An Economic Analysis Of Rebates Conditional On Positive Reviews,
2021
Singapore Management University
An Economic Analysis Of Rebates Conditional On Positive Reviews, Jianqing Chen, Zhiling Guo, Jian Huang
Research Collection School Of Computing and Information Systems
Strategic sellers on some online selling platforms have recently been using a conditional-rebate strategy to manipulate product reviews under which only purchasing consumers who post positive reviews online are eligible to redeem the rebate. A key concern for the conditional rebate is that it can easily induce fake reviews, which might be harmful to consumers and society. We develop a microbehavioral model capturing consumers’ review-sharing benefit, review-posting cost, and moral cost of lying to examine the seller’s optimal pricing and rebate decisions. We derive three equilibria: the no-rebate, organic-review equilibrium; the low-rebate, boosted-authentic-review equilibrium; and the high-rebate, partially-fake-review equilibrium. We …
Gpu-Accelerated Graph Label Propagation For Real-Time Fraud Detection,
2021
Singapore Management University
Gpu-Accelerated Graph Label Propagation For Real-Time Fraud Detection, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Research Collection School Of Computing and Information Systems
Fraud detection is a pressing challenge for most financial and commercial platforms. In this paper, we study the processing pipeline of fraud detection in a large e-commerce platform of TaoBao. Graph label propagation (LP) is a core component in this pipeline to detect suspicious clusters from the user-interaction graph. Furthermore, the run-time of the LP component occupies 75% overhead of TaoBao’s automated detection pipeline. To enable real-time fraud detection, we propose a GPU-based framework, called GLP, to support large-scale LP workloads in enterprises. We have identified two key challenges when integrating GPU acceleration into TaoBao’s data processing pipeline: (1) programmability …
Soarnet, Deep Learning Thermal Detection For Free Flight,
2021
California Polytechnic State University, San Luis Obispo
Soarnet, Deep Learning Thermal Detection For Free Flight, Jake T. Tallman
Master's Theses
Thermals are regions of rising hot air formed on the ground through the warming of the surface by the sun. Thermals are commonly used by birds and glider pilots to extend flight duration, increase cross-country distance, and conserve energy. This kind of powerless flight using natural sources of lift is called soaring. Once a thermal is encountered, the pilot flies in circles to keep within the thermal, so gaining altitude before flying off to the next thermal and towards the destination. A single thermal can net a pilot thousands of feet of elevation gain, however estimating thermal locations is not …
Virtual Reality For Hazard Mitigation And Community Resilience: An Interdisciplinary Collaboration With Community Engagement To Enhance Risk Awareness,
2021
Singapore Management University
Virtual Reality For Hazard Mitigation And Community Resilience: An Interdisciplinary Collaboration With Community Engagement To Enhance Risk Awareness, N. J. Stone, G. Yan, Fiona Fui-Hoon Nah, C. Sabharwal, K. Angle, F. E. Hatch, S. Runnels, V. Brown, G. M. Schoor, C. Engelbrecht
Research Collection School Of Computing and Information Systems
To achieve community resilience and mitigate the consequences of natural hazards, community officials must balance competing priorities for local resources and funding. Besides the challenge of dealing with multiple competing priorities, community officials face another challenge: low risk awareness of natural hazards by the public and other stakeholders. Considering that virtual reality (VR) has been used to enhance learning and to change attitudes and behaviors, animating natural hazards in VR has the potential to enhance stakeholders’ (e.g., the public, local/state/federal governments, insurance agencies, and property owners) risk awareness. Informed stakeholders make better decisions related to protective action. Therefore, we propose …
Iquant: Interactive Quantitative Investment Using Sparse Regression Factors,
2021
Singapore Management University
Iquant: Interactive Quantitative Investment Using Sparse Regression Factors, Xuanwu Yue, Qiao Gu, Deyun Wang, Huamin Qu, Yong Wang
Research Collection School Of Computing and Information Systems
