from two distinct partitions. In this paper, we propose a novel model called BiANE, short for Bipartite Attributed Network Embedding. In particular, BiANE not only models the inter-partition proximity but also models …

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Semi-Supervised Co-Clustering On Attributed Heterogeneous Information Networks, Yugang Ji, Chuan Shi, Yuan Fang, Xiangnan Kong, Mingyang Yin Jul 2020

Semi-Supervised Co-Clustering On Attributed Heterogeneous Information Networks, Yugang Ji, Chuan Shi, Yuan Fang, Xiangnan Kong, Mingyang Yin

Research Collection School Of Computing and Information Systems

Node clustering on heterogeneous information networks (HINs) plays an important role in many real-world applications. While previous research mainly clusters same-type nodes independently via exploiting structural similarity search, they ignore the correlations of different-type nodes. In this paper, we focus on the problem of co-clustering heterogeneous nodes where the goal is to mine the latent relevance of heterogeneous nodes and simultaneously partition them into the corresponding type-aware clusters. This problem is challenging in two aspects. First, the similarity or relevance of nodes is not only associated with multiple meta-path-based structures but also related to numerical and categorical attributes. Second, clusters …

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Context-Aware And Scale-Insensitive Temporal Repetition Counting, Huaidong Zhang, Xuemiao Xu, Guoqiang Han, Shengfeng He Jun 2020

Context-Aware And Scale-Insensitive Temporal Repetition Counting, Huaidong Zhang, Xuemiao Xu, Guoqiang Han, Shengfeng He

Research Collection School Of Computing and Information Systems

Temporal repetition counting aims to estimate the number of cycles of a given repetitive action. Existing deep learning methods assume repetitive actions are performed in a fixed time-scale, which is invalid for the complex repetitive actions in real life. In this paper, we tailor a context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown and diverse cycle-lengths. Our approach combines two key insights: (1) Cycle lengths from different actions are unpredictable that require large-scale searching, but, once a coarse cycle length is determined, the variety between repetitions can be overcome by regression. (2) Determining …

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Visual Commonsense Representation Learning Via Causal Inference, Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun Jun 2020

Visual Commonsense Representation Learning Via Causal Inference, Tan Wang, Jianqiang Huang, Hanwang Zhang, Qianru Sun

Research Collection School Of Computing and Information Systems

We present a novel unsupervised feature representation learning method, Visual Commonsense Region-based Convolutional Neural Network (VC R-CNN), to serve as an improved visual region encoder for high-level tasks such as captioning and VQA. Given a set of detected object regions in an image (e.g., using Faster R-CNN), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the con-textual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). We extensively apply …

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Gpu-Accelerated Subgraph Enumeration On Partitioned Graphs, Wentian Guo, Yuchen Li, Mo Sha, Bingsheng He, Xiaokui Xiao, Kian-Lee Tan Jun 2020

Gpu-Accelerated Subgraph Enumeration On Partitioned Graphs, Wentian Guo, Yuchen Li, Mo Sha, Bingsheng He, Xiaokui Xiao, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Subgraph enumeration is important for many applications such as network motif discovery and community detection. Recent works utilize graphics processing units (GPUs) to parallelize subgraph enumeration, but they can only handle graphs that fit into the GPU memory. In this paper, we propose a new approach for GPU-accelerated subgraph enumeration that can efficiently scale to large graphs beyond the GPU memory. Our approach divides the graph into partitions, each of which fits into the GPU memory. The GPU processes one partition at a time and searches the matched subgraphs of a given pattern (i.e., instances) within the partition as in …

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Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang Jun 2020

Hyperbolic Visual Embedding Learning For Zero-Shot Recognition, Shaoteng Liu, Jingjing Chen, Liangming Pan, Chong-Wah Ngo, Tat-Seng Chua, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. Comparing with existing zeroshot learning approaches, the network is more robust because the embedding feature in hyperbolic space better represents class hierarchy and thereby avoid misleading resulted from unrelated siblings. Our network outperforms exiting baselines under hierarchical evaluation with an extremely challenging setting, i.e., learning only from 1,000 categories to recognize 20,841 unseen categories. While under flat evaluation, it has competitive performance as state-of-the-art methods but …

