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2023

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Articles 121 - 150 of 386

Full-Text Articles in Databases and Information Systems

Personalized Fashion Outfit Generation With User Coordination Preference Learning, Yujuan Ding, P.Y. Mok, Yunshan Ma, Yi Bin Sep 2023

Personalized Fashion Outfit Generation With User Coordination Preference Learning, Yujuan Ding, P.Y. Mok, Yunshan Ma, Yi Bin

Research Collection School Of Computing and Information Systems

This paper focuses on personalized outfit generation, aiming to generate compatible fashion outfits catering to given users. Personalized recommendation by generating outfits of compatible items is an emerging task in the recommendation community with great commercial value but less explored. The task requires to explore both user-outfit personalization and outfit compatibility, any of which is challenging due to the huge learning space resulted from large number of items, users, and possible outfit options. To specify the user preference on outfits and regulate the outfit compatibility modeling, we propose to incorporate coordination knowledge in fashion. Inspired by the fact that users …

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Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang Sep 2023

Rosas: Deep Semi-Supervised Anomaly Detection With Contamination-Resilient Continuous Supervision, Hongzuo Xu, Yijie Wang, Guansong Pang, Songlei Jian, Ning Liu, Yongjun Wang

Research Collection School Of Computing and Information Systems

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training; 2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method …

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Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw Sep 2023

Continual Collaborative Filtering Through Gradient Alignment, Dinh Hieu Do, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

A recommender system operates in a dynamic environment where new items emerge and new users join the system, resulting in ever-growing user-item interactions over time. Existing works either assume a model trained offline on a static dataset (requiring periodic re-training with ever larger datasets); or an online learning setup that favors recency over history. As privacy-aware users could hide their histories, the loss of older information means that periodic retraining may not always be feasible, while online learning may lose sight of users' long-term preferences. In this work, we adopt a continual learning perspective to collaborative filtering, by compartmentalizing users …

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When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau Sep 2023

When Routing Meets Recommendation: Solving Dynamic Order Recommendations Problem In Peer-To-Peer Logistics Platforms, Zhiqin Zhang, Waldy Joe, Yuyang Er, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Peer-to-Peer (P2P) logistics platforms, unlike traditional last-mile logistics providers, do not have dedicated delivery resources (both vehicles and drivers). Thus, the efficiency of such operating model lies in the successful matching of demand and supply, i.e., how to match the delivery tasks with suitable drivers that will result in successful assignment and completion of the tasks. We consider a Same-Day Delivery Problem (SDDP) involving a P2P logistics platform where new orders arrive dynamically and the platform operator needs to generate a list of recommended orders to the crowdsourced drivers. We formulate this problem as a Dynamic Order Recommendations Problem (DORP). …

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Graph-Level Anomaly Detection Via Hierarchical Memory Networks, Chaoxi Niu, Guansong Pang, Ling Chen Sep 2023

Graph-Level Anomaly Detection Via Hierarchical Memory Networks, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules---node and graph memory modules---via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the …

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Real: A Representative Error-Driven Approach For Active Learning, Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du Sep 2023

Real: A Representative Error-Driven Approach For Active Learning, Cheng Chen, Yong Wang, Lizi Liao, Yueguo Chen, Xiaoyong Du

Research Collection School Of Computing and Information Systems

Given a limited labeling budget, active learning (al) aims to sample the most informative instances from an unlabeled pool to acquire labels for subsequent model training. To achieve this, al typically measures the informativeness of unlabeled instances based on uncertainty and diversity. However, it does not consider erroneous instances with their neighborhood error density, which have great potential to improve the model performance. To address this limitation, we propose Real, a novel approach to select data instances with Representative Errors for Active Learning. It identifies minority predictions as pseudo errors within a cluster and allocates an adaptive sampling budget for …

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The Power Of Identity Cues In Text-Based Customer Service: Evidence From Twitter, Yang Gao, Huaxia Rui, Shujing Sun Sep 2023

The Power Of Identity Cues In Text-Based Customer Service: Evidence From Twitter, Yang Gao, Huaxia Rui, Shujing Sun

Research Collection School Of Computing and Information Systems

Text-based customer service is emerging as an important channel through which companies can assist customers. However, the use of few identity cues may cause customers to feel limited social presence and even suspect the human identity of agents, especially in the current age of advanced algorithms. Does such a lack of social presence affect service interactions? We studied this timely question by evaluating the impact of customers’ perceived social presence on service outcomes and customers’ attitudes toward agents. Our identification strategy hinged on Southwest Airlines’ sudden requirement to include a first name in response to service requests on Twitter, which …

