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Full-Text Articles in Computer Sciences

Self-Supervised Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh Nov 2021

Self-Supervised Multi-Class Pre-Training For Unsupervised Anomaly Detection And Segmentation In Medical Images, Yu Tian, Fengbei Liu, Guansong Pang, Yuanhong Chen, Yuyuan Liu, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) that requires only normal (healthy) training images is an important tool for enabling the development of medical image analysis (MIA) applications, such as disease screening, since it is often difficult to collect and annotate abnormal (or disease) images in MIA. However, heavily relying on the normal images may cause the model training to overfit the normal class. Self-supervised pre-training is an effective solution to this problem. Unfortunately, current self-supervision methods adapted from computer vision are sub-optimal for MIA applications because they do not explore MIA domain knowledge for designing the pretext tasks or the training process. …

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Information Technology And Organizational Learning: Managing Behavioral Change In The Digital Age By Arthur M. Langer, Siu Loon Hoe Nov 2021

Information Technology And Organizational Learning: Managing Behavioral Change In The Digital Age By Arthur M. Langer, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

As the world battles yet another crisis because of the spread of COVID-19, the idea of digitalization brings about a whole new meaning. Many professionals and information technology (IT) managers have remarked that the spread of the coronavirus has accelerated the pace of digital transformation much more so than any effort put forth by C-suite executives. While it is true that most organizations do not accept new technology readily because of embedded legacy systems, changing the corporate cultures does play an important role in affecting the rate of IT adoption. Very often, leaders and senior executives focus on the technological …

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Efficient Server-Aided Secure Two-Party Computation In Heterogeneous Mobile Cloud Computing, Yulin Wu, Xuan Wang, Willy Susilo, Guomin Yang, Zoe L. Jiang, Qian Chen, Peng Xu Nov 2021

Efficient Server-Aided Secure Two-Party Computation In Heterogeneous Mobile Cloud Computing, Yulin Wu, Xuan Wang, Willy Susilo, Guomin Yang, Zoe L. Jiang, Qian Chen, Peng Xu

Research Collection School Of Computing and Information Systems

With the ubiquity of mobile devices and rapid development of cloud computing, mobile cloud computing (MCC) has been considered as an essential computation setting to support complicated, scalable and flexible mobile applications by overcoming the physical limitations of mobile devices with the aid of cloud. In the MCC setting, since many mobile applications (e.g., map apps) interacting with cloud server and application server need to perform computation with the private data of users, it is important to realize secure computation for MCC. In this article, we propose an efficient server-aided secure two-party computation (2PC) protocol for MCC. This is the …

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Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University Oct 2021

Ai: Friend Or Foe? (And What Business Leaders Need To Know), Singapore Management University

Perspectives@SMU

Artificial intelligence presents significant opportunities for business – as well as not insignificant threats to humanity – and governance frameworks are urgently needed to create a fair and equitable future under AI

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Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe Oct 2021

Measuring Data Collection Diligence For Community Healthcare, Galawala Ramesha Samurdhi Karunasena, M. S. Ambiya, Arunesh Sinha, R. Nagar, S. Dalal, Abdullah. H., D. Thakkar, D. Narayanan, M. Tambe

Research Collection School Of Computing and Information Systems

Data analytics has tremendous potential to provide targeted benefit in low-resource communities, however the availability of highquality public health data is a significant challenge in developing countries primarily due to non-diligent data collection by community health workers (CHWs). Our use of the word non-diligence here is to emphasize that poor data collection is often not a deliberate action by CHW but arises due to a myriad of factors, sometime beyond the control of the CHW. In this work, we define and test a data collection diligence score. This challenging unlabeled data problem is handled by building upon domain expert’s guidance …

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An Exploratory Study Of Social Support Systems To Help Older Adults In Managing Mobile Safety, Tamir Mendel, Debin Gao, David Lo, Eran Toch Oct 2021

An Exploratory Study Of Social Support Systems To Help Older Adults In Managing Mobile Safety, Tamir Mendel, Debin Gao, David Lo, Eran Toch

Research Collection School Of Computing and Information Systems

Older adults face increased safety challenges, such as targeted online fraud and phishing, contributing to the growing technological divide between them and younger adults. Social support from family and friends is often the primary way older adults receive help, but it may also lead to reliance on others. We have conducted an exploratory study to investigate older adults' attitudes and experiences related to mobile social support technologies for mobile safety. We interviewed 18 older adults about their existing support and used the think-aloud method to gather data about a prototype for providing social support during mobile safety challenges. Our findings …