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Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele Jun 2020

Adaptive Loss-Aware Quantization For Multi-Bit Networks, Zhongnan Qu, Zimu Zhou, Yun Cheng, Lothar Thiele

Research Collection School Of Computing and Information Systems

We investigate the compression of deep neural networks by quantizing their weights and activations into multiple binary bases, known as multi-bit networks (MBNs), which accelerate the inference and reduce the storage for the deployment on low-resource mobile and embedded platforms. We propose Adaptive Loss-aware Quantization (ALQ), a new MBN quantization pipeline that is able to achieve an average bitwidth below one-bit without notable loss in inference accuracy. Unlike previous MBN quantization solutions that train a quantizer by minimizing the error to reconstruct full precision weights, ALQ directly minimizes the quantizationinduced error on the loss function involving neither gradient approximation nor …

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Mnemonics Training: Multi-Class Incremental Learning Without Forgetting, Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, Qianru Sun Jun 2020

Mnemonics Training: Multi-Class Incremental Learning Without Forgetting, Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, Qianru Sun

Research Collection School Of Computing and Information Systems

Multi-Class Incremental Learning (MCIL) aims to learn new concepts by incrementally updating a model trained on previous concepts. However, there is an inherent trade-off to effectively learning new concepts without catastrophic forgetting of previous ones. To alleviate this issue, it has been proposed to keep around a few examples of the previous concepts but the effectiveness of this approach heavily depends on the representativeness of these examples. This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner. We train the framework through bilevel optimizations, i.e., model-level and …

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Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua Jun 2020

Knowledge Enhanced Neural Fashion Trend Forecasting, Yunshan Ma, Yujuan Ding, Xun Yang, Lizi Liao, Wai Keung Wong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Fashion trend forecasting is a crucial task for both academia andindustry. Although some efforts have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal thereal fashion trends. Towards insightful fashion trend forecasting,this work focuses on investigating fine-grained fashion element trends for specific user groups. We first contribute a large-scale fashion trend dataset (FIT) collected from Instagram with extracted time series fashion element records and user information. Furthermore, to effectively model the time series data of fashion elements with rather complex patterns, we propose a Knowledge Enhanced …

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Ntire 2020 Challenge On Video Quality Mapping: Methods And Results, D. Fuoli, Zhiwu Huang, M. Danelljan, R. Timofte, H. Wang, L. Jin, D. Su, J. Liu, J. Lee, M. Kudelski, L. Bala, D. Hryboy, M. Mozejko, M. Li, S. Li, B. Pang, C. Lu, Li C., He D., Li F. Jun 2020

Ntire 2020 Challenge On Video Quality Mapping: Methods And Results, D. Fuoli, Zhiwu Huang, M. Danelljan, R. Timofte, H. Wang, L. Jin, D. Su, J. Liu, J. Lee, M. Kudelski, L. Bala, D. Hryboy, M. Mozejko, M. Li, S. Li, B. Pang, C. Lu, Li C., He D., Li F.

Research Collection School Of Computing and Information Systems

This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The challenge includes both a supervised track (track 1) and a weakly-supervised track (track 2) for two benchmark datasets. In particular, track 1 offers a new Internet video benchmark, requiring algorithms to learn the map from more compressed videos to less compressed videos in a supervised training manner. In track 2, algorithms are required to learn the quality mapping from one device to another when their quality varies substantially and weaklyaligned video pairs …

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Secure Server-Aided Data Sharing Clique With Attestation, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin, Kefeng Fan Jun 2020

Secure Server-Aided Data Sharing Clique With Attestation, Yujue Wang, Hwee Hwa Pang, Robert H. Deng, Yong Ding, Qianhong Wu, Bo Qin, Kefeng Fan

