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Research Collection School Of Computing and Information Systems

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Prediction Of Relatedness In Stack Overflow: Deep Learning Vs. Svm: A Reproducibility Study, Bowen Xu, Amirreza Shirani, David Lo, Mohammad Amin Alipour Oct 2018

Prediction Of Relatedness In Stack Overflow: Deep Learning Vs. Svm: A Reproducibility Study, Bowen Xu, Amirreza Shirani, David Lo, Mohammad Amin Alipour

Research Collection School Of Computing and Information Systems

Background Xu et al. used a deep neural network (DNN) technique to classify the degree of relatedness between two knowledge units (question-answer threads) on Stack Overflow. More recently, extending Xu et al.'s work, Fu and Menzies proposed a simpler classification technique based on a fine-tuned support vector machine (SVM) that achieves similar performance but in a much shorter time. Thus, they suggested that researchers need to compare their sophisticated methods against simpler alternatives.Aim The aim of this work is to replicate the previous studies and further investigate the validity of Fu and Menzies' claim by evaluating the DNN- and SVM-based …

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Investigating Multimodal Affect Sensing In An Affective Tutoring System Using Unobtrusive Sensors, Hua Leong Fwa, Lindsay Marshall Oct 2018

Investigating Multimodal Affect Sensing In An Affective Tutoring System Using Unobtrusive Sensors, Hua Leong Fwa, Lindsay Marshall

Research Collection School Of Computing and Information Systems

Affect inextricably plays a critical role in the learning process. In this study, we investigate the multimodal fusion of facial, keystrokes, mouse clicks, head posture and contextual features for the detection of student’s frustration in an Affective Tutoring System. The results (AUC=0.64) demonstrated empirically that a multimodal approach offers higher accuracy and better robustness as compared to a unimodal approach. In addition, the inclusion of keystrokes and mouse clicks makes up for the detection gap where video based sensing modes (facial and head postures) are not available. The findings in this paper will dovetail to our end research objective of …

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Optimal In-Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo Oct 2018

Optimal In-Place Suffix Sorting, Zhize Li, Jian Li, Hongwei Huo

Research Collection School Of Computing and Information Systems

The suffix array is a fundamental data structure for many applications that involve string searching and data compression. Designing time/space-efficient suffix array construction algorithms has attracted significant attentions and considerable advances have been made for the past 20 years. We obtain the first in-place linear time suffix array construction algorithms that are optimal both in time and space for (read-only) integer alphabets. Our algorithm settles the open problem posed by Franceschini and Muthukrishnan in ICALP 2007. The open problem asked to design in-place algorithms in $o(n \log n)$ time and ultimately, in $O(n)$ time for (read-only) integer alphabets with $|\Sigma| …

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Predicting Visual Context For Unsupervised Event Segmentation In Continuous Photo-Streams, Ana García Del Molino, Joo-Hwee Lim, Ah-Hwee Tan Oct 2018

Predicting Visual Context For Unsupervised Event Segmentation In Continuous Photo-Streams, Ana García Del Molino, Joo-Hwee Lim, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them in an unsupervised way. However, such methodologies are ineffective to deal with heterogeneous events, e.g. taking a walk, and temporary changes in the sight direction, e.g. at a meeting. To address these limitations, we propose Contextual Event Segmentation (CES), a novel segmentation paradigm that uses an LSTM-based generative network to model the photo-stream sequences, predict their …

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Mini-Case Study Pedagogy: Experience From A Technical Course In An Information Systems Program, Venky Shankararaman, Swapna Gottipati, Alan Megargel Oct 2018

Mini-Case Study Pedagogy: Experience From A Technical Course In An Information Systems Program, Venky Shankararaman, Swapna Gottipati, Alan Megargel

Research Collection School Of Computing and Information Systems

Case method is one approach that has been widely adopted in teaching many professions including law, medicine and business. The general practice is to ask students to read the case, usually four to eight pages long, prior to the session, and then spend the whole session discussing the case. However, for technology courses, the students are required to learn concepts and apply the musing hands-on software tools through lab sessions. Hence, rather than use long cases, that cover the entire session, we need to use mini-cases that are usually one to two pages long. This pedagogy approach allows the class …

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Sufat: An Analytics Tool For Gaining Insights From Student Feedback Comments, Siddhant Pyasi, Swapna Gottipati, Venky Shankararaman Oct 2018

Sufat: An Analytics Tool For Gaining Insights From Student Feedback Comments, Siddhant Pyasi, Swapna Gottipati, Venky Shankararaman