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Noahqa: Numerical Reasoning With Interpretable Graph Question Answering Dataset, Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim Oct 2021

Noahqa: Numerical Reasoning With Interpretable Graph Question Answering Dataset, Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasoning processes to get the answers. As a result, the state-of-the-art QA research on numerical reasoning still focuses on simple calculations and does not provide the mathematical expressions or evidences justifying the answers. Second, the QA community has contributed much effort to improving the interpretability of QA models. However, these models fail to explicitly show …

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Sylpeniot: Symmetric Lightweight Predicate Encryption For Data Privacy Applications In Iot Environments, Tran Viet Xuan Phuong, Willy Susilo, Guomin Yang, Jongkil Kim, Yangwai Chow, Dongxi Liu Oct 2021

Sylpeniot: Symmetric Lightweight Predicate Encryption For Data Privacy Applications In Iot Environments, Tran Viet Xuan Phuong, Willy Susilo, Guomin Yang, Jongkil Kim, Yangwai Chow, Dongxi Liu

Research Collection School Of Computing and Information Systems

Privacy preserving mechanisms are essential for protecting data in IoT environments. This is particularly challenging as IoT environments often contain heterogeneous resource-constrained devices. One method for protecting privacy is to encrypt data with a pattern or metadata. To prevent information leakage, an evaluation using the pattern must be performed before the data can be retrieved. However, the computational costs associated with typical privacy preserving mechanisms can be costly. This makes such methods ill-suited for resource-constrained devices, as the high energy consumption will quickly drain the battery. This work solves this challenging problem by proposing SyLPEnIoT – Symmetric Lightweight Predicate Encryption …

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Improving The Performance Of Transportation Networks: A Semi-Centralized Pricing Approach, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Liujiang Kang, Xuexi Zhang, Qilun Wu Oct 2021

Improving The Performance Of Transportation Networks: A Semi-Centralized Pricing Approach, Zhiguang Cao, Hongliang Guo, Wen Song, Kaizhou Gao, Liujiang Kang, Xuexi Zhang, Qilun Wu

Research Collection School Of Computing and Information Systems

Improving the performance of transportation network is a crucial task in traffic management. In this paper, we start with a cooperative routing problem, which aims to minimize the chance of road network breakdown. To address this problem, we propose a subgradient method, which can be naturally implemented as a semi-centralized pricing approach. Particularly, each road link adopts the pricing scheme to calculate and adjust the local toll regularly, while the vehicles update their routes to minimize the toll costs by exploiting the global toll information. To prevent the potential oscillation brought by the subgradient method, we introduce a heavy-ball method …

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Uncovering Patterns In Reviewers' Feedback To Scene Description Authors, Rosiana Natalie, Jolene Kar Inn Loh, Huei Suen Tan, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara Oct 2021

Uncovering Patterns In Reviewers' Feedback To Scene Description Authors, Rosiana Natalie, Jolene Kar Inn Loh, Huei Suen Tan, Joshua Shi-Hao Tseng, Hernisa Kacorri, Kotaro Hara

Research Collection School Of Computing and Information Systems

Audio descriptions (ADs) can increase access to videos for blind people. Researchers have explored different mechanisms for generating ADs, with some of the most recent studies involving paid novices; to improve the quality of their ADs, novices receive feedback from reviewers. However, reviewer feedback is not instantaneous. To explore the potential for real-time feedback through automation, in this paper, we analyze 1,120 comments that 40 sighted novices received from a sighted or a blind reviewer. We find that feedback patterns tend to fall under four themes: (i) Quality; commenting on different AD quality variables, (ii) Speech Act; the utterance or …

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Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang Oct 2021

Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a “dog” model is learned within “grass+dog” and “road+dog” respectively, so the “grass” and …

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Using Role Play To Develop An Empathetic Mindset In Executive Education, Siu Loon Hoe, Tamsin Greulich-Smith Oct 2021

Using Role Play To Develop An Empathetic Mindset In Executive Education, Siu Loon Hoe, Tamsin Greulich-Smith

