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Articles 1 - 30 of 927
Full-Text Articles in Physical Sciences and Mathematics
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
An Adaptive Large Neighborhood Search For The Multi-Vehicle Profitable Tour Problem With Flexible Compartments And Mandatory Customers, Vincent F. Yu, Nabila Yuraisyah Salsabila, Aldy Gunawan, Anggun Nurfitriani Handoko
Research Collection School Of Computing and Information Systems
The home-refill delivery system is a business model that addresses the concerns of plastic waste and its impact on the environment. It allows customers to pick up their household goods at their doorsteps and refill them into their own containers. However, the difficulty in accessing customers’ locations and product consolidations are undeniable challenges. To overcome these issues, we introduce a new variant of the Profitable Tour Problem, named the multi-vehicle profitable tour problem with flexible compartments and mandatory customers (MVPTPFC-MC). The objective is to maximize the difference between the total collected profit and the traveling cost. We model the proposed …
Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning
Learning Adversarial Semantic Embeddings For Zero-Shot Recognition In Open Worlds, Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning
Research Collection School Of Computing and Information Systems
Zero-Shot Learning (ZSL) focuses on classifying samples of unseen classes with only their side semantic information presented during training. It cannot handle real-life, open-world scenarios where there are test samples of unknown classes for which neither samples (e.g., images) nor their side semantic information is known during training. Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes. To tackle this combined ZSL and OSR problem, we consider the case of “Zero-Shot Open-Set Recognition” (ZS-OSR), where a model is trained under the ZSL …
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Editorial: Emerging On-Demand Passenger And Logistics Systems: Modelling, Optimization, And Data Analytics, Jintao Ke, Hai Wang, Neda Masoud, Maximilian Schiffer, Goncalo H. A. Correia
Research Collection School Of Computing and Information Systems
The proliferation of smart personal devices and mobile internet access has fueled numerous advancements in on-demand transportation services. These services are facilitated by online digital platforms and range from providing rides to delivering products. Their influence is transforming transportation systems and leaving a mark on changing individual mobility, activity patterns, and consumption behaviors. For instance, on-demand transportation companies such as Uber, Lyft, Grab, and DiDi have become increasingly vital for meeting urban transportation needs by connecting available drivers with passengers in real time. The recent surge in door-to-door food delivery (e.g., Uber Eats, DoorDash, Meituan); grocery delivery (e.g., Amazon Fresh, …
Discovering Significant Topics From Legal Decisions With Selective Inference, Jerrold Tsin Howe Soh
Discovering Significant Topics From Legal Decisions With Selective Inference, Jerrold Tsin Howe Soh
Research Collection Yong Pung How School Of Law
We propose and evaluate an automated pipeline for discovering significant topics from legal decision texts by passing features synthesized with topic models through penalized regressions and post-selection significance tests. The method identifies case topics significantly correlated with outcomes, topic-word distributions which can be manually interpreted to gain insights about significant topics, and case-topic weights which can be used to identify representative cases for each topic. We demonstrate the method on a new dataset of domain name disputes and a canonical dataset of European Court of Human Rights violation cases. Topic models based on latent semantic analysis as well as language …
Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan
Meta-Interpretive Learning With Reuse, Rong Wang, Jun Sun, Cong Tian, Zhenhua Duan
Research Collection School Of Computing and Information Systems
Inductive Logic Programming (ILP) is a research field at the intersection between machine learning and logic programming, focusing on developing a formal framework for inductively learning relational descriptions in the form of logic programs from examples and background knowledge. As an emerging method of ILP, Meta-Interpretive Learning (MIL) leverages the specialization of a set of higher-order metarules to learn logic programs. In MIL, the input includes a set of examples, background knowledge, and a set of metarules, while the output is a logic program. MIL executes a depth-first traversal search, where its program search space expands polynomially with the number …
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
Non-Monotonic Generation Of Knowledge Paths For Context Understanding, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Knowledge graphs can be used to enhance text search and access by augmenting textual content with relevant background knowledge. While many large knowledge graphs are available, using them to make semantic connections between entities mentioned in the textual content remains to be a difficult task. In this work, we therefore introduce contextual path generation (CPG) which refers to the task of generating knowledge paths, contextual path, to explain the semantic connections between entities mentioned in textual documents with given knowledge graph. To perform CPG task well, one has to address its three challenges, namely path relevance, incomplete knowledge graph, and …
Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang
Knowledge Generation For Zero-Shot Knowledge-Based Vqa, Rui Cao, Jing Jiang
Research Collection School Of Computing and Information Systems
Previous solutions to knowledge-based visual question answering (K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitly show the knowledge needed to answer the questions and thus lack interpretability. Inspired by recent work on knowledge generation from LLMs for text-based QA, in this work we propose and test a similar knowledge-generation-based K-VQA method, which first generates knowledge from an LLM and then incorporates the generated …
Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang
Revisiting The Markov Property For Machine Translation, Cunxiao Du, Hao Zhou, Zhaopeng Tu, Jing Jiang
Research Collection School Of Computing and Information Systems
In this paper, we re-examine the Markov property in the context of neural machine translation. We design a Markov Autoregressive Transformer (MAT) and undertake a comprehensive assessment of its performance across four WMT benchmarks. Our findings indicate that MAT with an order larger than 4 can generate translations with quality on par with that of conventional autoregressive transformers. In addition, counter-intuitively, we also find that the advantages of utilizing a higher-order MAT do not specifically contribute to the translation of longer sentences.
Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Monocular Bev Perception Of Road Scenes Via Front-To-Top View Projection, Wenxi Liu, Qi Li, Weixiang Yang, Jiaxin Cai, Yuanhong Yu, Yuexin Ma, Shengfeng He, Jia Pan
Research Collection School Of Computing and Information Systems
HD map reconstruction is crucial for autonomous driving. LiDAR-based methods are limited due to expensive sensors and time-consuming computation. Camera-based methods usually need to perform road segmentation and view transformation separately, which often causes distortion and missing content. To push the limits of the technology, we present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view given a front-view monocular image only. We propose a front-to-top view projection (FTVP) module, which takes the constraint of cycle consistency between views into account and makes full use of their correlation to strengthen …
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
T-Pickseer: Visual Analysis Of Taxi Pick-Up Point Selection Behavior, Shuxian Gu, Yemo Dai, Zezheng Feng, Yong Wang, Haipeng Zeng
Research Collection School Of Computing and Information Systems
Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hotspot regions of pick-up points, which can make it easier for drivers to pick-up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory …
Catnet: Cross-Modal Fusion For Audio-Visual Speech Recognition, Xingmei Wang, Jianchen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
Catnet: Cross-Modal Fusion For Audio-Visual Speech Recognition, Xingmei Wang, Jianchen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
Research Collection School Of Computing and Information Systems
Automatic speech recognition (ASR) is a typical pattern recognition technology that converts human speeches into texts. With the aid of advanced deep learning models, the performance of speech recognition is significantly improved. Especially, the emerging Audio–Visual Speech Recognition (AVSR) methods achieve satisfactory performance by combining audio-modal and visual-modal information. However, various complex environments, especially noises, limit the effectiveness of existing methods. In response to the noisy problem, in this paper, we propose a novel cross-modal audio–visual speech recognition model, named CATNet. First, we devise a cross-modal bidirectional fusion model to analyze the close relationship between audio and visual modalities. Second, …
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft
Recommendations With Minimum Exposure Guarantees: A Post-Processing Framework, Ramon Lopes, Rodrigo Alves, Antoine Ledent, Rodrygo L. T. Santos, Marius Kloft
Research Collection School Of Computing and Information Systems
Relevance-based ranking is a popular ingredient in recommenders, but it frequently struggles to meet fairness criteria because social and cultural norms may favor some item groups over others. For instance, some items might receive lower ratings due to some sort of bias (e.g. gender bias). A fair ranking should balance the exposure of items from advantaged and disadvantaged groups. To this end, we propose a novel post-processing framework to produce fair, exposure-aware recommendations. Our approach is based on an integer linear programming model maximizing the expected utility while satisfying a minimum exposure constraint. The model has fewer variables than previous …
Machine Learning For Refining Knowledge Graphs: A Survey, Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan
Machine Learning For Refining Knowledge Graphs: A Survey, Budhitama Subagdja, D. Shanthoshigaa, Zhaoxia Wang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in knowledge graphs. With the growing popularity of KG in various domains, many techniques involving machine learning have been applied, but there is no survey dedicated to machine learning-based KG refinement yet. Based on a novel framework following the KG refinement process, this paper presents a survey of machine learning approaches to KG refinement according to the kind of operations in KG refinement, the training datasets, mode of learning, and process multiplicity. Furthermore, the survey aims to provide broad practical insights into …
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Research Collection School Of Computing and Information Systems
The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …
Efficient Unsupervised Video Hashing With Contextual Modeling And Structural Controlling, Jingru Duan, Yanbin Hao, Bin Zhu, Lechao Cheng, Pengyuan Zhou, Xiang Wang
Efficient Unsupervised Video Hashing With Contextual Modeling And Structural Controlling, Jingru Duan, Yanbin Hao, Bin Zhu, Lechao Cheng, Pengyuan Zhou, Xiang Wang
Research Collection School Of Computing and Information Systems
The most important effect of the video hashing technique is to support fast retrieval, which is benefiting from the high efficiency of binary calculation. Current video hash approaches are thus mainly targeted at learning compact binary codes to represent video content accurately. However, they may overlook the generation efficiency for hash codes, i.e., designing lightweight neural networks. This paper proposes an method, which is not only for computing compact hash codes but also for designing a lightweight deep model. Specifically, we present an MLP-based model, where the video tensor is split into several groups and multiple axial contexts are explored …
Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He
Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He
Research Collection School Of Computing and Information Systems
