Open Access. Powered by Scholars. Published by Universities.®
Databases and Information Systems Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Numerical Analysis and Scientific Computing (661)
- Artificial Intelligence and Robotics (344)
- Social and Behavioral Sciences (342)
- Graphics and Human Computer Interfaces (310)
- Software Engineering (234)
-
- Business (225)
- Communication (216)
- Engineering (192)
- Social Media (186)
- Theory and Algorithms (175)
- Computer Engineering (165)
- Information Security (142)
- OS and Networks (115)
- Programming Languages and Compilers (97)
- E-Commerce (81)
- Data Storage Systems (69)
- Medicine and Health Sciences (66)
- Public Affairs, Public Policy and Public Administration (54)
- Education (53)
- Management Information Systems (50)
- Transportation (46)
- Health Information Technology (45)
- International and Area Studies (43)
- Asian Studies (42)
- Finance and Financial Management (42)
- Digital Communications and Networking (32)
- Technology and Innovation (25)
- Keyword
-
- Social media (53)
- Machine learning (49)
- Online learning (43)
- Deep learning (40)
- Data mining (39)
-
- Artificial intelligence (34)
- Query processing (29)
- Twitter (28)
- Classification (24)
- Reinforcement learning (24)
- Algorithms (23)
- Deep Learning (23)
- Neural networks (23)
- Clustering (21)
- Graph neural networks (20)
- Machine Learning (20)
- Semantics (20)
- Task analysis (20)
- Algorithm (19)
- Visualization (19)
- Anomaly detection (18)
- Cloud computing (18)
- Image retrieval (18)
- Recommender systems (18)
- Social network (18)
- Performance (17)
- Sentiment analysis (17)
- Singapore (17)
- Natural language processing (16)
- Social networks (16)
- Publication Year
Articles 871 - 900 of 3441
Full-Text Articles in Databases and Information Systems
Occluded Person Re-Identification With Single-Scale Global Representations, Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen
Occluded Person Re-Identification With Single-Scale Global Representations, Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen
Research Collection School Of Computing and Information Systems
Occluded person re-identification (ReID) aims at re-identifying occluded pedestrians from occluded or holistic images taken across multiple cameras. Current state-of-the-art (SOTA) occluded ReID models rely on some auxiliary modules, including pose estimation, feature pyramid and graph matching modules, to learn multi-scale and/or part-level features to tackle the occlusion challenges. This unfortunately leads to complex ReID models that (i) fail to generalize to challenging occlusions of diverse appearance, shape or size, and (ii) become ineffective in handling non-occluded pedestrians. However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians. To address these two …
Missing Data Imputation For Solar Yield Prediction Using Temporal Multi-Modal Variational Auto-Encoder, Meng Shen, Huaizheng Zhang, Yixin Cao, Fan Yang, Yonggang Wen
Missing Data Imputation For Solar Yield Prediction Using Temporal Multi-Modal Variational Auto-Encoder, Meng Shen, Huaizheng Zhang, Yixin Cao, Fan Yang, Yonggang Wen
Research Collection School Of Computing and Information Systems
The accurate and robust prediction of short-term solar power generation is significant for the management of modern smart grids, where solar power has become a major energy source due to its green and economical nature. However, the solar yield prediction can be difficult to conduct in the real world where hardware and network issues can make the sensors unreachable. Such data missing problem is so prevalent that it degrades the performance of deployed prediction models and even fails the model execution. In this paper, we propose a novel temporal multi-modal variational auto-encoder (TMMVAE) model, to enhance the robustness of short-term …
Countering Attacker Data Manipulation In Security Games, Andrew R. Butler, Thanh H. Nguyen, Arunesh Sinha
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 …
Aixfood'21: 3rd Workshop On Aixfood, Ricardo Guerrero, Michael Spranger, Shuqiang Jiang, Chong-Wah Ngo
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 …
Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman
Mining Informal & Short Student Self-Reflections For Detecting Challenging Topics: A Learning Outcomes Insight Dashboard, De Lin Ong, Gottipati Swapna, Siaw Ling Lo, Venky Shankararaman
Research Collection School Of Computing and Information Systems
Having students write short self-reflections at the end of each weekly session enables them to reflect on what they have learnt in the session and topics they find challenging. Analysing these self-reflections provides instructors with insights on how to address the missing conceptions and misconceptions of the students and appropriately plan and deliver the next session. Currently, manual methods adopted to analyse these student reflections are time consuming and tedious. This paper proposes a solution model that uses content mining and NLP techniques to automate the analysis of short self-reflections. We evaluate the solution model by studying its implementation in …