Research Collection School Of Computing and Information Systems

Teacher evaluation is a vital element inimproving student learning outcomes. Course and instructor feedback given bystudents, provides insights that can help improve student learning outcomes andteaching quality. Teaching and course evaluation systems help to collectquantitative and qualitative feedback from students. Since manually analysingthe qualitative feedback is painstaking and a tedious process, usually, onlythe quantitative feedback is often used for evaluating the course and theinstructor. However, useful knowledge is hidden in the qualitative comments, inthe form of sentiments and suggestions that can provide valuable insights tohelp plan improvements in the course content and delivery. In order toefficiently gather, analyse and provide …

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Multiperspective Graph-Theoretic Similarity Measure, Dung D. Le, Hady W. Lauw Oct 2018

Multiperspective Graph-Theoretic Similarity Measure, Dung D. Le, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Determining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of object-to-object relationships, it is natural to rely on graph-theoretic measures. One seminal technique for measuring the structural-context similarity between a pair of graph vertices is SimRank, whose underlying intuition is that two objects are similar if they are connected by similar objects. However, by design, SimRank as well as its variants capture only a single view or perspective of similarity. Meanwhile, in many real-world scenarios, there emerge multiple perspectives of similarity, i.e., two objects may be similar from one perspective, …

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Traffic-Cascade: Mining And Visualizing Lifecycles Of Traffic Congestion Events Using Public Bus Trajectories, Agus Trisnajaya Kwee, Meng-Fen Chiang, Philips Kokoh Prasetyo, Ee-Peng Lim Oct 2018

Traffic-Cascade: Mining And Visualizing Lifecycles Of Traffic Congestion Events Using Public Bus Trajectories, Agus Trisnajaya Kwee, Meng-Fen Chiang, Philips Kokoh Prasetyo, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

As road transportation supports both economic and social activities in developed cities, it is important to maintain smooth traffic on all highways and local roads. Whenever possible, traffic congestions should be detected early and resolved quickly. While existing traffic monitoring dashboard systems have been put in place in many cities, these systems require high-cost vehicle speed monitoring instruments and detect traffic congestion as independent events. There is a lack of low-cost dashboards to inspect and analyze the lifecycle of traffic congestion which is critical in assessing the overall impact of congestion, determining the possible the source(s) of congestion and its …

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A Learning And Masking Approach To Secure Learning, Linh Nguyen, Sky Wang, Arunesh Sinha Oct 2018

A Learning And Masking Approach To Secure Learning, Linh Nguyen, Sky Wang, Arunesh Sinha

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have been shown to be vulnerable against adversarial examples, which are data points cleverly constructed to fool the classifier. Such attacks can be devastating in practice, especially as DNNs are being applied to ever increasing critical tasks like image recognition in autonomous driving. In this paper, we introduce a new perspective on the problem. We do so by first defining robustness of a classifier to adversarial exploitation. Next, we show that the problem of adversarial example generation can be posed as learning problem. We also categorize attacks in literature into high and low perturbation attacks; well-known …

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Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua Oct 2018

Knowledge-Aware Multimodal Dialogue Systems, Lizi Liao, Yunshan Ma, Xiangnan He, Richang Hong, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

By offering a natural way for information seeking, multimodal dialogue systems are attracting increasing attention in several domains such as retail, travel etc. However, most existing dialogue systems are limited to textual modality, which cannot be easily extended to capture the rich semantics in visual modality such as product images. For example, in fashion domain, the visual appearance of clothes and matching styles play a crucial role in understanding the user's intention. Without considering these, the dialogue agent may fail to generate desirable responses for users. In this paper, we present a Knowledge-aware Multimodal Dialogue (KMD) model to address the …

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Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen Oct 2018

Geometry-Aware Similarity Learning On Spd Manifolds For Visual Recognition, Zhiwu Huang, R. Wang, X. Li, W. Liu, S. Shan, Gool L. Van, X Chen

Research Collection School Of Computing and Information Systems

Symmetric positive definite (SPD) matrices have been employed for data representation in many visual recognition tasks. The success is mainly attributed to learning discriminative SPD matrices encoding the Riemannian geometry of the underlying SPD manifolds. In this paper, we propose a geometry-aware SPD similarity learning (SPDSL) framework to learn discriminative SPD features by directly pursuing a manifold-manifold transformation matrix of full column rank. Specifically, by exploiting the Riemannian geometry of the manifolds of fixed-rank positive semidefinite (PSD) matrices, we present a new solution to reduce optimization over the space of column full-rank transformation matrices to optimization on the PSD manifold, …

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Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua Oct 2018

Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its …

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Influence Maximization On Social Graphs: A Survey, Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan Oct 2018

