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The Virtual Reality Questionnaire Toolkit, Martin Feick, Niko Kleer, Anthony Tang, Anthonio Kruger Oct 2020

The Virtual Reality Questionnaire Toolkit, Martin Feick, Niko Kleer, Anthony Tang, Anthonio Kruger

Research Collection School Of Computing and Information Systems

In this work, we present the VRQuestionnaire Toolkit, which enables the research community to easily collect subjective measures within virtual reality (VR). We contribute a highly customizable and reusable open-source toolkit which can be integrated in existing VR projects rapidly. The toolkit comes with a pre-installed set of standard questionnaires such as NASA TLX, SSQ and SUS Presence questionnaire. Our system aims to lower the entry barrier to use questionnaires in VR and to significantly reduce development time and cost needed to run pre-, in between- and post-study questionnaires.

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Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura Oct 2020

Zoomwalls: Dynamic Walls That Simulate Haptic Infrastructure For Room-Scale Vr World, Yan Yixian, Kazuki Takashima, Anthony Tang, Takayuki Tanno, Kazuyuki Fujita, Yoshifumi Kitamura

Research Collection School Of Computing and Information Systems

We focus on the problem of simulating the haptic infrastructure of a virtual environment (i.e. walls, doors). Our approach relies on multiple ZoomWalls---autonomous robotic encounter-type haptic wall-shaped props---that coordinate to provide haptic feedback for room-scale virtual reality. Based on a user's movement through the physical space, ZoomWall props are coordinated through a predict-and-dispatch architecture to provide just-in-time haptic feedback for objects the user is about to touch. To refine our system, we conducted simulation studies of different prediction algorithms, which helped us to refine our algorithmic approach to realize the physical ZoomWall prototype. Finally, we evaluated our system through a …

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The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller Oct 2020

The Future Of Work Now: Automl At 84.51°And Kroger, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon.

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Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan Oct 2020

Did Our Course Design On Software Architecture Meet Our Student’S Learning Expectations?, Eng Lieh Ouh, Benjamin Gan, Yunghans Irawan

Research Collection School Of Computing and Information Systems

This Innovative Practice Full Paper discusses our course design on software architecture to meet the learning expectations of two groups of software engineers. Software engineers with working experiences frequently find themselves the need to upskill in their lifelong learning journey. Their learning expectations are shaped not just by their need to know but also other learning characteristics such as their working experiences. In many cases, we design courses based on the required learning outcomes and assessment criteria. In this paper, we wish to find out whether our course design on software architecture has met the learning expectations of our students …

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Presentation Of Computer Security Risk Information: Impact Of Framing And Base Size, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Richard Hall, Maggie Cheng Oct 2020

Presentation Of Computer Security Risk Information: Impact Of Framing And Base Size, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Richard Hall, Maggie Cheng

Research Collection School Of Computing and Information Systems

This research explores how the presentation of computer security risks impacts users’ risk perceptions and behavior. It draws on Prospect Theory to generate hypotheses related to users’ decision-making in the computer security context. A 2 × 3 mixed factorial experimental design (N = 178) was carried out and the results show that framing and base size of information on computer security risks influence users’ perceived risk and risk-taking behavior. More specifically, negative framing and large base size increase users’ perceived risk and reduce users’ risk-taking behavior. The findings from this research suggest that using negative framing and large base size …

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Meta-Rcnn: Meta Learning For Few-Shot Object Detection, Xiongwei Wu, Doyen Sahoo, Steven Hoi Oct 2020

Meta-Rcnn: Meta Learning For Few-Shot Object Detection, Xiongwei Wu, Doyen Sahoo, Steven Hoi

Research Collection School Of Computing and Information Systems

Despite significant advances in deep learning based object detection in recent years, training effective detectors in a small data regime remains an open challenge. This is very important since labelling training data for object detection is often very expensive and time-consuming. In this paper, we investigate the problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the meta-learning principle, we propose a new meta-learning framework for object detection named "Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning …

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Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He Oct 2020

Two-Stage Photograph Cartoonization Via Line Tracing, Simin Li, Qiang Wen, Shuang Zhao, Zixun Sun, Shengfeng He

Research Collection School Of Computing and Information Systems

Cartoon is highly abstracted with clear edges, which makes it unique from the other art forms. In this paper, we focus on the essential cartoon factors of abstraction and edges, aiming to cartoonize real-world photographs like an artist. To this end, we propose a two-stage network, each stage explicitly targets at producing abstracted shading and crisp edges respectively. In the first abstraction stage, we propose a novel unsupervised bilateral flattening loss, which allows generating high-quality smoothing results in a label-free manner. Together with two other semantic-aware losses, the abstraction stage imposes different forms of regularization for creating cartoon-like flattened images. …