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …
A Big Data Approach To Augmenting The Huff Model With Road Network And Mobility Data For Store Footfall Prediction, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau
A Big Data Approach To Augmenting The Huff Model With Road Network And Mobility Data For Store Footfall Prediction, Ming Hui Tan, Kar Way Tan, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Conventional methodologies for new retail store catchment area and footfall estimation rely on ground surveys which are costly and time-consuming. This study augments existing research in footfall estimation through the innovative integration of mobility data and road network to create population-weighted centroids and delineate residential neighbourhoods via a community detection algorithm. Our findings are then used to enhance Huff Model which is commonly used in site selection and footfall estimation. Our approach demonstrated the vast potential residing within big data where we harness the power of mobility data and road network information, offering a cost-effective and scalable alternative. It obviates …
Last Digit Tendency: Lucky Number And Psychological Rounding In Mobile Transactions, Hai Wang, Tian Lu, Yingjie Zhang, Yue Wu, Yiheng Sun, Jingran Dong, Wen Huang
Last Digit Tendency: Lucky Number And Psychological Rounding In Mobile Transactions, Hai Wang, Tian Lu, Yingjie Zhang, Yue Wu, Yiheng Sun, Jingran Dong, Wen Huang
Research Collection School Of Computing and Information Systems
The distribution of digits in numbers obtained from different sources reveals interesting patterns. The well-known Benford’s law states that the first digits in many real-life numerical data sets have an asymmetric, logarithmic distribution in which small digits are more common; this asymmetry diminishes for subsequent digits, and the last digit tends to be uniformly distributed. In this paper, we investigate the digit distribution of numbers in a large mobile transaction data set with 835 million mobile transactions and payments made by approximately 460,000 users in more than 300 cities. Although the first digits of the numbers in these mobile transactions …
Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek
Knowledge Graph Enhanced Aspect-Based Sentiment Analysis Incorporating External Knowledge, Autumn Teo, Zhaoxia Wang, Haibo Pen, Budhitama Subagdja, Seng-Beng Ho, Boon Kiat Quek
Research Collection School Of Computing and Information Systems
Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different …
Designing An Overseas Experiential Course In Data Science, Hua Leong Fwa, Graham Ng
Designing An Overseas Experiential Course In Data Science, Hua Leong Fwa, Graham Ng
Research Collection School Of Computing and Information Systems
Unprecedented demand for data science professionals in the industry has led to many educational institutions launching new data science courses. It is however imperative that students of data science programmes learn through execution of real-world, authentic projects on top of acquiring foundational knowledge on the basics of data science. In the process of working on authentic, real-world projects, students not only create new knowledge but also learn to solve open, sophisticated, and ill-structured problems in an inter-disciplinary fashion. In this paper, we detailed our approach to design a data science curriculum premised on learners solving authentic data science problems sourced …
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink
Understanding The Impact Of Trade Policy Effect Uncertainty On Firm-Level Innovation Investment: A Deep Learning Approach, Daniel Chang, Nan Hu, Peng Liang, Morgan Swink
Research Collection School Of Computing and Information Systems
Integrating the real options perspective and resource dependence theory, this study examines how firms adjust their innovation investments to trade policy effect uncertainty (TPEU), a less studied type of firm specific, perceived environmental uncertainty in which managers have difficulty predicting how potential policy changes will affect business operations. To develop a text-based, context-dependent, time-varying measure of firm-level perceived TPEU, we apply Bidirectional Encoder Representations from Transformers (BERT), a state-of-the-art deep learning approach. We apply BERT to analyze the texts of mandatory Management Discussion and Analysis (MD&A) sections of annual reports for a sample of 22,669 firm-year observations from 3,181 unique …
Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua
Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
As one of the core video semantic understanding tasks, Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject to two key drawbacks, including the lack of fine-grained spatial scene perception and the insufficiently modeling of video temporality. Towards this end, this work explores a novel holistic spatio-temporal scene graph (namely HostSG) representation based on the existing dynamic scene graph structures, which well model both the fine-grained spatial semantics and temporal …
Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Large-Scale Graph Label Propagation On Gpus, Chang Ye, Yuchen Li, Bingsheng He, Zhao Li, Jianling Sun
Research Collection School Of Computing and Information Systems