Influence Maximization On Social Graphs: A Survey, Yuchen Li, Ju Fan, Yanhao Wang, Kian-Lee Tan

Research Collection School Of Computing and Information Systems

Influence Maximization (IM), which selects a set of k users (called seed set) from a social network to maximize the expected number of influenced users (called influence spread), is a key algorithmic problem in social influence analysis. Due to its immense application potential and enormous technical challenges, IM has been extensively studied in the past decade. In this paper, we survey and synthesize a wide spectrum of existing studies on IM from an algorithmic perspective, with a special focus on the following key aspects (1) a review of well-accepted diffusion models that capture information diffusion process and build the foundation …

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Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua Oct 2018

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

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Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua Oct 2018

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

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Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua Oct 2018

Interpretable Multimodal Retrieval For Fashion Products, Lizi Liao, Xiangnan He, Bo Zhao, Chong-Wah Ngo, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Deep learning methods have been successfully applied to fashion retrieval. However, the latent meaning of learned feature vectors hinders the explanation of retrieval results and integration of user feedback. Fortunately, there are many online shopping websites organizing fashion items into hierarchical structures based on product taxonomy and domain knowledge. Such structures help to reveal how human perceive the relatedness among fashion products. Nevertheless, incorporating structural knowledge for deep learning remains a challenging problem. This paper presents techniques for organizing and utilizing the fashion hierarchies in deep learning to facilitate the reasoning of search results and user intent. The novelty of …

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Self-Supervised Feature Learning For Semantic Segmentation Of Overhead Imagery, Suriya Singh, Anil Batra, Guansong Pang, Lorenzo Torresani, Saikat Basu, Manohar Paluri, C. V. Jawahar Sep 2018

Self-Supervised Feature Learning For Semantic Segmentation Of Overhead Imagery, Suriya Singh, Anil Batra, Guansong Pang, Lorenzo Torresani, Saikat Basu, Manohar Paluri, C. V. Jawahar

Research Collection School Of Computing and Information Systems

Overhead imageries play a crucial role in many applications such as urban planning, crop yield forecasting, mapping, and policy making. Semantic segmentation could enable automatic, efficient, and large-scale understanding of overhead imageries for these applications. However, semantic segmentation of overhead imageries is a challenging task, primarily due to the large domain gap from existing research in ground imageries, unavailability of large-scale dataset with pixel-level annotations, and inherent complexity in the task. Readily available vast amount of unlabeled overhead imageries share more common structures and patterns compared to the ground imageries, therefore, its large-scale analysis could benefit from unsupervised feature learning …

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Challenges In Learning Uml: From The Perspective Of Diagrammatic Representation And Reasoning, Z. Shen, S. Tan, Keng Siau Sep 2018

Challenges In Learning Uml: From The Perspective Of Diagrammatic Representation And Reasoning, Z. Shen, S. Tan, Keng Siau

Research Collection School Of Computing and Information Systems

Unified modeling language (UML) is widely taught in the information systems (IS) curriculum. To understand UML in IS education, this paper reports on an empirical study that taps into students’ learning of UML. The study uses a concept-mapping technique to identify the challenges in learning UML notational elements. It reveals that some technical properties of UML diagrammatic representation, coupled with students’ cognitive attributes, hinder both perceptual and conceptual processes involved in searching, recognizing, and inferring visual information, which creates learning barriers. This paper also discusses how to facilitate perceptual and conceptual processes in instruction to overcome learning challenges. The study …

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A Strategic Value Appropriation Path For Cloud Computing, Abhishek Kathuria, Arti Mann, Jiban Khuntia, Robert J. Kauffman Sep 2018

A Strategic Value Appropriation Path For Cloud Computing, Abhishek Kathuria, Arti Mann, Jiban Khuntia, Robert J. Kauffman

Research Collection School Of Computing and Information Systems

Cloud-based information management is one of the leading competitive differentiation strategies for firms. With the increasing criticality of information management in value creation and process support, establishing an integrated capability with cloud computing is vital for organizational success in the changing landscape of business competition. These issues have received scant attention, however. We draw on the resource-based view, dynamic capability hierarchy concepts, and the perspective of operand and operant resources to suggest a cloud value appropriation model for firms. We argue that, to appropriate business value from cloud computing, the firm needs to effectively deploy cloud computing and leverage cloud …

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Implicit Linking Of Food Entities In Social Media, Wen Haw Chong, Ee Peng Lim Sep 2018