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What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino Oct 2020

What Is The Vocabulary Of Flaky Tests?, Gustavo Pinto, Breno Miranda, Supun Dissanayake, Marcelo D'Amorim, Christoph Treude, Antonia Bertolino

Research Collection School Of Computing and Information Systems

Flaky tests are tests whose outcomes are non-deterministic. Despite the recent research activity on this topic, no effort has been made on understanding the vocabulary of flaky tests. This work proposes to automatically classify tests as flaky or not based on their vocabulary. Static classification of flaky tests is important, for example, to detect the introduction of flaky tests and to search for flaky tests after they are introduced in regression test suites. We evaluated performance of various machine learning algorithms to solve this problem. We constructed a data set of flaky and non-flaky tests by running every test case, …

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Message From The General Co-Chairs And The Program Co-Chairs, Christoph Treude, Hongyu Zhang, Kelly Blincoe, Zhenchang Xing Oct 2020

Message From The General Co-Chairs And The Program Co-Chairs, Christoph Treude, Hongyu Zhang, Kelly Blincoe, Zhenchang Xing

Research Collection School Of Computing and Information Systems

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

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Federated Topic Discovery: A Semantic Consistent Approach, Yexuan Shi, Yongxin Tong, Zhiyang Su, Di Jiang, Zimu Zhou, Wenbin Zhang Oct 2020

Federated Topic Discovery: A Semantic Consistent Approach, Yexuan Shi, Yongxin Tong, Zhiyang Su, Di Jiang, Zimu Zhou, Wenbin Zhang

Research Collection School Of Computing and Information Systems

General-purpose topic models have widespread industrial applications. Yet high-quality topic modeling is becoming increasingly challenging because accurate models require large amounts of training data typically owned by multiple parties, who are often unwilling to share their sensitive data for collaborative training without guarantees on their data privacy. To enable effective privacy-preserving multiparty topic modeling, we propose a novel federated general-purpose topic model named private and consistent topic discovery (PC-TD). On the one hand, PC-TD seamlessly integrates differential privacy in topic modeling to provide privacy guarantees on sensitive data of different parties. On the other hand, PC-TD exploits multiple sources of …

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Multi-Modal Cooking Workflow Construction For Food Recipes, Liangming Pan, Jingjing Chen, Jianlong Wu, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Yugang Jiang, Tat-Seng Chua Oct 2020

Multi-Modal Cooking Workflow Construction For Food Recipes, Liangming Pan, Jingjing Chen, Jianlong Wu, Shaoteng Liu, Chong-Wah Ngo, Min-Yen Kan, Yugang Jiang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe. This is a non-trivial task that involves common-sense reasoning. However, existing efforts rely on hand-crafted features to extract the workflow graph from recipes due to the lack of large-scale labeled datasets. Moreover, they fail to utilize the cooking images, which constitute an important part of food recipes. In this paper, we build MM-ReS, the first large-scale dataset for cooking workflow construction, consisting of 9,850 recipes with human-labeled workflow graphs. Cooking steps …

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Compact Bilinear Augmented Query Structured Attention For Sport Highlights Classification, Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Qing Liu, Xiaojun Hu Oct 2020

Compact Bilinear Augmented Query Structured Attention For Sport Highlights Classification, Yanbin Hao, Hao Zhang, Chong-Wah Ngo, Qing Liu, Xiaojun Hu

Research Collection School Of Computing and Information Systems

Understanding fine-grained activities, such as sport highlights, is a problem being overlooked and receives considerably less research attention. Potential reasons include absences of specific fine-grained action benchmark datasets, research preferences to general supercategorical activities classification, and challenges of large visual similarities between fine-grained actions. To tackle these, we collect and manually annotate two sport highlights datasets, i.e., Basketball8 & Soccer-10, for fine-grained action classification. Sample clips in the datasets are annotated with professional sub-categorical actions like “dunk”, “goalkeeping” and etc. We also propose a Compact Bilinear Augmented Query Structured Attention (CBA-QSA) module and stack it on top of general three-dimensional …

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Cross-Domain Cross-Modal Food Transfer, Bin Zhu, Chong-Wah Ngo, Jingjing Chen Oct 2020