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Literature Review In The Generative Ai Era: How To Make A Compelling Contribution, Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah Sep 2023

Literature Review In The Generative Ai Era: How To Make A Compelling Contribution, Shan L. Pan, Rohit Nishant, Tuure Tuunanen, Fiona Fui-Hoon Nah

Research Collection School Of Computing and Information Systems

As we write this editorial for this special issue, we are amidst the significant technological changes that are continuing to shape society. Since the emergence of ChatGPT in November 2022, humanity has become aware of the potential of generative AI (i.e., AI that can generate content) and large language models (LLMs) (i.e., AI models trained on a massive corpus of unstructured data). There is growing debate and discussion about the promise and perils of generative AI for the future of work, and academia is not immune. Premier journals in the IS domain, such as Information Systems Research, have published editorials …

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Models And Algorithms For Promoting Diverse And Fair Query Results, Md Mouinul Islam Aug 2023

Models And Algorithms For Promoting Diverse And Fair Query Results, Md Mouinul Islam

Dissertations

Ensuring fairness and diversity in search results are two key concerns in compelling search and recommendation applications. This work explicitly studies these two aspects given multiple users' preferences as inputs, in an effort to create a single ranking or top-k result set that satisfies different fairness and diversity criteria. From group fairness standpoint, it adapts demographic parity like group fairness criteria and proposes new models that are suitable for ranking or producing top-k set of results. This dissertation also studies equitable exposure of individual search results in long tail data, a concept related to individual fairness. First, the dissertation focuses …

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Diversification And Fairness In Top-K Ranking Algorithms, Mahsa Asadi Aug 2023

Diversification And Fairness In Top-K Ranking Algorithms, Mahsa Asadi

Dissertations

Given a user query, the typical user interfaces, such as search engines and recommender systems, only allow a small number of results to be returned to the user. Hence, figuring out what would be the top-k results is an important task in information retrieval, as it helps to ensure that the most relevant results are presented to the user. There exists an extensive body of research that studies how to score the records and return top-k to the user. Moreover, there exists an extensive set of criteria that researchers identify to present the user with top-k results, and result diversification …

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Human-Ai Complex Task Planning, Sepideh Nikookar Aug 2023

Human-Ai Complex Task Planning, Sepideh Nikookar

Dissertations

The process of complex task planning is ubiquitous and arises in a variety of compelling applications. A few leading examples include designing a personalized course plan or trip plan, designing music playlists/work sessions in web applications, or even planning routes of naval assets to collaboratively discover an unknown destination. For all of these aforementioned applications, creating a plan requires satisfying a basic construct, i.e., composing a sequence of sub-tasks (or items) that optimizes several criteria and satisfies constraints. For instance, in course planning, sub-tasks or items are core and elective courses, and degree requirements capture their complex dependencies as constraints. …

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Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang Aug 2023

Data-Driven 2d Materials Discovery For Next-Generation Electronics, Zeyu Zhang

Dissertations

The development of material discovery and design has lasted centuries in human history. After the concept of modern chemistry and material science was established, the strategy of material discovery relies on the experiments. Such a strategy becomes expensive and time-consuming with the increasing number of materials nowadays. Therefore, a novel strategy that is faster and more comprehensive is urgently needed. In this dissertation, an experiment-guided material discovery strategy is developed and explained using metal-organic frameworks (MOFs) as instances. The advent of 7r-stacked layered MOFs, which offer electrical conductivity on top of permanent porosity and high surface area, opened up new …

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Public Biological Databases And The Sui Generis Database Right, Alexander Bernier, Christian Busse, Tania M. Bubela Aug 2023

Public Biological Databases And The Sui Generis Database Right, Alexander Bernier, Christian Busse, Tania M. Bubela

Office of the Provost

The sui generis database right is an intellectual property right created in the European Union to stimulate investment in the curation of databases. Since its inception, communities engaged in research and development efforts have questioned its potential to incentivise database production, and posit that it stifles productive downstream uses of existing datasets. European courts have restricted the right’s ambit through a restrictive interpretation of the circumstances in which it applies, which we argue, enables downstream use of biological databases. Nonetheless, residual ambiguities about potential infringement of the right exist. The prospect of unintentional infringement can frustrate downstream innovation. These ambiguities …