Implicit Linking Of Food Entities In Social Media, Wen Haw Chong, Ee Peng Lim

Research Collection School Of Computing and Information Systems

Dining is an important part in people’s lives and this explains why food-related microblogs and reviews are popular in social media. Identifying food entities in food-related posts is important to food lover profiling and food (or restaurant) recommendations. In this work, we conduct Implicit Entity Linking (IEL) to link food-related posts to food entities in a knowledge base. In IEL, we link posts even if they do not contain explicit entity mentions. We first show empirically that food venues are entity-focused and associated with a limited number of food entities each. Hence same-venue posts are likely to share common food …

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Talent Flow Analytics In Online Professional Network, Richard J. Oentaryo, Ee-Peng Lim, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo Sep 2018

Talent Flow Analytics In Online Professional Network, Richard J. Oentaryo, Ee-Peng Lim, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

Analyzing job hopping behavior is important for understanding job preference and career progression of working individuals. When analyzed at the workforce population level, job hop analysis helps to gain insights of talent flow among different jobs and organizations. Traditionally, surveys are conducted on job seekers and employers to study job hop behavior. Beyond surveys, job hop behavior can also be studied in a highly scalable and timely manner using a data-driven approach in response to fast-changing job landscape. Fortunately, the advent of online professional networks (OPNs) has made it possible to perform a large-scale analysis of talent flow. In this …

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Jobcomposer: Career Path Optimization Via Multicriteria Utility Learning, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo Sep 2018

Jobcomposer: Career Path Optimization Via Multicriteria Utility Learning, Richard J. Oentaryo, Xavier Jayaraj Siddarth Ashok, Ee-Peng Lim, Philips Kokoh Prasetyo

Research Collection School Of Computing and Information Systems

With online professional network platforms (OPNs, e.g., LinkedIn, Xing, etc.)becoming popular on the web, people are now turning to these platforms tocreate and share their professional profiles, to connect with others who sharesimilar professional aspirations and to explore new career opportunities. Theseplatforms however do not offer a long-term roadmap to guide career progressionand improve workforce employability. The career trajectories of OPN users canserve as a reference but they are not always optimal. A career plan can also bedevised through consultation with career coaches, whose knowledge may howeverbe limited to a few industries. To address the above limitations, we present anovel …

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Are You On The Right Track? Learning Career Tracks For Job Movement Analysis, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Yuan Tian, Chih-Chieh Hung Sep 2018

Are You On The Right Track? Learning Career Tracks For Job Movement Analysis, Meng-Fen Chiang, Ee-Peng Lim, Wang-Chien Lee, Yuan Tian, Chih-Chieh Hung

Research Collection School Of Computing and Information Systems

Career track represents a vertical career pathway, where one can gradually move up to take up higher job appointments when relevant skills are acquired. Understanding the propensity of career movements in an evolving job market can enable timely career guidance to job seekers and working professionals. To this end, we harvest career trajectories from online professional network (OPN). Our focus lies on obtaining a macro view on career movements at the track granularity. Specifically, we propose a semi-supervised career track labelling framework to automatically assign career tracks for large set of jobs. To contextually label jobs, we collect example jobs …

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Assessing Carbon Pollution Standards: Electric Power Generation Pathways And Their Water Impacts, Kustini Lim-Wavde, Haibo Zhai, Robert John Kauffman, Edward S. Rubin Sep 2018

Assessing Carbon Pollution Standards: Electric Power Generation Pathways And Their Water Impacts, Kustini Lim-Wavde, Haibo Zhai, Robert John Kauffman, Edward S. Rubin

Research Collection School Of Computing and Information Systems

Highlights•Without carbon regulations, CO2 emissions and water use are highly affected by fuel prices.•Carbon regulations reduce both CO2 emissions and water use.•Without incentives, carbon capture is not competitive with cheap natural gas and renewables.•A stringent constraint on water withdrawal lowers the fleet share of once-through cooling.AbstractThis study evaluates transition pathways in electricity generation and their future water impacts. Scenarios that do or do not comply with the carbon pollution standards – based on the U.S. New Source Performance Standards and Clean Power Plan – are evaluated. Using the Electric Reliability Council of Texas region as an illustration, the scenarios with …

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A Hybrid Model For Identity Obfuscation By Face Replacement, Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele Sep 2018

A Hybrid Model For Identity Obfuscation By Face Replacement, Qianru Sun, Ayush Tewari, Weipeng Xu, Mario Fritz, Christian Theobalt, Bernt Schiele

Research Collection School Of Computing and Information Systems

As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition, becomes increasingly challenging. We propose a new hybrid approach to obfuscate identities in photos by head replacement. Our approach combines state of the art parametric face synthesis with latest advances in Generative Adversarial Networks (GAN) for data-driven image synthesis. On the one hand, the parametric part of our method gives us control over the facial parameters and allows for explicit manipulation of the identity. On the other hand, the data-driven aspects allow for adding fine details and overall realism as …