Cross-Domain Cross-Modal Food Transfer, Bin Zhu, Chong-Wah Ngo, Jingjing Chen

Research Collection School Of Computing and Information Systems

The recent works in cross-modal image-to-recipe retrieval pave a new way to scale up food recognition. By learning the joint space between food images and recipes, food recognition is boiled down as a retrieval problem by evaluating the similarity of embedded features. The major drawback, nevertheless, is the difficulty in applying an already-trained model to recognize different cuisines of dishes unknown to the model. In general, model updating with new training examples, in the form of image-recipe pairs, is required to adapt a model to new cooking styles in a cuisine. Nevertheless, in practice, acquiring sufficient number of image-recipe pairs …

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Person-Level Action Recognition In Complex Events Via Tsd-Tsm Networks, Yanbin Hao, Zi-Niu Liu, Hao Zhang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo Oct 2020

Person-Level Action Recognition In Complex Events Via Tsd-Tsm Networks, Yanbin Hao, Zi-Niu Liu, Hao Zhang, Bin Zhu, Jingjing Chen, Yu-Gang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

The task of person-level action recognition in complex events aims to densely detect pedestrians and individually predict their actions from surveillance videos. In this paper, we present a simple yet efficient pipeline for this task, referred to as TSD-TSM networks. Firstly, we adopt the TSD detector for the pedestrian localization on each single keyframe. Secondly, we generate the sequential ROIs for a person proposal by replicating the adjusted bounding box coordinates around the keyframe. Particularly, we propose to conduct straddling expansion and region squaring on the original bounding box of a person proposal to widen the potential space of motion …

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Deeprhythm: Exposing Deepfakes With Attentional Visual Heartbeat Rhythms, Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao Oct 2020

Deeprhythm: Exposing Deepfakes With Attentional Visual Heartbeat Rhythms, Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand for effective DeepFakes detectors. Motivated by the fact that remote visual photoplethysmography (PPG) is made possible by monitoring the minuscule periodic changes of skin color due to blood pumping through the face, we conjecture that normal heartbeat rhythms found in the real face videos will be disrupted or even entirely broken in a DeepFake video, making it a potentially powerful indicator for DeepFake detection. In this work, we propose DeepRhythm, a DeepFake …

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Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu Oct 2020

Amora: Black-Box Adversarial Morphing Attack, Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the black-box attack problem, we devise a simple yet effective …

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Deepsonar: Towards Effective And Robust Detection Of Ai-Synthesized Fake Voices, Run Wang, Felix Juefei-Xu, Yihao Huang, Qing Guo, Xiaofei Xie, Lei Ma, Yang Liu Oct 2020

Deepsonar: Towards Effective And Robust Detection Of Ai-Synthesized Fake Voices, Run Wang, Felix Juefei-Xu, Yihao Huang, Qing Guo, Xiaofei Xie, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

With the recent advances in voice synthesis, AI-synthesized fake voices are indistinguishable to human ears and widely are applied to produce realistic and natural DeepFakes, exhibiting real threats to our society. However, effective and robust detectors for synthesized fake voices are still in their infancy and are not ready to fully tackle this emerging threat. In this paper, we devise a novel approach, named DeepSonar, based on monitoring neuron behaviors of speaker recognition (SR) system, i.e., a deep neural network (DNN), to discern AI-synthesized fake voices. Layer-wise neuron behaviors provide an important insight to meticulously catch the differences among inputs, …

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Annapurna: An Automated Smartwatch-Based Eating Detection And Food Journaling System, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee Oct 2020

Annapurna: An Automated Smartwatch-Based Eating Detection And Food Journaling System, Sougata Sen, Vigneshwaran Subbaraju, Archan Misra, Rajesh Krishna Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Maintaining a food journal can allow an individual to monitor eating habits, including unhealthy eating sessions, food items causing severe reactions, or portion size related information. However, manually maintaining a food journal can be burdensome. In this paper, we explore the vision of a pervasive, automated, completely unobtrusive, food journaling system using a commodity smartwatch. We present a prototype system — Annapurna— which is composed of three key components: (a) a smartwatch-based gesture recognizer that can robustly identify eating-specific gestures occurring anywhere, (b) a smartwatch-based image captor that obtains a small set of relevant images (containing views of the food …

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Hierarchical Identity-Based Signature In Polynomial Rings, Zhichao Yang, Dung H. Duong, Willy Susilo, Guomin Yang, Chao Li, Rongmao Chen Oct 2020

Hierarchical Identity-Based Signature In Polynomial Rings, Zhichao Yang, Dung H. Duong, Willy Susilo, Guomin Yang, Chao Li, Rongmao Chen