Research Collection School Of Computing and Information Systems

The purpose of this article is to discuss the importance of a role play activity as part of an experiential instructional strategy to develop an empathetic mindset among professionals, managers, and executives (PMEs) attending an executive education program in change management. This article provides an approach and process for management educators and facilitators of executive education programs to introduce and teach role play for the busy executives to learn about empathy. Role play is a useful teaching method that helps adult learners understand the importance of seeing things from another person’s point of view especially within a short period of …

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Direct Differentiable Augmentation Search, Aoming Liu, Zehao Huang, Zhiwu Huang, Huang, Naiyan Wang Oct 2021

Direct Differentiable Augmentation Search, Aoming Liu, Zehao Huang, Zhiwu Huang, Huang, Naiyan Wang

Research Collection School Of Computing and Information Systems

Data augmentation has been an indispensable tool to improve the performance of deep neural networks, however the augmentation can hardly transfer among different tasks and datasets. Consequently, a recent trend is to adopt AutoML technique to learn proper augmentation policy without extensive hand-crafted tuning. In this paper, we propose an efficient differentiable search algorithm called Direct Differentiable Augmentation Search (DDAS). It exploits meta-learning with one-step gradient update and continuous relaxation to the expected training loss for efficient search. Our DDAS can achieve efficient augmentation search without relying on approximations such as Gumbel-Softmax or second order gradient approximation. To further reduce …

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Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan Oct 2021

Design Of A Two-Echelon Freight Distribution System In Last-Mile Logistics Considering Covering Locations And Occasional Drivers, Vincent F. Yu, Panca Jodiawan, Ming-Lu Hou, Aldy Gunawan

Research Collection School Of Computing and Information Systems

This research addresses a new variant of the vehicle routing problem, called the two-echelon vehicle routing problem with time windows, covering options, and occasional drivers (2E-VRPTW-CO-OD). In this problem, two types of fleets are available to serve customers, city freighters and occasional drivers (ODs), while two delivery options are available to customers, home delivery and alternative delivery. For customers choosing the alternative delivery, their demands are delivered to one of the available covering locations for them to pick up. The objective of 2E-VRPTW-CO-OD is to minimize the total cost consisting of routing costs, connection costs, and compensations paid to ODs …

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Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady W. Lauw Oct 2021

Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary information that may encode additional clues on how users consume items. Examples of such data (referred to as modalities) are social networks, item’s descriptive text, product images. The objective of this tutorial is to offer a comprehensive review of recent advances to represent, transform and incorporate the different modalities into recommendation models. Moreover, through practical hands-on sessions, we …

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Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw Oct 2021

Towards Source-Aligned Variational Models For Cross-Domain Recommendation, Aghiles Salah, Thanh-Binh Tran, Hady W. Lauw

Research Collection School Of Computing and Information Systems

Data sparsity is a long-standing challenge in recommender systems. Among existing approaches to alleviate this problem, cross-domain recommendation consists in leveraging knowledge from a source domain or category (e.g., Movies) to improve item recommendation in a target domain (e.g., Books). In this work, we advocate a probabilistic approach to cross-domain recommendation and rely on variational autoencoders (VAEs) as our latent variable models. More precisely, we assume that we have access to a VAE trained on the source domain that we seek to leverage to improve preference modeling in the target domain. To this end, we propose a model which learns …

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Bv-Person: A Large-Scale Dataset For Bird-View Person Re-Identification, Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li Oct 2021

Bv-Person: A Large-Scale Dataset For Bird-View Person Re-Identification, Cheng Yan, Guansong Pang, Lei Wang, Jile Jiao, Xuetao Feng, Chunhua Shen, Jingjing Li

Research Collection School Of Computing and Information Systems

Person Re-IDentification (ReID) aims at re-identifying persons from non-overlapping cameras. Existing person ReID studies focus on horizontal-view ReID tasks, in which the person images are captured by the cameras from a (nearly) horizontal view. In this work we introduce a new ReID task, bird-view person ReID, which aims at searching for a person in a gallery of horizontal-view images with the query images taken from a bird's-eye view, i.e., an elevated view of an object from above. The task is important because there are a large number of video surveillance cameras capturing persons from such an elevated view at public …

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Countering Attacker Data Manipulation In Security Games, Andrew R. Butler, Thanh H. Nguyen, Arunesh Sinha Oct 2021