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Forecasting Stock Indices With The Covid-19 Infection Rate As An Exogenous Variable, Mohammad Saha A. Patwary Aug 2023

Forecasting Stock Indices With The Covid-19 Infection Rate As An Exogenous Variable, Mohammad Saha A. Patwary

School of Computing and Informatics

Forecasting stock market indices is challenging because stock prices are usually nonlinear and non- stationary. COVID-19 has had a significant impact on stock market volatility, which makes forecasting more challenging. Since the number of confirmed cases significantly impacted the stock price index; hence, it has been considered a covariate in this analysis. The primary focus of this study is to address the challenge of forecasting volatile stock indices during Covid-19 by employing time series analysis. In particular, the goal is to find the best method to predict future stock price indices in relation to the number of COVID-19 infection rates. …

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On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference, Gui Huang, Ru Tao Aug 2023

On Digital Productivity Base Of Policies For Cross-Border Data Flows Between Rcep Parties And Its Influences—Taking Digital Integration Index As A Reference, Gui Huang, Ru Tao

Bulletin of Chinese Academy of Sciences (Chinese Version)

This study reviews the newest legislation and policies of Regional Comprehensive Economic Partnership (RCEP) participating countries on cross-border data flow, and then categorized them according to the ban on data transfer, local storage of data, permission-based regulation, and standards-based regulation. By referring to the indexes in the ASEAN Digital Integration Index, the subject and object factors of digital productivity in RCEP parities are sorted out, as well as the status quo of digital economy. Through the introduction of data value chain theory, the decisive impact of digital productivity factors on the policy formulation of cross-border data flow is expounded; by …

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Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan Aug 2023

Paradigm Review Of Data Localization In India And Its Implications For China, Ying Fan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Data localization is a focal point of global data governance and its impact on global data governance is no longer confined to a single country. Over the years, India has followed a unique policy framework in terms of cross-border data flows and data localization, and its insistence on data sovereignty reflects its position in the international arena. This study uses the Indian data localization paradigm as a research base to discuss the common phenomenon of disconnect between policy motivations and practical effects of data localization, and as an entry point to introduce the latest Indian research findings in this area. …

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Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi Aug 2023

Research On Multi-Source Heterogeneous Big Data Fusion Based On Wsr, Aihua Li, Weijia Xu, Yong Shi

Bulletin of Chinese Academy of Sciences (Chinese Version)

In the era of multi-source heterogeneous big data, big data presents new features such as cross, diversity and variability. The applications of big data in a wider range of fields have new requirements for data fusion. Under this background, the connotation of data fusion is enriched and expanded. The generalized data fusion includes the fusion of data resources, the fusion of model methods, and the fusion of decision-makers' knowledge and experience. This study analyzes the characteristics of multi-source heterogeneous data fusion at three different fusion levels: data level, information level and decision level, and discusses challenges for data fusion in …

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Cybersecurity Safeguards: What Cybersecurity Safeguards Could Have Prevented The Intelligence/Data Breach By A Member Of The Air National Guard, Christopher Curtis Royal Aug 2023

Cybersecurity Safeguards: What Cybersecurity Safeguards Could Have Prevented The Intelligence/Data Breach By A Member Of The Air National Guard, Christopher Curtis Royal

Cyber Operations and Resilience Program Graduate Projects

Jack Teixeira, a 21-year-old IT specialist Air National Guard found himself on the wrong side of the US law after sharing what is considered classified and extremely sensitive information about USA's operations and role in Ukraine and Russia war. Like other previous cases of leakage of classified intelligence, the case of Teixeira raises concerns about the weaknesses and vulnerability of federal agencies' IT systems and security protocols governing accessibility to classified documents. Internal leakages of such classified documents hurt national security and can harm the country, especially when such secretive intelligence finds its way into the hands of enemies. Unauthorized …

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Semantically Constitutive Entities In Knowledge Graphs, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw Aug 2023

Semantically Constitutive Entities In Knowledge Graphs, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw

Research Collection School Of Computing and Information Systems

Knowledge graphs are repositories of facts about a world. In this work, we seek to distill the set of entities or nodes in a knowledge graph into a specified number of constitutive nodes, whose embeddings would be retained. Intuitively, the remaining accessory nodes could have their original embeddings “forgotten”, and yet reconstitutable from those of the retained constitutive nodes. The constitutive nodes thus represent the semantically constitutive entities, which retain the core semantics of the knowledge graph. We propose a formulation as well as algorithmic solutions to minimize the reconstitution errors. The derived constitutive nodes are validated empirically both in …