Research Collection School Of Computing and Information Systems

Hierarchical identity-based signature (HIBS) plays a core role in a large community as it significantly reduces the workload of the root private key generator. To make HIBS still available and secure in post-quantum era, constructing lattice-based schemes is a promising option. In this paper, we present an efficient HIBS scheme in polynomial rings. Although there are many lattice-based signatures proposed in recent years, to the best of our knowledge, our HIBS scheme is the first ring-based construction. In the center of our construction are two new algorithms to extend lattice trapdoors to higher dimensions, which are non-trivial and of independent …

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White-Box Fairness Testing Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, Dai Ting Oct 2020

White-Box Fairness Testing Through Adversarial Sampling, Peixin Zhang, Jingyi Wang, Jun Sun, Guoliang Dong, Xinyu Wang, Xingen Wang, Jin Song Dong, Dai Ting

Research Collection School Of Computing and Information Systems

Although deep neural networks (DNNs) have demonstrated astonishing performance in many applications, there are still concerns on their dependability. One desirable property of DNN for applications with societal impact is fairness (i.e., non-discrimination). In this work, we propose a scalable approach for searching individual discriminatory instances of DNN. Compared with state-of-the-art methods, our approach only employs lightweight procedures like gradient computation and clustering, which makes it significantly more scalable than existing methods. Experimental results show that our approach explores the search space more effectively (9 times) and generates much more individual discriminatory instances (25 times) using much less time (half …

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Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang Oct 2020

Sentiment Analysis For Software Engineering: How Far Can Pre-Trained Transformer Models Go?, Ting Zhang, Bowen Xu, Thung Ferdian, Stefanus Agus Haryono, David Lo, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Extensive research has been conducted on sentiment analysis for software engineering (SA4SE). Researchers have invested much effort in developing customized tools (e.g., SentiStrength-SE, SentiCR) to classify the sentiment polarity for Software Engineering (SE) specific contents (e.g., discussions in Stack Overflow and code review comments). Even so, there is still much room for improvement. Recently, pre-trained Transformer-based models (e.g., BERT, XLNet) have brought considerable breakthroughs in the field of natural language processing (NLP). In this work, we conducted a systematic evaluation of five existing SA4SE tools and variants of four state-of-the-art pre-trained Transformer-based models on six SE datasets. Our work is …

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Using Student Perceptions To Design Smart Class Participation Tools: A Technology Framework, Swapna Gottipati, Shankararaman, Venky, Mark Wei Jie Ng Oct 2020

Using Student Perceptions To Design Smart Class Participation Tools: A Technology Framework, Swapna Gottipati, Shankararaman, Venky, Mark Wei Jie Ng

Research Collection School Of Computing and Information Systems

Our research full paper studies the perceptions of students and proposes technology design framework for smart class participation tools. Participation in classroom discussions has been observed to improve student comprehension and performance. In our contemporary tertiary educational context, student participation is characterised in mainly two forms; inclass discussions and online forums. Both forms of participation generate voluminous amounts of knowledge. However, the present difficulty in capturing and analysing these forms of participation leads to loss of knowledge and insights, which otherwise could be very useful. This study aims to analyse ways to improve data capture as well as data analysis …

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Automated Discussion Analysis - Framework For Knowledge Analysis From Class Discussions, Swapna Gottipati, Venky Shankararaman, Mallikan Gokarn Nitin Oct 2020

Automated Discussion Analysis - Framework For Knowledge Analysis From Class Discussions, Swapna Gottipati, Venky Shankararaman, Mallikan Gokarn Nitin

Research Collection School Of Computing and Information Systems

This research full paper, describes knowledge management of class discussions using an analytics based framework. Discussions, either live classroom or through online forums, when used as a teaching method can help stimulate critical thinking. It allows the teacher to explore in-depth the key concepts covered in the course, motivates students to articulate their ideas clearly and challenge the students to think more deeply. Analysing the discussions helps instructors gain better insights on the personal and collaborative learning behaviour of students. However, knowledge from in-class discussions and online forums is not effectively captured and mined due to lack of appropriate automated …

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Viscene: A Collaborative Authoring Tool For Scene Descriptions In Videos, Rosiana Natalie, Ebrima Jarjue, Hernisa Kacorri, Kotaro Hara Oct 2020

Viscene: A Collaborative Authoring Tool For Scene Descriptions In Videos, Rosiana Natalie, Ebrima Jarjue, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions can make the visual content in videos accessible to people with visual impairments. However, the majority of the online videos lack audio descriptions due in part to the shortage of experts who can create high-quality descriptions. We present ViScene, a web-based authoring tool that taps into the larger pool of sighted non-experts to help them generate high-quality descriptions via two feedback mechanisms - succinct visualizations and comments from an expert. Through a mixed-design study with N = 6 participants, we explore the usability of ViScene and the quality of the descriptions created by sighted non-experts with and without …