Countering Attacker Data Manipulation In Security Games, Andrew R. Butler, Thanh H. Nguyen, Arunesh Sinha

Research Collection School Of Computing and Information Systems

. Defending against attackers with unknown behavior is an important area of research in security games. A well-established approach is to utilize historical attack data to create a behavioral model of the attacker. However, this presents a vulnerability: a clever attacker may change its own behavior during learning, leading to an inaccurate model and ineffective defender strategies. In this paper, we investigate how a wary defender can defend against such deceptive attacker. We provide four main contributions. First, we develop a new technique to estimate attacker true behavior despite data manipulation by the clever adversary. Second, we extend this technique …

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Links Do Matter: Understanding The Drivers Of Developer Interactions In Software Ecosystems, Subhajit Datta, Amrita Bhattacharjee, Subhashis Majumder Oct 2021

Links Do Matter: Understanding The Drivers Of Developer Interactions In Software Ecosystems, Subhajit Datta, Amrita Bhattacharjee, Subhashis Majumder

Research Collection School Of Computing and Information Systems

Studies of collaborating individuals engaged in collective enterprises usually focus on the individuals, rather than the links supporting their interaction. Accordingly, large scale software development ecosystems have also been examined primarily in terms of developer engagement. We posit that communication links between developers play a central role in the sustenance and effectiveness of such ecosystems. In this paper, we investigate whether and how developer attributes relate to the importance of the communication channels between them. We present a technique using 2nd order Markov models to extract features of interest of the links and apply the technique on data from a …

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Aixfood'21: 3rd Workshop On Aixfood, Ricardo Guerrero, Michael Spranger, Shuqiang Jiang, Chong-Wah Ngo Oct 2021

Aixfood'21: 3rd Workshop On Aixfood, Ricardo Guerrero, Michael Spranger, Shuqiang Jiang, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Food and cooking analysis present exciting research and application challenges for modern AI systems, particularly in the context of multimodal data such as images or video. A meal that appears in a food image is a product of a complex progression of cooking stages, often described in the accompanying textual recipe form. In the cooking process, individual ingredients change their physical properties, become combined with other food components, all to produce a final, yet highly variable, appearance of the meal. Recognizing food items or meals on a plate from images or videos, their physical properties such as the amount, nutritional …

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Mlcatchup: Automated Update Of Deprecated Machine-Learning Apis In Python, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang Oct 2021

Mlcatchup: Automated Update Of Deprecated Machine-Learning Apis In Python, Stefanus Agus Haryono, Thung Ferdian, David Lo, Julia Lawall, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Machine learning (ML) libraries are gaining vast popularity, especially in the Python programming language. Using the latest version of such libraries is recommended to ensure the best performance and security. When migrating to the latest version of a machine learning library, usages of deprecated APIs need to be updated, which is a time-consuming process. In this paper, we propose MLCatchUp, an automated API usage update tool for deprecated APIs of popular ML libraries written in Python. MLCatchUp automatically infers the required transformation to migrate usages of deprecated API through the differences between the deprecated and updated API signatures. MLCatchUp offers …

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Target-Guided Emotion-Aware Chat Machine, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng Oct 2021

Target-Guided Emotion-Aware Chat Machine, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng

Research Collection School Of Computing and Information Systems

The consistency of a response to a given post at the semantic level and emotional level is essential for a dialogue system to deliver humanlike interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem and proposes a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leveraging target information to generate more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed …

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Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh Oct 2021

Constrained Contrastive Distribution Learning For Unsupervised Anomaly Detection And Localisation In Medical Images, Yu Tian, Guansong Pang, Fengbei Liu, Yuanhong Chen, Seon Ho Shin, Johan W. Verjans, Rajvinder Singh

Research Collection School Of Computing and Information Systems

Unsupervised anomaly detection (UAD) learns one-class classifiers exclusively with normal (i.e., healthy) images to detect any abnormal (i.e., unhealthy) samples that do not conform to the expected normal patterns. UAD has two main advantages over its fully supervised counterpart. Firstly, it is able to directly leverage large datasets available from health screening programs that contain mostly normal image samples, avoiding the costly manual labelling of abnormal samples and the subsequent issues involved in training with extremely class-imbalanced data. Further, UAD approaches can potentially detect and localise any type of lesions that deviate from the normal patterns. One significant challenge faced …