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Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao Aug 2023

Sparsity Brings Vulnerabilities: Exploring New Metrics In Backdoor Attacks, Jianwen Tian, Kefan Qiu, Debin Gao, Zhi Wang, Xiaohui Kuang, Gang Zhao

Research Collection School Of Computing and Information Systems

Nowadays, using AI-based detectors to keep pace with the fast iterating of malware has attracted a great attention. However, most AI-based malware detectors use features with vast sparse subspaces to characterize applications, which brings significant vulnerabilities to the model. To exploit this sparsityrelated vulnerability, we propose a clean-label backdoor attack consisting of a dissimilarity metric-based candidate selection and a variation ratio-based trigger construction. The proposed backdoor is verified on different datasets, including a Windows PE dataset, an Android dataset with numerical and boolean feature values, and a PDF dataset. The experimental results show that the attack can slash the accuracy …

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Balancing Utility And Fairness In Submodular Maximization, Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang Aug 2023

Balancing Utility And Fairness In Submodular Maximization, Yanhao Wang, Yuchen Li, Francesco Bonchi, Ying Wang

Research Collection School Of Computing and Information Systems

Submodular function maximization is a fundamental combinatorial optimization problem with plenty of applications – including data summarization, influence maximization, and recommendation. In many of these problems, the goal is to find a solution that maximizes the average utility over all users, for each of whom the utility is defined by a monotone submodular function. However, when the population of users is composed of several demographic groups, another critical problem is whether the utility is fairly distributed across different groups. Although the utility and fairness objectives are both desirable, they might contradict each other, and, to the best of our knowledge, …

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Fintech Data Infrastructure For Esg Disclosure Compliance, Randall E. Duran, Peter Tierney Aug 2023

Fintech Data Infrastructure For Esg Disclosure Compliance, Randall E. Duran, Peter Tierney

Research Collection School Of Computing and Information Systems

Regulations related to the disclosure of environmental, governance, and social (ESG) factors are evolving rapidly and are a major concern for financial compliance worldwide. Information technology has the potential to reduce the effort and cost of ESG disclosure compliance. However, comprehensive and accurate ESG data are necessary for disclosures. Currently, the availability and quality of underlying data for ESG disclosures vary widely and are often deficient. The process involved with obtaining ESG data is also often inefficient and prone to error. This paper compares the models used and the evolution of Fintech data infrastructure developed to support financial services with …

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Single-View View Synthesis With Self-Rectified Pseudo-Stereo, Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He Aug 2023

Single-View View Synthesis With Self-Rectified Pseudo-Stereo, Yang Zhou, Hanjie Wu, Wenxi Liu, Zheng Xiong, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo viewpoint, which serves as an auxiliary input to construct the 3D space. In this way, the challenging novel view synthesis process is decoupled into two simpler problems of stereo synthesis and 3D reconstruction. In order to synthesize a structurally correct and detail-preserved stereo image, we propose a self-rectified stereo synthesis to amend erroneous …

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The 4th International Workshop On Talent And Management Computing (Tmc'2023): Editorial, Hengshu Zhu, Hui Xiong, Yong Ge, Ee-Peng Lim Aug 2023

The 4th International Workshop On Talent And Management Computing (Tmc'2023): Editorial, Hengshu Zhu, Hui Xiong, Yong Ge, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to deal with the talent and management related tasks in a quantitative manner. Indeed, thanks to the era of big data, the availability of large-scale talent data provides unparalleled opportunities for business leaders to understand the rules of talent and management, which in turn deliver intelligence for effective decision making and management for their organizations. In the past few years, talent and management computing have increasingly attracted attentions from KDD communities, and a number of research/applied data science efforts have been devoted. To …

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Knowledge Representation For Conceptual, Motivational, And Affective Processes In Natural Language Communication, Seng Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria Aug 2023

Knowledge Representation For Conceptual, Motivational, And Affective Processes In Natural Language Communication, Seng Beng Ho, Zhaoxia Wang, Boon-Kiat Quek, Erik Cambria

