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Articles 1 - 30 of 53
Full-Text Articles in Engineering
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Identifying Regional Trends In Avatar Customization, Peter Mawhorter, Sercan Sengun, Haewoon Kwak, D. Fox Harrell
Research Collection School Of Computing and Information Systems
Since virtual identities such as social media profiles and avatars have become a common venue for self-expression, it has become important to consider the ways in which existing systems embed the values of their designers. In order to design virtual identity systems that reflect the needs and preferences of diverse users, understanding how the virtual identity construction differs between groups is important. This paper presents a new methodology that leverages deep learning and differential clustering for comparative analysis of profile images, with a case study of almost 100 000 avatars from a large online community using a popular avatar creation …
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
A Mathematical Programming Model For The Green Mixed Fleet Vehicle Routing Problem With Realistic Energy Consumption And Partial Recharges, Vincent F. Yu, Panca Jodiwan, Aldy Gunawan, Audrey Tedja Widjaja
Research Collection School Of Computing and Information Systems
A green mixed fleet vehicle routing with realistic energy consumption and partial recharges problem (GMFVRP-REC-PR) is addressed in this paper. This problem involves a fixed number of electric vehicles and internal combustion vehicles to serve a set of customers. The realistic energy consumption which depends on several variables is utilized to calculate the electricity consumption of an electric vehicle and fuel consumption of an internal combustion vehicle. Partial recharging policy is included into the problem to represent the real life scenario. The objective of this problem is to minimize the total travelled distance and the total emission produced by internal …
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Research Collection School Of Computing and Information Systems
A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method,change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches forrecommending refactorings that change software decomposition (such as a move method) do not explorethe use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First …
Digitalization In Practice: The Fifth Discipline Advantage, Siu Loon Hoe
Digitalization In Practice: The Fifth Discipline Advantage, Siu Loon Hoe
Research Collection School Of Computing and Information Systems
Purpose The purpose of this paper is to provide advice to organizations on how to become successful in the digital age. The paper revisits Peter Senge's (1990) notion of the learning organization and discusses the relevance of systems thinking and the other four disciplines, namely, personal mastery, mental models, shared vision and team learning in the context of the current digitalization megatrend. Design/methodology/approach This paper is based on content analysis of essays from international organizations, strategy experts and management scholars, and insights gained from the author's consulting experience. A comparative case study from the health and social sector is also …
Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang
Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang
Research Collection School Of Computing and Information Systems
With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation indus- try. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensi- tive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform …
Secure Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta
Secure Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Due to an increasing number of avenues for conducting cross-VM side-channel attacks, the security of multi-tenant public IaaS cloud environments is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. In this paper, we focus on secure VM placement algorithms which a cloud provider can use for the automatic enforcement of security against such co-location based attacks. To do so, we first establish a metric for evaluating and quantifying co-location security of multi-tenant public IaaS clouds, and then propose a novel VM placement …
Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions, Nur Camellia Binte Zakaria, Rajesh Balan, Youngki Lee
Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions, Nur Camellia Binte Zakaria, Rajesh Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Stress and depression are a common affliction in all walks of life. When left unmanaged, stress can inhibit productivity or cause depression. Depression can occur independently of stress. There has been a sharp rise in mobile health initiatives to monitor stress and depression. However, these initiatives usually require users to install dedicated apps or multiple sensors, making such solutions hard to scale. Moreover, they emphasise sensing individual factors and overlook social interactions, which plays a significant role in influencing stress and depression while being a part of a social system. We present StressMon, a stress and depression detection system that …
Explaining Regressions Via Alignment Slicing And Mending, Haijun Wang, Yun Lin, Zijiang Yang, Jun Sun, Yang Liu, Jinsong Dong, Qinghua Zheng, Ting Liu
Explaining Regressions Via Alignment Slicing And Mending, Haijun Wang, Yun Lin, Zijiang Yang, Jun Sun, Yang Liu, Jinsong Dong, Qinghua Zheng, Ting Liu
Research Collection School Of Computing and Information Systems
Regression faults, which make working code stop functioning, are often introduced when developers make changes to the software. Many regression fault localization techniques have been proposed. However, issues like inaccuracy and lack of explanation are still obstacles for their practical application. In this work, we propose a trace-based approach to identifying not only where the root cause of a regression bug lies, but also how the defect is propagated to its manifestation as the explanation. In our approach, we keep the trace of original correct version as reference and infer the faulty steps on the trace of regression version so …