The model-based investing using financial factors is evolving as a principal method for quantitative investment. The main challenge lies in the selection of effective factors towards excess market returns. Existing approaches, either hand-picking factors or applying feature selection algorithms, do not orchestrate both human knowledge and computational power. This paper presents iQUANT, an interactive quantitative investment system that assists equity traders to quickly spot promising financial factors from initial recommendations suggested by algorithmic models, and conduct a joint refinement of factors and stocks for investment portfolio composition. We work closely with professional traders to assemble empirical characteristics of “good” factors …
Boosting Video Representation Learning With Multi-Faceted Integration,
2021
Singapore Management University
Boosting Video Representation Learning With Multi-Faceted Integration, Zhaofan Qiu, Yao Ting, Chong-Wah Ngo, Xiao-Ping Zhang, Dong Wu, Tao Mei
Research Collection School Of Computing and Information Systems
Video content is multifaceted, consisting of objects, scenes, interactions or actions. The existing datasets mostly label only one of the facets for model training, resulting in the video representation that biases to only one facet depending on the training dataset. There is no study yet on how to learn a video representation from multifaceted labels, and whether multifaceted information is helpful for video representation learning. In this paper, we propose a new learning framework, MUlti-Faceted Integration (MUFI), to aggregate facets from different datasets for learning a representation that could reflect the full spectrum of video content. Technically, MUFI formulates the …
Marrying Top-K With Skyline Queries: Relaxing The Preference Input While Producing Output Of Controllable Size,
2021
Singapore Management University
Marrying Top-K With Skyline Queries: Relaxing The Preference Input While Producing Output Of Controllable Size, Kyriakos Mouratidis, Keming Li, Bo Tang
Research Collection School Of Computing and Information Systems
The two most common paradigms to identify records of preference in a multi-objective setting rely either on dominance (e.g., the skyline operator) or on a utility function defined over the records’ attributes (typically, using a top-�� query). Despite their proliferation, each of them has its own palpable drawbacks. Motivated by these drawbacks, we identify three hard requirements for practical decision support, namely, personalization, controllable output size, and flexibility in preference specification. With these requirements as a guide, we combine elements from both paradigms and propose two new operators, ORD and ORU. We perform a qualitative study to demonstrate how they …
Cache-Efficient Fork-Processing Patterns On Large Graphs,
2021
Singapore Management University
Cache-Efficient Fork-Processing Patterns On Large Graphs, Shengliang Lu, Shixuan Sun, Johns Paul, Yuchen Li, Bingsheng He
Research Collection School Of Computing and Information Systems
As large graph processing emerges, we observe a costly fork-processing pattern (FPP) that is common in many graph algorithms. The unique feature of the FPP is that it launches many independent queries from different source vertices on the same graph. For example, an algorithm in analyzing the network community profile can execute Personalized PageRanks that start from tens of thousands of source vertices at the same time. We study the efficiency of handling FPPs in state-of-the-art graph processing systems on multi-core architectures, including Ligra, Gemini, and GraphIt. We find that those systems suffer from severe cache miss penalty because of …
Self-Adaptive Graph Traversal On Gpus,
2021
Singapore Management University
Self-Adaptive Graph Traversal On Gpus, Mo Sha, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
GPU’s massive computing power offers unprecedented opportunities to enable large graph analysis. Existing studies proposed various preprocessing approaches that convert the input graphs into dedicated structures for GPU-based optimizations. However, these dedicated approaches incur significant preprocessing costs as well as weak programmability to build general graph applications. In this paper, we introduce SAGE, a self-adaptive graph traversal on GPUs, which is free from preprocessing and operates on ubiquitous graph representations directly. We propose Tiled Partitioning and Resident Tile Stealing to fully exploit the computing power of GPUs in a runtime and self-adaptive manner. We also propose Sampling-based Reordering to further …
Minimum Coresets For Maxima Representation Of Multidimensional Data,
2021
Singapore Management University
Minimum Coresets For Maxima Representation Of Multidimensional Data, Yanhao Wang, Michael Mathioudakis, Yuchen Li, Kian-Lee Tan
Research Collection School Of Computing and Information Systems