Research Collection School Of Computing and Information Systems

Natural language communication is an intricate and complex process. The speaker usually begins with an intention and motivation of what is to be communicated, and what outcomes are expected from the communication, while taking into consideration the listener’s mental model to concoct an appropriate sentence. Likewise, the listener has to interpret the speaker’s message, and respond accordingly, also with the speaker’s mental model in mind. Doing this successfully entails the appropriate representation of the conceptual, motivational, and affective processes that underlie language generation and understanding. Whereas big-data approaches in language processing (such as chatbots and machine translation) have performed well, …

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Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang Aug 2023

Multi-View Graph Contrastive Learning For Solving Vehicle Routing Problems, Yuan Jiang, Zhiguang Cao, Yaoxin Wu, Jie Zhang

Research Collection School Of Computing and Information Systems

Recently, neural heuristics based on deep learning have reported encouraging results for solving vehicle routing problems (VRPs), especially on independent and identically distributed (i.i.d.) instances, e.g. uniform. However, in the presence of a distribution shift for the testing instances, their performance becomes considerably inferior. In this paper, we propose a multi-view graph contrastive learning (MVGCL) approach to enhance the generalization across different distributions, which exploits a graph pattern learner in a self-supervised fashion to facilitate a neural heuristic equipped with an active search scheme. Specifically, our MVGCL first leverages graph contrastive learning to extract transferable patterns from VRP graphs to …

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A Survey On Proactive Dialogue Systems: Problems, Methods, And Prospects, Yang Deng, Wenqiang Lei, Wai Lam, Tat-Seng Chua Aug 2023

A Survey On Proactive Dialogue Systems: Problems, Methods, And Prospects, Yang Deng, Wenqiang Lei, Wai Lam, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Proactive dialogue systems, related to a wide range of real-world conversational applications, equip the conversational agent with the capability of leading the conversation direction towards achieving pre-defined targets or fulfilling certain goals from the system side. It is empowered by advanced techniques to progress to more complicated tasks that require strategical and motivational interactions. In this survey, we provide a comprehensive overview of the prominent problems and advanced designs for conversational agent's proactivity in different types of dialogues. Furthermore, we discuss challenges that meet the real-world application needs but require a greater research focus in the future. We hope that …

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Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu Aug 2023

Evolve Path Tracer: Early Detection Of Malicious Addresses In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

With the boom of cryptocurrency and its concomitant financial risk concerns, detecting fraudulent behaviors and associated malicious addresses has been drawing significant research effort. Most existing studies, however, rely on the full history features or full-fledged address transaction networks, both of which are unavailable in the problem of early malicious address detection and therefore failing them for the task. To detect fraudulent behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose …

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Diffusion Variational Autoencoder For Tackling Stochasticity In Multi-Step Regression Stock Price Prediction, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua Aug 2023

Diffusion Variational Autoencoder For Tackling Stochasticity In Multi-Step Regression Stock Price Prediction, Kelvin J.L. Koa, Yunshan Ma, Ritchie Ng, Tat‑Seng Chua

Research Collection School Of Computing and Information Systems

Multi-step stock price prediction over a long-term horizon is crucial for forecasting its volatility, allowing financial institutions to price and hedge derivatives, and banks to quantify the risk in their trading books. Additionally, most financial regulators also require a liquidity horizon of several days for institutional investors to exit their risky assets, in order to not materially affect market prices. However, the task of multi-step stock price prediction is challenging, given the highly stochastic nature of stock data. Current solutions to tackle this problem are mostly designed for single-step, classification-based predictions, and are limited to low representation expressiveness. The problem …

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Decoding The Underlying Meaning Of Multimodal Hateful Memes, Ming Shan Hee, Wen Haw Chong, Roy Ka-Wei Lee Aug 2023

Decoding The Underlying Meaning Of Multimodal Hateful Memes, Ming Shan Hee, Wen Haw Chong, Roy Ka-Wei Lee

Research Collection School Of Computing and Information Systems

Recent studies have proposed models that yielded promising performance for the hateful meme classification task. Nevertheless, these proposed models do not generate interpretable explanations that uncover the underlying meaning and support the classification output. A major reason for the lack of explainable hateful meme methods is the absence of a hateful meme dataset that contains ground truth explanations for benchmarking or training. Intuitively, having such explanations can educate and assist content moderators in interpreting and removing flagged hateful memes. This paper address this research gap by introducing Hateful meme with Reasons Dataset (HatReD), which is a new multimodal hateful meme …

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