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Lis: Lightweight Signature Schemes For Continuous Message Authentication In Cyber-Physical Systems, Zheng Yang, Chenglu Jin, Yangguang Tian, Junyu Lai, Jianying Zhou Oct 2020

Lis: Lightweight Signature Schemes For Continuous Message Authentication In Cyber-Physical Systems, Zheng Yang, Chenglu Jin, Yangguang Tian, Junyu Lai, Jianying Zhou

Research Collection School Of Computing and Information Systems

Cyber-Physical Systems (CPS) provide the foundation of our critical infrastructures, which form the basis of emerging and future smart services and improve our quality of life in many areas. In such CPS, sensor data is transmitted over the network to the controller, which will make real-time control decisions according to the received sensor data. Due to the existence of spoofing attacks (more specifically to CPS, false data injection attacks), one has to protect the authenticity and integrity of the transmitted data. For example, a digital signature can be used to solve this issue. However, the resource-constrained field devices like sensors …

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Iot-Enabled Community Care For Ageing-In-Place: The Singapore Experience, Hwee-Pink Tan Oct 2020

Iot-Enabled Community Care For Ageing-In-Place: The Singapore Experience, Hwee-Pink Tan

Research Collection School Of Computing and Information Systems

The paradigm of ageing-in-place – where the elderly live and age in their own homes, independently and safely, with care provided by the community – is compelling, especially in societies that face both shortages in institutionalized eldercare resources, and rapidly ageing populations. When the number of elderly who live alone rises rapidly, support and care from their communities become increasing crucial. Internet of Things (IoT) technologies. They can become the fundamental enabler for smart community eldercare. In this presentation, I would like to share our experiences and learnings gathered from large-scale deployments of IoT systems in in-home and community spaces …

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Revocable And Certificateless Public Auditing For Cloud Storage, Yinghui Zhang, Tiantian Zhang, Shengmin Xu, Guowen Xu, Dong Zheng Oct 2020

Revocable And Certificateless Public Auditing For Cloud Storage, Yinghui Zhang, Tiantian Zhang, Shengmin Xu, Guowen Xu, Dong Zheng

Research Collection School Of Computing and Information Systems

Plenty of computing and storage resources in the cloud are provided for users with restricted computing and storage resources, which has attracted the attention of many researchers. A generic blockchain-based cloud data auditing scheme is proposed, which is compatible with any blockchains including the bitcoin blockchain. In the data integrity checking scheme, certificateless signature (CLS) can be used to verify the identity of users. Besides, the key exchange is utilized in the key generation, which can eliminate the security channel to achieve system robustness. Considering the real situation, the users who join the cloud storage system may be revoked for …

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Visual Sentiment Analysis For Review Images With Item-Oriented And User-Oriented Cnn: Reproducibility Companion Paper, Quoc Tuan Truong, Hady W. Lauw, Martin Aumuller, Naoko Nitta Oct 2020

Visual Sentiment Analysis For Review Images With Item-Oriented And User-Oriented Cnn: Reproducibility Companion Paper, Quoc Tuan Truong, Hady W. Lauw, Martin Aumuller, Naoko Nitta

Research Collection School Of Computing and Information Systems

We revisit our contributions on visual sentiment analysis for online review images published at ACM Multimedia 2017, where we develop item-oriented and user-oriented convolutional neural networks that better capture the interaction of image features with specific expressions of users or items. In this work, we outline the experimental claims as well as describe the procedures to reproduce the results therein. In addition, we provide artifacts including data sets and code to replicate the experiments.

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Tpr: Text-Aware Preference Ranking For Recommender Systems, Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim Oct 2020

Tpr: Text-Aware Preference Ranking For Recommender Systems, Yu-Neng Chuang, Chih-Ming Chen, Chuan-Ju Wang, Ming-Feng Tsai, Yuan Fang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

Textual data is common and informative auxiliary information for recommender systems. Most prior art utilizes text for rating prediction, but rare work connects it to top-N recommendation. Moreover, although advanced recommendation models capable of incorporating auxiliary information have been developed, none of these are specifically designed to model textual information, yielding a limited usage scenario for typical user-to-item recommendation. In this work, we present a framework of text-aware preference ranking (TPR) for top-N recommendation, in which we comprehensively model the joint association of user-item interaction and relations between items and associated text. Using the TPR framework, we construct a joint …

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Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu Oct 2020

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …

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