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Smart Contract Development: Challenges And Opportunities, Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach D. Le, Xin Xia, Yang Feng, Zhenyu Chen, Baowen Xu Oct 2021

Smart Contract Development: Challenges And Opportunities, Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach D. Le, Xin Xia, Yang Feng, Zhenyu Chen, Baowen Xu

Research Collection School Of Computing and Information Systems

Smart contract, a term which was originally coined to refer to the automation of legal contracts in general, has recently seen much interest due to the advent of blockchain technology. Recently, the term is popularly used to refer to low-level code scripts running on a blockchain platform. Our study focuses exclusively on this subset of smart contracts. Such smart contracts have increasingly been gaining ground, finding numerous important applications (e.g., crowdfunding) in the real world. Despite the increasing popularity, smart contract development still remains somewhat a mystery to many developers largely due to its special design and applications. Are there …

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Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng Oct 2021

Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng

Research Collection School Of Computing and Information Systems

Tax evasion is a serious economic problem for many countries, as it can undermine the government’s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A …

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Holoboard: A Large-Format Immersive Teaching Board Based On Pseudo Holographics, Jiangtao Gong, Teng Han, Siling Guo, Jiannan Li, Siyu Zha, Liuxin Zhang, Feng Tian, Qianying Wang, Yong Rui Oct 2021

Holoboard: A Large-Format Immersive Teaching Board Based On Pseudo Holographics, Jiangtao Gong, Teng Han, Siling Guo, Jiannan Li, Siyu Zha, Liuxin Zhang, Feng Tian, Qianying Wang, Yong Rui

Research Collection School Of Computing and Information Systems

In this paper, we present HoloBoard, an interactive large-format pseduo-holographic display system for lecture based classes. With its unique properties of immersive visual display and transparent screen, we designed and implemented a rich set of novel interaction techniques like immersive presentation, role-play, and lecturing behind the scene that are potentially valuable for lecturing in class. We conducted a controlled experimental study to compare a HoloBoard class with a normal class through measuring students’ learning outcomes and three dimensions of engagement (i.e., behavioral, emotional, and cognitive engagement). We used pre-/post- knowledge tests and multimodal learning analytics to measure students’ learning outcomes …

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From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He Oct 2021

From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He

Research Collection School Of Computing and Information Systems

Existing GAN inversion methods are stuck in a paradox that the inverted codes can either achieve high-fidelity reconstruction, or retain the editing capability. Having only one of them clearly cannot realize real image editing. In this paper, we resolve this paradox by introducing consecutive images (e.g., video frames or the same person with different poses) into the inversion process. The rationale behind our solution is that the continuity of consecutive images leads to inherent editable directions. This inborn property is used for two unique purposes: 1) regularizing the joint inversion process, such that each of the inverted codes is semantically …

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Privacy-Preserving Voluntary-Tallying Leader Election For Internet Of Things, Tong Wu, Guomin Yang, Liehuang Zhu, Yulin Wu Oct 2021

Privacy-Preserving Voluntary-Tallying Leader Election For Internet Of Things, Tong Wu, Guomin Yang, Liehuang Zhu, Yulin Wu

Research Collection School Of Computing and Information Systems

The Internet of Things (IoT) is commonly deployed with devices of limited power and computation capability. A centralized IoT architecture provides a simplified management for IoT system but brings redundancy by the unnecessary data traffic with a data center. A decentralized IoT reduces the cost on data traffic and is resilient to the single-point-of failure. The blockchain technique has attracted a large amount of research, which is redeemed as a perspective of decentralized IoT system infrastructure. It also brings new privacy challenges for that the blockchain is a public ledger of all digital events executed and shared among all participants. …

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Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong Oct 2021

Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …

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Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady Wirawan Lauw Oct 2021

Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Recommender systems typically learn from user-item preference data such as ratings and clicks. This information is sparse in nature, i.e., observed user-item preferences often represent less than 5% of possible interactions. One promising direction to alleviate data sparsity is to leverage auxiliary information that may encode additional clues on how users consume items. Examples of such data (referred to as modalities) are social networks, item’s descriptive text, product images. The objective of this tutorial is to offer a comprehensive review of recent advances to represent, transform and incorporate the different modalities into recommendation models. Moreover, through practical hands-on sessions, we …

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