Graph label propagation (LP) is a core component in many downstream applications such as fraud detection, recommendation and image segmentation. In this paper, we propose GLP, a GPU-based framework to enable efficient LP processing on large-scale graphs. By investigating the data processing pipeline in a large e-commerce platform, we have identified two key challenges on integrating GPU-accelerated LP processing to the pipeline: (1) programmability for evolving application logics; (2) demand for real-time performance. Motivated by these challenges, we offer a set of expressive APIs that data engineers can customize and deploy efficient LP algorithms on GPUs with ease. To achieve …
Heterogeneous Graph Neural Network With Multi-View Representation Learning, Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu
Heterogeneous Graph Neural Network With Multi-View Representation Learning, Zezhi Shao, Yongjun Xu, Wei Wei, Fei Wang, Zhao Zhang, Feida Zhu
Research Collection School Of Computing and Information Systems
In recent years, graph neural networks (GNNs)-based methods have been widely adopted for heterogeneous graph (HG) embedding, due to their power in effectively encoding rich information from a HG into the low-dimensional node embeddings. However, previous works usually easily fail to fully leverage the inherent heterogeneity and rich semantics contained in the complex local structures of HGs. On the one hand, most of the existing methods either inadequately model the local structure under specific semantics, or neglect the heterogeneity when aggregating information from the local structure. On the other hand, representations from multiple semantics are not comprehensively integrated to obtain …
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Data Provenance Via Differential Auditing, Xin Mu, Ming Pang, Feida Zhu
Research Collection School Of Computing and Information Systems
With the rising awareness of data assets, data governance, which is to understand where data comes from, how it is collected, and how it is used, has been assuming evergrowing importance. One critical component of data governance gaining increasing attention is auditing machine learning models to determine if specific data has been used for training. Existing auditing techniques, like shadow auditing methods, have shown feasibility under specific conditions such as having access to label information and knowledge of training protocols. However, these conditions are often not met in most real-world applications. In this paper, we introduce a practical framework for …
Npf-200: A Multi-Modal Eye Fixation Dataset And Method For Non-Photorealistic Videos, Ziyu Yang, Sucheng Ren, Zongwei Wu, Nanxuan Zhao, Junle Wang, Jing Qin, Shengfeng He
Npf-200: A Multi-Modal Eye Fixation Dataset And Method For Non-Photorealistic Videos, Ziyu Yang, Sucheng Ren, Zongwei Wu, Nanxuan Zhao, Junle Wang, Jing Qin, Shengfeng He
Research Collection School Of Computing and Information Systems
Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies. This work aims to take a step forward to understand how humans perceive nonphotorealistic videos with eye fixation (i.e., saliency detection), which is critical for enhancing media production, artistic design, and game user experience. To fill in the gap of missing a suitable dataset for this research line, we present NPF-200, the first largescale multi-modal dataset of purely non-photorealistic videos with eye fixations. Our dataset has three characteristics: 1) it contains soundtracks that are essential according to vision and psychological studies; 2) it …
Matk: The Meme Analytical Tool Kit, Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang, Nirmalendu Prakash, Rui Cao, Roy Ka-Wei Lee
Matk: The Meme Analytical Tool Kit, Ming Shan Hee, Aditi Kumaresan, Nguyen Khoi Hoang, Nirmalendu Prakash, Rui Cao, Roy Ka-Wei Lee
Research Collection School Of Computing and Information Systems
The rise of social media platforms has brought about a new digital culture called memes. Memes, which combine visuals and text, can strongly influence public opinions on social and cultural issues. As a result, people have become interested in categorizing memes, leading to the development of various datasets and multimodal models that show promising results in this field. However, there is currently a lack of a single library that allows for the reproduction, evaluation, and comparison of these models using fair benchmarks and settings. To fill this gap, we introduce the Meme Analytical Tool Kit (MATK), an open-source toolkit specifically …
Quantumeyes: Towards Better Interpretability Of Quantum Circuits, Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang
Quantumeyes: Towards Better Interpretability Of Quantum Circuits, Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang
Research Collection School Of Computing and Information Systems
Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, …
Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen
Complex Knowledge Base Question Answering: A Survey, Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Zhao Wayne Xin, Ji Rong Wen
Research Collection School Of Computing and Information Systems
Knowledge base question answering (KBQA) aims to answer a question over a knowledge base (KB). Early studies mainly focused on answering simple questions over KBs and achieved great success. However, their performances on complex questions are still far from satisfactory. Therefore, in recent years, researchers propose a large number of novel methods, which looked into the challenges of answering complex questions. In this survey, we review recent advances in KBQA with the focus on solving complex questions, which usually contain multiple subjects, express compound relations, or involve numerical operations. In detail, we begin with introducing the complex KBQA task and …