Deep Hashing By Discriminating Hard Examples, Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, Edwin Hancock
Deep Hashing By Discriminating Hard Examples, Cheng Yan, Guansong Pang, Xiao Bai, Chunhua Shen, Jun Zhou, Edwin Hancock
Research Collection School Of Computing and Information Systems
This paper tackles a rarely explored but critical problem within learning to hash, i.e., to learn hash codes that effectively discriminate hard similar and dissimilar examples, to empower large-scale image retrieval. Hard similar examples refer to image pairs from the same semantic class that demonstrate some shared appearance but have different fine-grained appearance. Hard dissimilar examples are image pairs that come from different semantic classes but exhibit similar appearance. These hard examples generally have a small distance due to the shared appearance. Therefore, effective encoding of the hard examples can well discriminate the relevant images within a small Hamming distance, …
Generic Construction Of Elgamal-Type Attribute-Based Encryption Schemes With Revocability And Dual-Policy, Shengmin Xu, Yinghui Zhang, Yingjiu Li, Ximeng Liu, Guomin Yang
Generic Construction Of Elgamal-Type Attribute-Based Encryption Schemes With Revocability And Dual-Policy, Shengmin Xu, Yinghui Zhang, Yingjiu Li, Ximeng Liu, Guomin Yang
Research Collection School Of Computing and Information Systems
Cloud is a computing paradigm for allowing data owners to outsource their data to enjoy on-demand services and mitigate the burden of local data storage. However, secure sharing of data via cloud remains an essential issue since the cloud service provider is untrusted. Fortunately, asymmetric-key encryption, such as identity-based encryption (IBE) and attribute-based encryption (ABE), provides a promising tool to offer data confidentiality and has been widely applied in cloud-based applications. In this paper, we summarize the common properties of most of IBE and ABE and introduce a cryptographic primitive called ElGamal type cryptosystem. This primitive can be used to …
Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo
Why Reinventing The Wheels? An Empirical Study On Library Reuse And Re-Implementation, Bowen Xu, Le An, Ferdian Thung, Foutse Khomh, David Lo
Research Collection School Of Computing and Information Systems
Nowadays, with the rapid growth of open source software (OSS), library reuse becomes more and more popular since a large amount of third- party libraries are available to download and reuse. A deeper understanding on why developers reuse a library (i.e., replacing self-implemented code with an external library) or re-implement a library (i.e., replacing an imported external library with self-implemented code) could help researchers better understand the factors that developers are concerned with when reusing code. This understanding can then be used to improve existing libraries and API recommendation tools for researchers and practitioners by using the developers concerns identified …
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Efficient Distributed Reachability Querying Of Massive Temporal Graphs, Tianming Zhang, Yunjun Gao, Chen Lu, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen
Research Collection School Of Computing and Information Systems
Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper's study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed …
Confusion And Information Triggered By Photos In Persona Profiles, Joni Salminen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Lene Nielsen, Bernard J. Jansen
Confusion And Information Triggered By Photos In Persona Profiles, Joni Salminen, Soon-Gyo Jung, Jisun An, Haewoon Kwak, Lene Nielsen, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We investigate whether additional photos beyond a single headshot makes a persona profile more informative without confusing the end user. We conduct an eye-tracking experiment and qualitative interviews with digital content creators after varying the persona in photos via a single headshot, a headshot and photo of the persona in different contexts, and a headshot with photos of different people with key persona attributes the gender and age. Findings show that contextual photos provide significantly more persona information to end users; however, showing photos of multiple people engenders confusion and lowers informativeness. Also, as anticipated, viewing additional photos requires more …
Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang
Low-Rank Sparse Subspace For Spectral Clustering, Xiaofeng Zhu, Shichao Zhang, Yonggang Li, Jilian Zhang, Lifeng Yang, Yue Fang
Research Collection School Of Computing and Information Systems
The current two-step clustering methods separately learn the similarity matrix and conduct k means clustering. Moreover, the similarity matrix is learnt from the original data, which usually contain noise. As a consequence, these clustering methods cannot achieve good clustering results. To address these issues, this paper proposes a new graph clustering methods (namely Low-rank Sparse Subspace clustering (LSS)) to simultaneously learn the similarity matrix and conduct the clustering from the low-dimensional feature space of the original data. Specifically, the proposed LSS integrates the learning of similarity matrix of the original feature space, the learning of similarity matrix of the low-dimensional …
Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam
Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam
Research Collection School Of Computing and Information Systems
Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and …
Simulated Annealing For The Multi-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey Tedja Widjaja, Pieter Vansteenwegen
Simulated Annealing For The Multi-Vehicle Cyclic Inventory Routing Problem, Aldy Gunawan, Vincent F. Yu, Audrey Tedja Widjaja, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