Research Collection School Of Computing and Information Systems

In this paper, we consider the security issues in data sharing cliques via remote server. We present a public key re-encryption scheme with delegated equality test on ciphertexts (PRE-DET). The scheme allows users to share outsourced data on the server without performing decryption-then-encryption procedures, allows new users to dynamically join the clique, allows clique users to attest the message underlying a ciphertext, and enables the server to partition outsourced user data without any further help of users after being delegated. We introduce the PRE-DET framework, propose a concrete construction and formally prove its security against five types of adversaries regarding …

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Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou Jun 2020

Goods Consumed During Transit In Split Delivery Vehicle Routing Problems: Modeling And Solution, Wenzhe Yang, Di Wang, Wei Pang, Ah-Hwee Tan, You Zhou

Research Collection School Of Computing and Information Systems

This article presents the modeling and solution of an extended type of split delivery vehicle routing problem (SDVRP). In SDVRP, the demands of customers need to be met by efficiently routing a given number of capacitated vehicles, wherein each customer may be served multiple times by more than one vehicle. Furthermore, in many real-world scenarios, consumption of vehicles en route is the same as the goods being delivered to customers, such as food, water and fuel in rescue or replenishment missions in harsh environments. Moreover, the consumption may also be in virtual forms, such as time spent in constrained tasks. …

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Maximum A Posteriori Estimation For Information Source Detection, Biao Chang, Enhong Chen, Feida Zhu, Qi Liu, Tong Xu Jun 2020

Maximum A Posteriori Estimation For Information Source Detection, Biao Chang, Enhong Chen, Feida Zhu, Qi Liu, Tong Xu

Research Collection School Of Computing and Information Systems

Information source detection is to identify nodes initiating the diffusion process in a network, which has a wide range of applications including epidemic outbreak prevention, Internet virus source identification, and rumor source tracing in social networks. Although it has attracted ever-increasing attention from research community in recent years, existing solutions still suffer from high time complexity and inadequate effectiveness, due to high dynamics of information diffusion and observing just a snapshot of the whole process. To this end, we present a comprehensive study for single information source detection in weighted graphs. Specifically, we first propose a maximum a posteriori (MAP) …

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Use Of Eye-Tracking To Identify Psychological Indicators Of Sleepiness, Debasis Roy, Fiona Fui-Hoon Nah, Matthew Thimgan May 2020

Use Of Eye-Tracking To Identify Psychological Indicators Of Sleepiness, Debasis Roy, Fiona Fui-Hoon Nah, Matthew Thimgan

Research Collection School Of Computing and Information Systems

Sleepiness or sleep deprivation creates a serious hazard or obstacle to task execution and performance. Sleep deprivation can be life-threating (e.g., when driving or executing attention-critical tasks). We are interested to examine if eye-tracking technology can be used to assess and detect sleepiness in an online environment. In this research proposal, we will focus on examining the relationships between sleepiness and parameters involving pupil size, blinks, and saccades.

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Jplink: On Linking Jobs To Vocational Interest Types, Amila Silva, Pei Chi Lo, Ee-Peng Lim May 2020

Jplink: On Linking Jobs To Vocational Interest Types, Amila Silva, Pei Chi Lo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Linking job seekers with relevant jobs requires matching based on not only skills, but also personality types. Although the Holland Code also known as RIASEC has frequently been used to group people by their suitability for six different categories of occupations, the RIASEC category labels of individual jobs are often not found in job posts. This is attributed to significant manual efforts required for assigning job posts with RIASEC labels. To cope with assigning massive number of jobs with RIASEC labels, we propose JPLink, a machine learning approach using the text content in job titles and job descriptions. JPLink exploits …

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Relationships Between Willingness To Share Information For Benefits And Trust, Gaurav Bansal, Fiona Fui-Hoon Nah May 2020

Relationships Between Willingness To Share Information For Benefits And Trust, Gaurav Bansal, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

This research examines the role of willingness to share one’s information for three benefits as tradeoffs – monetary gains, personalization, and national security – and their effects on trust in online businesses. Data were gathered from MTurk and the results indicate that willingness to share information for monetary gains and personalization is marginally associated with trust in online businesses, but willingness to share information for national security has no association with trust in online businesses. The paper also discusses implications, limitations, and future research directions.