Target-Guided Emotion-Aware Chat Machine, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu, Shanshan Feng
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 …
A Large-Scale Benchmark For Food Image Segmentation, Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C. H. Hoi, Qianru Sun
A Large-Scale Benchmark For Food Image Segmentation, Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C. H. Hoi, Qianru Sun
Research Collection School Of Computing and Information Systems
Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks—the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different …
From Continuity To Editability: Inverting Gans With Consecutive Images, Yangyang Xu, Yong Du, Wenpeng Xiao, Xuemiao Xu, Shengfeng He
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 …
Privacy-Preserving Voluntary-Tallying Leader Election For Internet Of Things, Tong Wu, Guomin Yang, Liehuang Zhu, Yulin Wu
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. …
Multi-Modal Recommender Systems: Hands-On Exploration, Quoc Tuan Truong, Aghiles Salah, Hady Wirawan Lauw
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 …
Visilence: An Interactive Visualization Tool For Error Resilience Analysis, Shaolun Ruan, Yong Wang, Qiang Guan
Visilence: An Interactive Visualization Tool For Error Resilience Analysis, Shaolun Ruan, Yong Wang, Qiang Guan
Research Collection School Of Computing and Information Systems
Soft errors have become one of the major concerns for HPC applications, as those errors can result in seriously corrupted outcomes, such as silent data corruptions (SDCs). Prior studies on error resilience have studied the robustness of HPC applications. However, it is still difficult for program developers to identify potential vulnerability to soft errors. In this paper, we present Visilence, a novel visualization tool to visually analyze error vulnerability based on the control-flow graph generated from HPC applications. Visilence efficiently visualizes the affected program states under injected errors and presents the visual analysis of the most vulnerable parts of an …
Condensing A Sequence To One Informative Frame For Video Recognition, Qiu. Zhaofan, Ting Yao, Yan Shu, Chong-Wah Ngo, Tao Mei
Condensing A Sequence To One Informative Frame For Video Recognition, Qiu. Zhaofan, Ting Yao, Yan Shu, Chong-Wah Ngo, Tao Mei
Research Collection School Of Computing and Information Systems
Video is complex due to large variations in motion and rich content in fine-grained visual details. Abstracting useful information from such information-intensive media requires exhaustive computing resources. This paper studies a two-step alternative that first condenses the video sequence to an informative" frame" and then exploits off-the-shelf image recognition system on the synthetic frame. A valid question is how to define" useful information" and then distill it from a video sequence down to one synthetic frame. This paper presents a novel Informative Frame Synthesis (IFS) architecture that incorporates three objective tasks, ie, appearance reconstruction, video categorization, motion estimation, and two …
A First Look At Accessibility Issues In Popular Github Projects, Tingting Bi, Xin Xia, David Lo, Aldeida Aleti
A First Look At Accessibility Issues In Popular Github Projects, Tingting Bi, Xin Xia, David Lo, Aldeida Aleti
Research Collection School Of Computing and Information Systems
Accessibility design elements allow people to access software products and services independent of their different abilities. However, accessibility is challenging to handle and whether accessibility is widely considered in software projects is unclear. In this work, we aim to understand if accessibility is a prevalent consideration in practice, what accessibility issues are discussed in GitHub projects, what potential reasons cause accessibility issues, and what solutions (e.g., tools and standards) are applied for addressing accessibility issues. In this work, we collect 11,820 accessibility issues and their threads discussed by developers in popular GitHub projects. We manually analyzed and grouped the collected …
Impact Of Digital Nudging On Information Security Behavior: An Experimental Study On Framing And Priming In Cybersecurity, Kavya Sharma, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Maggie X. Cheng
Impact Of Digital Nudging On Information Security Behavior: An Experimental Study On Framing And Priming In Cybersecurity, Kavya Sharma, Xinhui Zhan, Fiona Fui-Hoon Nah, Keng Siau, Maggie X. Cheng
Research Collection School Of Computing and Information Systems