This paper studies the Multi-Vehicle Cyclic Inventory Routing Problem (MV-CIRP) as the extension of the Single-Vehicle CIRP (SV-CIRP). The objective is to minimize both distribution and inventory costs at the customers and to maximize the collected rewards simultaneously. The problem is treated as a single objective optimization problem. A subset of customers is selected for each vehicle including the quantity to be delivered to each customer. For each vehicle, a cyclic distribution plan is developed. We construct a mathematical programming model and propose a simulated annealing (SA) metaheuristic for solving both SV-CIRP and MV-CIRP. For SV-CIRP, experimental results on benchmark …
Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang
Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Items adopted by a user over time are indicative ofthe underlying preferences. We are concerned withlearning such preferences from observed sequencesof adoptions for recommendation. As multipleitems are commonly adopted concurrently, e.g., abasket of grocery items or a sitting of media consumption, we deal with a sequence of baskets asinput, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of relateditems that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating informationon pairwise correlations among items would help toarrive at more coherent basket recommendations.Towards this objective, we develop a …
Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau
Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization Under Uncertainty, Jonathan David Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we …
Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau
Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of …
Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Zac: A Zone Path Construction Approach For Effective Real-Time Ridesharing, Meghna Lowalekar, Pradeep Varakantham, Patrick Jaillet
Research Collection School Of Computing and Information Systems
Real-time ridesharing systems such as UberPool, Lyft Line, GrabShare have become hugely popular as they reduce the costs for customers, improve per trip revenue for drivers and reduce traffic on the roads by grouping customers with similar itineraries. The key challenge in these systems is to group the right requests to travel in available vehicles in real-time, so that the objective (e.g., requests served, revenue or delay) is optimized. The most relevant existing work has focussed on generating as many relevant feasible (with respect to available delay for customers) combinations of requests (referred to as trips) as possible in real-time. …
Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau
Entropy Based Independent Learning In Anonymous Multi-Agent Settings, Tanvi Verma, Pradeep Varakantham, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Efficient sequential matching of supply and demand is a problem of interest in many online to offline services. For instance, Uber, Lyft, Grab for matching taxis to customers; Ubereats, Deliveroo, FoodPanda etc for matching restaurants to customers. In these online to offline service problems, individuals who are responsible for supply (e.g., taxi drivers, delivery bikes or delivery van drivers) earn more by being at the ”right” place at the ”right” time. We are interested in developing approaches that learn to guide individuals to be in the ”right” place at the ”right” time (to maximize revenue) in the presence of other …
The Impact Of Changes Mislabeled By Szz On Just-In-Time Defect Prediction, Yuanrui Fan, Xin Xia, Daniel A. Costa, David Lo, Ahmed E. Hassan, Shanping Li
The Impact Of Changes Mislabeled By Szz On Just-In-Time Defect Prediction, Yuanrui Fan, Xin Xia, Daniel A. Costa, David Lo, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
Just-in-Time (JIT) defect prediction—a technique which aims to predict bugs at change level—has been paid more attention. JIT defect prediction leverages the SZZ approach to identify bug-introducing changes. Recently, researchers found that the performance of SZZ (including its variants) is impacted by a large amount of noise. SZZ may considerably mislabel changes that are used to train a JIT defect prediction model, and thus impact the prediction accuracy. In this paper, we investigate the impact of the mislabeled changes by different SZZ variants on the performance and interpretation of JIT defect prediction models. We analyze four SZZ variants (i.e., B-SZZ, …
Model And Analysis Of Labor Supply For Ride-Sharing Platforms In The Presence Of Sample Self-Selection And Endogeneity, Hao Sun, Hai Wang, Zhixi Wan
Model And Analysis Of Labor Supply For Ride-Sharing Platforms In The Presence Of Sample Self-Selection And Endogeneity, Hao Sun, Hai Wang, Zhixi Wan
Research Collection School Of Computing and Information Systems
With the popularization of ride-sharing services, drivers working as freelancers on ride-sharing platforms can design their schedules flexibly. They make daily decisions regard- ing whether to participate in work, and if so, how many hours to work. Factors such as hourly income rate affect both the participation decision and working-hour decision, and evaluation of the impacts of hourly income rate on labor supply becomes important. In this paper, we propose an econometric framework with closed-form measures to estimate both the participation elasticity (i.e., extensive margin elasticity) and working-hour elasticity (i.e., intensive margin elasticity) of labor supply. We model the sample …
Matching Passengers And Drivers With Multiple Objectives In Ride Sharing Markets, Guodong Lyu, Chung Piaw Teo, Wangchi Cheung, Hai Wang
Matching Passengers And Drivers With Multiple Objectives In Ride Sharing Markets, Guodong Lyu, Chung Piaw Teo, Wangchi Cheung, Hai Wang
Research Collection School Of Computing and Information Systems
In many cities in the world, ride sharing companies, such as Uber, Didi, Grab and Lyft, have been able to leverage on Internet-based platforms to conduct online decision making to connect passengers and drivers. These online platforms facilitate the integration of passengers and drivers’ mobility data on smart phones in real-time, which enables a convenient matching between demand and supply in real time. These clear operational advantages have motivated many similar shared service business models in the public transportation arena, and have been a disruptive force to the traditional taxi industry.
Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis
Geometric Top-K Processing: Updates Since Mdm'16 [Advanced Seminar], Kyriakos Mouratidis
Research Collection School Of Computing and Information Systems
The top-k query has been studied extensively, and is considered the norm for multi-criteria decision making in large databases. In recent years, research has considered several complementary operators to the traditional top-k query, drawing inspiration (both in terms of problem formulation and solution design) from the geometric nature of the top-k processing model. In this seminar, we will present advances in that stream of work, focusing on updates since the preliminary seminar on the same topic in MDM'16.
Single Image Reflection Removal Beyond Linearity, Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, Guoqiang Han, Shengfeng He
Single Image Reflection Removal Beyond Linearity, Qiang Wen, Yinjie Tan, Jing Qin, Wenxi Liu, Guoqiang Han, Shengfeng He
Research Collection School Of Computing and Information Systems
Due to the lack of paired data, the training of image reflection removal relies heavily on synthesizing reflection images. However, existing methods model reflection as a linear combination model, which cannot fully simulate the real-world scenarios. In this paper, we inject non-linearity into reflection removal from two aspects. First, instead of synthesizing reflection with a fixed combination factor or kernel, we propose to synthesize reflection images by predicting a non-linear alpha blending mask. This enables a free combination of different blurry kernels, leading to a controllable and diverse reflection synthesis. Second, we design a cascaded network for reflection removal with …
Metagraph-Based Learning On Heterogeneous Graphs, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Jiaqi Shi, Kevin Chang, Xiao-Li Li
Metagraph-Based Learning On Heterogeneous Graphs, Yuan Fang, Wenqing Lin, Vincent W. Zheng, Min Wu, Jiaqi Shi, Kevin Chang, Xiao-Li Li
Research Collection School Of Computing and Information Systems
Data in the form of graphs are prevalent, ranging from biological and social networks to citation graphs and the Web. Inparticular, most real-world graphs are heterogeneous, containing objects of multiple types, which present new opportunities for manyproblems on graphs. Consider a typical proximity search problem on graphs, which boils down to measuring the proximity between twogiven nodes. Most earlier studies on homogeneous or bipartite graphs only measure a generic form of proximity, without accounting fordifferent “semantic classes”—for instance, on a social network two users can be close for different reasons, such as being classmates orfamily members, which represent two distinct …
Coordinating Supply And Demand On An On-Demand Service Platform With Impatient Customers, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang
Coordinating Supply And Demand On An On-Demand Service Platform With Impatient Customers, Jiaru Bai, Kut C. So, Christopher S. Tang, Xiqun Chen, Hai Wang
Research Collection School Of Computing and Information Systems
We consider an on-demand service platform using earning sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price sensitive customers with heterogeneous valuation of the service. As such, both the supply and demand are "endogenously'' dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a …
R2gan: Cross-Modal Recipe Retrieval With Generative Adversarial Network, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Yanbin Hao
R2gan: Cross-Modal Recipe Retrieval With Generative Adversarial Network, Bin Zhu, Chong-Wah Ngo, Jingjing Chen, Yanbin Hao
Research Collection School Of Computing and Information Systems
Representing procedure text such as recipe for crossmodal retrieval is inherently a difficult problem, not mentioning to generate image from recipe for visualization. This paper studies a new version of GAN, named Recipe Retrieval Generative Adversarial Network (R2GAN), to explore the feasibility of generating image from procedure text for retrieval problem. The motivation of using GAN is twofold: learning compatible cross-modal features in an adversarial way, and explanation of search results by showing the images generated from recipes. The novelty of R2GAN comes from architecture design, specifically a GAN with one generator and dual discriminators is used, which makes the …
Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham
Worker Demographics And Earnings On Amazon Mechanical Turk: An Exploratory Analysis, Kotaro Hara, Kristy Milland, Benjamin V. Hanrahan, Chris Callison-Burch, Abigail Adams, Saiph Savage, Jeffrey P. Bigham
Research Collection School Of Computing and Information Systems
Prior research reported that workers on Amazon Mechanical Turk (AMT) are underpaid, earning about $2/h. But the prior research did not investigate the difference in wage due to worker characteristics (e.g., country of residence). We present the first data-driven analysis on wage gap on AMT. Using work log data and demographic data collected via online survey, we analyse the gap in wage due to different factors. We show that there is indeed wage gap; for example, workers in the U.S. earn $3.01/h while those in India earn $1.41/h on average.