Coresets are succinct summaries of large datasets such that, for a given problem, the solution obtained from a coreset is provably competitive with the solution obtained from the full dataset. As such, coreset-based data summarization techniques have been successfully applied to various problems, e.g., geometric optimization, clustering, and approximate query processing, for scaling them up to massive data. In this paper, we study coresets for the maxima representation of multidimensional data: Given a set �� of points in R �� , where �� is a small constant, and an error parameter �� ∈ (0, 1), a subset �� ⊆ �� …
On M-Impact Regions And Standing Top-K Influence Problems,
2021
Singapore Management University
On M-Impact Regions And Standing Top-K Influence Problems, Bo Tang, Kyriakos Mouratidis, Mingji Han
Research Collection School Of Computing and Information Systems
In this paper, we study the ��-impact region problem (mIR). In a context where users look for available products with top-�� queries, mIR identifies the part of the product space that attracts the most user attention. Specifically, mIR determines the kind of attribute values that lead a (new or existing) product to the top-�� result for at least a fraction of the user population. mIR has several applications, ranging from effective marketing to product improvement. Importantly, it also leads to (exact and efficient) solutions for standing top-�� impact problems, which were previously solved heuristically only, or whose current solutions face …
Efficient Conditional Gan Transfer With Knowledge Propagation Across Classes,
2021
Singapore Management University
Efficient Conditional Gan Transfer With Knowledge Propagation Across Classes, Shahbazi. Mohamad, Zhiwu Huang, Huang, Danda Pani Paudel, Ajad Chhatkuli, Gool L. Van
Research Collection School Of Computing and Information Systems
Generative adversarial networks (GANs) have shown impressive results in both unconditional and conditional image generation. In recent literature, it is shown that pre-trained GANs, on a different dataset, can be transferred to improve the image generation from a small target data. The same, however, has not been well-studied in the case of conditional GANs (cGANs), which provides new opportunities for knowledge transfer compared to unconditional setup. In particular, the new classes may borrow knowledge from the related old classes, or share knowledge among themselves to improve the training. This motivates us to study the problem of efficient conditional GAN transfer …
Ganmut: Learning Interpretable Conditional Space For A Gamut Of Emotions,
2021
Singapore Management University
Ganmut: Learning Interpretable Conditional Space For A Gamut Of Emotions, S. D'Apolito, D.P. Paundel, Zhiwu Huang, A.R. Vergara, Gool L. Van
Research Collection School Of Computing and Information Systems
Humans can communicate emotions through a plethora of facial expressions, each with its own intensity, nuances and ambiguities. The generation of such variety by means of conditional GANs is limited to the expressions encoded in the used label system. These limitations are caused either due to burdensome labeling demand or the confounded label space. On the other hand, learning from inexpensive and intuitive basic categorical emotion labels leads to limited emotion variability. In this paper, we propose a novel GAN-based framework which learns an expressive and interpretable conditional space (usable as a label space) of emotions, instead of conditioning on …
Minimizing The Regret Of An Influence Provider,
2021
Singapore Management University
Minimizing The Regret Of An Influence Provider, Yipeng Zhang, Yuchen Li, Zhifeng Bao, Baihua Zheng
Research Collection School Of Computing and Information Systems
Influence maximization has been studied extensively from the perspective of the influencer. However, the influencer typically purchases influence from a provider, for example in the form of purchased advertising. In this paper, we study the problem from the perspective of the influence provider. Specifically, we focus on influence providers who sell Out-of-Home (OOH) advertising on billboards. Given a set of requests from influencers, how should an influence provider allocate resources to minimize regret, whether due to forgone revenue from influencers whose needs were not met or due to over-provisioning of resources to meet the needs of influencers? We formalize this …
Multi-View Collaborative Network Embedding,
2021
Nanyang Technological University
Multi-View Collaborative Network Embedding, Sezin Kircali Ata, Yuan Fang, Min Wu, Jiaqi Shi, Chee Keong Kwoh, Xiaoli Li
Research Collection School Of Computing and Information Systems
Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked, if they have common favorite videos in one view, then they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this article, we propose Multi-view collAborative Network Embedding (MANE), a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration—while diversity enables …
Catch You With Cache: Out-Of-Vm Introspection To Trace Malicious Executions,
2021
Singapore Management University
Catch You With Cache: Out-Of-Vm Introspection To Trace Malicious Executions, Chao Su, Xuhua Ding, Qinghai Zeng
Research Collection School Of Computing and Information Systems
Out-of-VM introspection is an imperative part of security analysis. The legacy methods either modify the system, introducing enormous overhead, or rely heavily on hardware features, which are neither available nor practical in most cloud environments. In this paper, we propose a novel analysis method, named as Catcher, that utilizes CPU cache to perform out-of-VM introspection. Catcher does not make any modifications to the target program and its running environment, nor demands special hardware support. Implemented upon Linux KVM, it natively introspects the target's virtual memory. More importantly, it uses the cache-based side channel to infer the target control flow. To …
Reciprocal Transformations For Unsupervised Video Object Segmentation,
2021
Singapore Management University
Reciprocal Transformations For Unsupervised Video Object Segmentation, Sucheng Ren, Wenxi Liu, Yongtuo Liu, Haoxin Chen, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Unsupervised video object segmentation (UVOS) aims at segmenting the primary objects in videos without any human intervention. Due to the lack of prior knowledge about the primary objects, identifying them from videos is the major challenge of UVOS. Previous methods often regard the moving objects as primary ones and rely on optical flow to capture the motion cues in videos, but the flow information alone is insufficient to distinguish the primary objects from the background objects that move together. This is because, when the noisy motion features are combined with the appearance features, the localization of the primary objects is …
Learning From The Master: Distilling Cross-Modal Advanced Knowledge For Lip Reading,
2021
Singapore Management University
Learning From The Master: Distilling Cross-Modal Advanced Knowledge For Lip Reading, Sucheng Ren, Yong Du, Jianming Lv, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal data could lead to a learning ambiguity and thus limits the performance of lip reading. In this paper, we propose a novel collaborative framework for lip reading, and two aspects of issues are considered: 1) the teacher should understand bi-modal knowledge to possibly bridge the inherent cross-modal gap; 2) …
Discovering Interpretable Latent Space Directions Of Gans Beyond Binary Attributes,
2021
Singapore Management University
Discovering Interpretable Latent Space Directions Of Gans Beyond Binary Attributes, Huiting Yang, Liangyu Chai, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He
Research Collection School Of Computing and Information Systems
Generative adversarial networks (GANs) learn to map noise latent vectors to high- fidelity image outputs. It is found that the input latent space shows semantic correlations with the output image space. Recent works aim to interpret the latent space and discover meaningful directions that correspond to human interpretable image transformations. However, these methods either rely on explicit scores of attributes (e.g., memorability) or are restricted to binary ones (e.g., gender), which largely limits the applicability of editing tasks, especially for free- form artistic tasks like style/anime editing. In this paper, we propose an adversarial method, AdvStyle, for discovering interpretable directions …
Spatially-Invariant Style-Codes Controlled Makeup Transfer,
2021
Singapore Management University
Spatially-Invariant Style-Codes Controlled Makeup Transfer, Han Deng, Chu Han, Hongmin Cai, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Transferring makeup from the misaligned reference image is challenging. Previous methods overcome this barrier by computing pixel-wise correspondences between two images, which is inaccurate and computational-expensive. In this paper, we take a different perspective to break down the makeup transfer problem into a two-step extraction-assignment process. To this end, we propose a Style-based Controllable GAN model that consists of three components, each of which corresponds to target style-code encoding, face identity features extraction, and makeup fusion, respectively. In particular, a Part-specific Style Encoder encodes the component-wise makeup style of the reference image into a style-code in an intermediate latent space …