Purpose: Phishing attacks are the most common cyber threats targeted at users. Digital nudging in the form of framing and priming may reduce user susceptibility to phishing. This research focuses on two types of digital nudging, framing and priming, and examines the impact of framing and priming on users' behavior (i.e. action) in a cybersecurity setting. It draws on prospect theory, instance-based learning theory and dual-process theory to generate the research hypotheses. Design/methodology/approach: A 3 × 2 experimental study was carried out to test the hypotheses. The experiment consisted of three levels for framing (i.e. no framing, negative framing and …
Prediction Of Synthetic Lethal Interactions In Human Cancers Using Multi-View Graph Auto-Encoder, Zhifeng Hao, Di Wu, Yuan Fang, Min Wu, Ruichu Cai, Xiaoli Li
Prediction Of Synthetic Lethal Interactions In Human Cancers Using Multi-View Graph Auto-Encoder, Zhifeng Hao, Di Wu, Yuan Fang, Min Wu, Ruichu Cai, Xiaoli Li
Research Collection School Of Computing and Information Systems
Synthetic lethality (SL) is a very important concept for the development of targeted anticancer drugs. However, experimental methods for SL detection often suffer from various issues like high cost and low consistency across cell lines. Hence, computational methods for predicting novel SLs have recently emerged as complements for wet-lab experiments. In addition, SL data can be represented as a graph where nodes are genes and edges are the SL interactions. It is thus motivated to design advanced graph-based machine learning algorithms for SL prediction. In this paper, we propose a novel SL prediction method using Multi-view Graph Auto-Encoder (SLMGAE). We …
Token Shift Transformer For Video Classification, Zhang Hao, Yanbin. Hao, Chong-Wah Ngo
Token Shift Transformer For Video Classification, Zhang Hao, Yanbin. Hao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Transformer achieves remarkable successes in understanding 1 and 2-dimensional signals (e.g., NLP and Image Content Understanding). As a potential alternative to convolutional neural networks, it shares merits of strong interpretability, high discriminative power on hyper-scale data, and flexibility in processing varying length inputs. However, its encoders naturally contain computational intensive operations such as pair-wise self-attention, incurring heavy computational burden when being applied on the complex 3-dimensional video signals. This paper presents Token Shift Module (i.e., TokShift), a novel, zero-parameter, zero-FLOPs operator, for modeling temporal relations within each transformer encoder. Specifically, the TokShift barely temporally shifts partial [Class] token features back-and-forth …
Integrated Discourse Analysis & Learning Skills Framework For Class Conversations, Devyn Wei Hung Tan, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky
Integrated Discourse Analysis & Learning Skills Framework For Class Conversations, Devyn Wei Hung Tan, Gottipati Swapna, Kyong Jin Shim, Shankararaman, Venky
Research Collection School Of Computing and Information Systems
Constructive interactions through discussion forums allow students to open their horizons and thought processes to acquire more knowledge and develop skills. Thus, discussion forums play an important role in supporting learning. Additionally, the discussion forum provides the content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. One approach to understanding the student learning experience is through the analysis of the discussion threads. This research proposes the application of discourse analysis and collaborative learning frameworks to discussion forums to gain further insights into the student’s learning in a classroom. …
Assessing Generalizability Of Codebert, Xin Zhou, Donggyun Han, David Lo
Assessing Generalizability Of Codebert, Xin Zhou, Donggyun Han, David Lo
Research Collection School Of Computing and Information Systems
Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code search dataset, is proposed. Although promising, CodeBERT has not been evaluated beyond its pre-trained dataset for NL-PL tasks. Also, it has only been shown effective on two tasks that are close in nature to its pre-trained data. This raises two questions: Can CodeBERT generalize beyond its pre-trained data? Can it generalize to …
Quantum Computing: Computational Excellence For Society 5.0, Paul R. Griffin, Michael Boguslavsky, Junye Huang, Robert J. Kauffman, Brian R. Tan
Quantum Computing: Computational Excellence For Society 5.0, Paul R. Griffin, Michael Boguslavsky, Junye Huang, Robert J. Kauffman, Brian R. Tan
Research Collection School Of Computing and Information Systems
In this chapter, we consider which general business problems may be suitable for exploring the utilization of quantum computing and provide a framework for applying quantum computing. The characteristics of quantum computing systems are mapped into business problems to show the potential advantages of quantum computing. The framework shows how quantum computing can be applied in general, and a use case is offered for quantum machine learning (QML) related to the credit ratings of small and medium-size enterprises (SMEs).