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A 2020 Perspective On "Client Risk Informedness In Brokered Cloud Services: An Experimental Pricing Study", Di Shang, Robert J. Kauffman May 2020

A 2020 Perspective On "Client Risk Informedness In Brokered Cloud Services: An Experimental Pricing Study", Di Shang, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Cloud computing and the cloud services market have advanced in the past ten years. Cloud services now include most information technology (IT) services from fundamental computing services to more cutting- edge artificial intelligence (AI) services. Accordingly, opportunities have emerged for research on the design of new market features to improve the cloud services market to benefit providers and users. Based on our observation of the recent development of cloud services, in this short research commentary, we share our agenda for future studies of this important sector of IT services.

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Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan May 2020

Chaff From The Wheat: Characterizing And Determining Valid Bug Reports, Yuanrui Fan, Xin Xia, David Lo, Ahmed E. Hassan

Research Collection School Of Computing and Information Systems

Developers use bug reports to triage and fix bugs. When triaging a bug report, developers must decide whether the bug report is valid (i.e., a real bug). A large amount of bug reports are submitted every day, with many of them end up being invalid reports. Manually determining valid bug report is a difficult and tedious task. Thus, an approach that can automatically analyze the validity of a bug report and determine whether a report is valid can help developers prioritize their triaging tasks and avoid wasting time and effort on invalid bug reports. In this study, motivated by the …

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Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu May 2020

Robust Graph Learning From Noisy Data, Zhao Kang, Haiqi Pan, Steven C. H. Hoi, Zenglin Xu

Research Collection School Of Computing and Information Systems

Learning graphs from data automatically have shown encouraging performance on clustering and semisupervised learning tasks. However, real data are often corrupted, which may cause the learned graph to be inexact or unreliable. In this paper, we propose a novel robust graph learning scheme to learn reliable graphs from the real-world noisy data by adaptively removing noise and errors in the raw data. We show that our proposed model can also be viewed as a robust version of manifold regularized robust principle component analysis (RPCA), where the quality of the graph plays a critical role. The proposed model is able to …

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Route Choice Behaviour And Travel Information In A Congested Network: Static And Dynamic Recursive Models, Giselle De Moraes Ramos, Tien Mai, Winnie Daamen, Emma Frejinger May 2020

Route Choice Behaviour And Travel Information In A Congested Network: Static And Dynamic Recursive Models, Giselle De Moraes Ramos, Tien Mai, Winnie Daamen, Emma Frejinger

Research Collection School Of Computing and Information Systems

Travel information has the potential to influence travellers choices, in order to steer travellers to less congested routes and alleviate congestion. This paper investigates, on the one hand, how travel information affects route choice behaviour, and on the other hand, the impact of the travel time representation on the interpretation of parameter estimates and prediction accuracy. To this end, we estimate recursive models using data from an innovative data collection effort consisting of route choice observation data from GPS trackers, travel diaries and link travel times on the overall network. Though such combined data sets exist, these have not yet …

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Platform Pricing With Strategic Buyers: The Impact Of Future Production Cost, Mei Lin, Xiajun Amy Pan, Quan Zheng May 2020

Platform Pricing With Strategic Buyers: The Impact Of Future Production Cost, Mei Lin, Xiajun Amy Pan, Quan Zheng

Research Collection School Of Computing and Information Systems

Two-sided platforms are often coupled with exclusive hardware products that connect two sides of users, the consumers of the hardware product (i.e., buyers) and the application developers (i.e., sellers). The hardware product in the platform business model introduces three important issues that are not yet well understood in the literature of platform pricing: potentially downward-trending production cost, product quality improvements, and consumers' strategic behaviors. Using analytical modeling, our study explicitly factors in these issues in analyzing a monopoly platform owner's two-sided pricing problem. The platform sequentially introduces and prices quality-improving hardware products, for which the costliness of quality may decrease. …

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Hierarchical Reinforcement Learning With Integrated Discovery Of Salient Subgoals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan May 2020

Hierarchical Reinforcement Learning With Integrated Discovery Of Salient Subgoals, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals which are simpler to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is …

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