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Cognitive Antecedents Of Family Business Bias In Investment Decisions: A Commentary On 'Risky Decisions And The Family Firm Bias: An Experimental Study Based On Prospect Theory, H. Fang, Keng Siau, E. Memili, J. Dou Sep 2018

Cognitive Antecedents Of Family Business Bias In Investment Decisions: A Commentary On 'Risky Decisions And The Family Firm Bias: An Experimental Study Based On Prospect Theory, H. Fang, Keng Siau, E. Memili, J. Dou

Research Collection School Of Computing and Information Systems

Lude and Prügl explored “family business bias,” a cognitive tendency where the family nature of a firm can often reduce investors’ perceived risk in investments. As a result, investors would display lower risk-avoidance in the gain domain and reinforced risk-seeking in the loss domain. We expanded the authors’ work by introducing four cognitive factors (anchoring, representativeness, stereotype heuristic, and information availability) that can explain the underlying mechanisms behind the prevalence of “family business bias” and other cognitive misperceptions surrounding family businesses when it comes to investment decisions.

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Wasserstein Divergence For Gans, J. Wu, Zhiwu Huang, J. Thoma, D. Acharya, Gool L. Van Sep 2018

Wasserstein Divergence For Gans, J. Wu, Zhiwu Huang, J. Thoma, D. Acharya, Gool L. Van

Research Collection School Of Computing and Information Systems

In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state-of-the-art due to the theoretical contributions and competitive qualitative performance. However, it is very challenging to approximate the k-Lipschitz constraint required by the Wasserstein-1 metric (W-met). In this paper, we propose a novel Wasserstein divergence (W-div), which is a relaxed version of W-met and does not require the k-Lipschitz constraint. As a concrete application, we introduce a Wasserstein divergence objective for GANs (WGAN-div), which can faithfully approximate W-div through optimization. Under various settings, including …

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Question-Guided Hybrid Convolution For Visual Question Answering, Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven C. H. Hoi, Xiaogang Wang Sep 2018

Question-Guided Hybrid Convolution For Visual Question Answering, Peng Gao, Pan Lu, Hongsheng Li, Shuang Li, Yikang Li, Steven C. H. Hoi, Xiaogang Wang

Research Collection School Of Computing and Information Systems

In this paper, we propose a novel Question-Guided Hybrid Convolution (QGHC) network for Visual Question Answering (VQA). Most state-of-the-art VQA methods fuse the high-level textual and visual features from the neural network and abandon the visual spatial information when learning multi-modal features.To address these problems, question-guided kernels generated from the input question are designed to convolute with visual features for capturing the textual and visual relationship in the early stage. The question-guided convolution can tightly couple the textual and visual information but also introduce more parameters when learning kernels. We apply the group convolution, which consists of question-independent kernels and …

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Diversity In Online Advertising: A Case Study Of 69 Brands On Social Media, Jisun An, Ingmar Weber Sep 2018

Diversity In Online Advertising: A Case Study Of 69 Brands On Social Media, Jisun An, Ingmar Weber

Research Collection School Of Computing and Information Systems

Lack of diversity in advertising is a long-standing problem. Despite growing cultural awareness and missed business opportunities, many minorities remain under- or inappropriately represented in advertising. Previous research has studied how people react to culturally embedded ads, but such work focused mostly on print media or television using lab experiments. In this work, we look at diversity in content posted by 69 U.S. brands on two social media platforms, Instagram and Facebook. Using face detection technology, we infer the gender, race, and age of both the faces in the ads and of the users engaging with ads. Using this dataset, …

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A Two-Stage Mechanism For Ordinal Peer Assessment, Zhize Li, Le Zhang, Zhixuan Fang, Jian Li Sep 2018

A Two-Stage Mechanism For Ordinal Peer Assessment, Zhize Li, Le Zhang, Zhixuan Fang, Jian Li

Research Collection School Of Computing and Information Systems

Peer assessment is a major method for evaluating the performance of employee, accessing the contributions of individuals within a group, making social decisions and many other scenarios. The idea is to ask the individuals of the same group to assess the performance of the others. Scores or rankings are then determined based on these evaluations. However, peer assessment can be biased and manipulated, especially when there is a conflict of interests. In this paper, we consider the problem of eliciting the underlying ordering (i.e. ground truth) of n strategic agents with respect to their performances, e.g., quality of work, contributions, …

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