Cloudnplay: Resource Optimization For A Cloud-Native Gaming System, Angelus Wibowo, Nguyen Binh Duong Ta
Cloudnplay: Resource Optimization For A Cloud-Native Gaming System, Angelus Wibowo, Nguyen Binh Duong Ta
Research Collection School Of Computing and Information Systems
Cloud gaming enables people playing graphically intensive games from their less powerful, or even outdated computing devices. It is challenging to realize cloud gaming as it requires minimal latency in server-side processing, rendering and streaming, which are expensive in terms of resource requirements, e.g., powerful GPU servers. Commercial gaming providers, e.g., Google Stadia, Amazon Luna, etc., hardly disclose any information on how they optimize gaming performance and cloud cost. In this work, we aim to investigate resource cost optimization for such cloud gaming systems. In contrast to previous work which have been focusing more on theoretical approaches, we deliver a …
Design And Supervision Model Of Group Projects For Active Learning, Yi Meng Lau, Kyong Jin Shim, Swapna Gottipati
Design And Supervision Model Of Group Projects For Active Learning, Yi Meng Lau, Kyong Jin Shim, Swapna Gottipati
Research Collection School Of Computing and Information Systems
This research paper presents a group project framework for a second-year programming course, which was conducted during the COVID-19 pandemic. The framework offers well defined stages of the group project which allow students to work on their choice of a real-world problem, integrate their learnings from previous courses, and present a working solution. In the group project, students actively participate, reflect, and contribute to achieving the goals set in the learning objectives of the course. Our framework incorporates key features from Kolb’s Experiential Learning Theory (1984) and principles of active learning from Barnes (1989) to achieve active and experiential learning …
Latent Class Analysis For Identifying Subclasses Of Depression Using Jmp Pro 16, Karishma Yadav, Fei Fei Sue-Ann Seet, Tin Seong Kam, Tin Seong Kam
Latent Class Analysis For Identifying Subclasses Of Depression Using Jmp Pro 16, Karishma Yadav, Fei Fei Sue-Ann Seet, Tin Seong Kam, Tin Seong Kam
Research Collection School Of Computing and Information Systems
According to WHO, “Depression is a leading cause of disability worldwide and is a major contributor to the overall global burden of disease”. A major stumbling block in the care of depressed patients remains the accurate diagnosis of the severity of depression. Patient Health Questionnaire (PHQ-9), a 9-question instrument is widely used for diagnosing and determining the severity of depression. However, the popularly used 5-Category of depression severity based on the sum of responses to the 9 questions was overly subjective. In view of this limitation, our paper aims to demonstrate how Latent Class Analysis of JMP Pro can be …
Can Differential Testing Improve Automatic Speech Recognition Systems?, Muhammad Hilmi Asyrofi, Zhou Yang, Jieke Shi, Chu Wei Quan, David Lo
Can Differential Testing Improve Automatic Speech Recognition Systems?, Muhammad Hilmi Asyrofi, Zhou Yang, Jieke Shi, Chu Wei Quan, David Lo
Research Collection School Of Computing and Information Systems
Due to the widespread adoption of Automatic Speech Recognition (ASR) systems in many critical domains, ensuring the quality of recognized transcriptions is of great importance. A recent work, CrossASR++, can automatically uncover many failures in ASR systems by taking advantage of the differential testing technique. It employs a Text-To-Speech (TTS) system to synthesize audios from texts and then reveals failed test cases by feeding them to multiple ASR systems for cross-referencing. However, no prior work tries to utilize the generated test cases to enhance the quality of ASR systems. In this paper, we explore the subsequent improvements brought by leveraging …
Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
Weakly-Supervised Video Anomaly Detection With Contrastive Learning Of Long And Short-Range Temporal Features, Yu Tian, Guansong Pang, Yuanhong Chen, Rajvinder Singh, Johan W. Verjans, Gustavo Carneiro
Research Collection School Of Computing and Information Systems
Anomaly detection with weakly supervised video-level labels is typically formulated as a multiple instance learning (MIL) problem, in which we aim to identify snippets containing abnormal events, with each video represented as a bag of video snippets. Although current methods show effective detection performance, their recognition of the positive instances, i.e., rare abnormal snippets in the abnormal videos, is largely biased by the dominant negative instances, especially when the abnormal events are subtle anomalies that exhibit only small differences compared with normal events. This issue is exacerbated in many methods that ignore important video temporal dependencies. To address this issue, …
Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He
Differentiated Learning For Multi-Modal Domain Adaptation, Jianming Lv, Kaijie Liu, Shengfeng He
Research Collection School Of Computing and Information Systems
Directly deploying a trained multi-modal classifier to a new environment usually leads to poor performance due to the well-known domain shift problem. Existing multi-modal domain adaptation methods treated each modality equally and optimize the sub-models of different modalities synchronously. However, as observed in this paper, the degrees of domain shift in different modalities are usually diverse. We propose a novel Differentiated Learning framework to make use of the diversity between multiple modalities for more effective domain adaptation. Specifically, we model the classifiers of different modalities as a group of teacher/student sub-models, and a novel Prototype based Reliability Measurement is presented …
Deep Learning For Image Super-Resolution: A Survey, Zhihao Wang, Jian Chen, Steven C. H. Hoi
Deep Learning For Image Super-Resolution: A Survey, Zhihao Wang, Jian Chen, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
Image Super-Resolution (SR) is an important class of image processing techniqueso enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning techniques. This article aims to provide a comprehensive survey on recent advances of image super-resolution using deep learning approaches. In general, we can roughly group the existing studies of SR techniques into three major categories: supervised SR, unsupervised SR, and domain-specific SR. In addition, we also cover some other important issues, such as publicly available benchmark datasets and performance evaluation metrics. Finally, we conclude this survey by …
Online Learning: A Comprehensive Survey, Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao
Online Learning: A Comprehensive Survey, Steven C. H. Hoi, Doyen Sahoo, Jing Lu, Peilin Zhao
Research Collection School Of Computing and Information Systems
Online learning represents a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time. The goal of online learning is to maximize the accuracy/correctness for the sequence of predictions/decisions made by the online learner given the knowledge of correct answers to previous prediction/learning tasks and possibly additional information. This is in contrast to traditional batch or offline machine learning methods that are often designed to learn a model from the entire training data set at once. Online …
Unified And Incremental Simrank: Index-Free Approximation With Scheduled Principle, Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin C.-C. Chang, Hongtai Cao, Zhen Jiang, Minghui Wu
Unified And Incremental Simrank: Index-Free Approximation With Scheduled Principle, Fanwei Zhu, Yuan Fang, Kai Zhang, Kevin C.-C. Chang, Hongtai Cao, Zhen Jiang, Minghui Wu
Research Collection School Of Computing and Information Systems
SimRank is a popular link-based similarity measure on graphs. It enables a variety of applications with different modes of querying (e.g., single-pair, single-source and all-pair modes). In this paper, we propose UISim, a unified and incremental framework for all SimRank modes based on a scheduled approximation principle. UISim processes queries with incremental and prioritized exploration of the entire computation space, and thus allows flexible tradeoff of time and accuracy. On the other hand, it creates and shares common “building blocks” for online computation without relying on indexes, and thus is efficient to handle both static and dynamic graphs. Our experiments …
Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo
Biasheal: On-The-Fly Black-Box Healing Of Bias In Sentiment Analysis Systems, Zhou Yang, Harshit Jain, Jieke Shi, Muhammad Hilmi Asyrofi, David Lo
Research Collection School Of Computing and Information Systems
Although Sentiment Analysis (SA) is widely applied in many domains, existing research has revealed that the unfairness in SA systems can be harmful to the welfare of less privileged people. Several works propose pre-processing and in-processing methods to eliminate bias in SA systems, but little attention is paid to utilizing post-processing methods to heal bias. Postprocessing methods are particularly important for systems that use third-party SA services. Systems that use such services have no access to the SA engine or its training data and thus cannot apply pre-processing nor in-processing methods. Therefore, this paper proposes a black-box post-processing method to …
Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang
Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang
Research Collection School Of Computing and Information Systems
Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identification. Our experiment results suggest that BERT indeed can separate the literal and idiomatic usages of a PIE with high accuracy. It is also able to encode the idiomatic meaning of a PIE to some extent.