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Articles 1 - 30 of 37
Full-Text Articles in Physical Sciences and Mathematics
C³: Code Clone-Based Identification Of Duplicated Components, Yanming Yang, Ying Zou, Xing Hu, David Lo, Chao Ni, John C. Grundy, Xin: Xia
C³: Code Clone-Based Identification Of Duplicated Components, Yanming Yang, Ying Zou, Xing Hu, David Lo, Chao Ni, John C. Grundy, Xin: Xia
Research Collection School Of Computing and Information Systems
Reinventing the wheel is a detrimental programming practice in software development that frequently results in the introduction of duplicated components. This practice not only leads to increased maintenance and labor costs but also poses a higher risk of propagating bugs throughout the system. Despite numerous issues introduced by duplicated components in software, the identification of component-level clones remains a significant challenge that existing studies struggle to effectively tackle. Specifically, existing methods face two primary limitations that are challenging to overcome: 1) Measuring the similarity between different components presents a challenge due to the significant size differences among them; 2) Identifying …
Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang
Neural Airport Ground Handling, Yaoxin Wu, Jianan Zhou, Yunwen Xia, Xianli Zhang, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Airport ground handling (AGH) offers necessary operations to flights during their turnarounds and is of great importance to the efficiency of airport management and the economics of aviation. Such a problem involves the interplay among the operations that leads to NP-hard problems with complex constraints. Hence, existing methods for AGH are usually designed with massive domain knowledge but still fail to yield high-quality solutions efficiently. In this paper, we aim to enhance the solution quality and computation efficiency for solving AGH. Particularly, we first model AGH as a multiple-fleet vehicle routing problem (VRP) with miscellaneous constraints including precedence, time windows, …
Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo
Robust Test Selection For Deep Neural Networks, Weifeng Sun, Meng Yan, Zhongxin Liu, David Lo
Research Collection School Of Computing and Information Systems
Deep Neural Networks (DNNs) have been widely used in various domains, such as computer vision and software engineering. Although many DNNs have been deployed to assist various tasks in the real world, similar to traditional software, they also suffer from defects that may lead to severe outcomes. DNN testing is one of the most widely used methods to ensure the quality of DNNs. Such method needs rich test inputs with oracle information (expected output) to reveal the incorrect behaviors of a DNN model. However, manually labeling all the collected test inputs is a labor-intensive task, which delays the quality assurance …
Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin
Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin
Research Collection School Of Computing and Information Systems
Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in this work, we propose a learning-based approach namely SnapVuln, which first utilizes multiple vulnerability-specific inter-procedural slicing algorithms to capture vulnerability semantics of various types and then employs a Gated Graph Neural Network (GGNN) with an attention mechanism to learn vulnerability semantics. We compare SnapVuln with state-of-the-art learning-based approaches on two public datasets, and confirm that SnapVuln outperforms them. We further perform an ablation study …
An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le
An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le
Research Collection School Of Computing and Information Systems
In this work, we experiment an idealistic approach for smart contract correctness verification and enforcement, based on the assumption that developers are either desired or required to provide a correctness specification due to the importance of smart contracts and the fact that they are immutable after deployment. We design a static verification system with a specification language which supports fully compositional verification (with the help of function specifications, contract invariants, loop invariants and call invariants). Our approach has been implemented in a tool named iContract which automatically proves the correctness of a smart contract statically or checks the unverified part …
Robust Maximum Capture Facility Location Under Random Utility Maximization Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Robust Maximum Capture Facility Location Under Random Utility Maximization Models, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
We study a robust version of the maximum capture facility location problem in a competitive market, assuming that each customer chooses among all available facilities according to a random utility maximization (RUM) model. We employ the generalized extreme value (GEV) family of models and assume that the parameters of the RUM model are not given exactly but lie in convex uncertainty sets. The problem is to locate new facilities to maximize the worst-case captured user demand. We show that, interestingly, our robust model preserves the monotonicity and submodularity from its deterministic counterpart, implying that a simple greedy heuristic can guarantee …
Joint Location And Cost Planning In Maximum Capture Facility Location Under Random Utilities, Ngan H. Duong, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Joint Location And Cost Planning In Maximum Capture Facility Location Under Random Utilities, Ngan H. Duong, Tien Thanh Dam, Thuy Anh Ta, Tien Mai
Research Collection School Of Computing and Information Systems
We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the costs (or budgets) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all available facilities according to a RUM model. We examine two RUM frameworks in the discrete choice literature, namely, the additive and multiplicative RUM. While the former has been widely used in facility location problems, we are the first to explore the latter in …
Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu
Research Collection School Of Computing and Information Systems
Cryptocurrency’s pseudo-anonymous nature makes it vulnerable to malicious activities. However, existing deep learning solutions lack interpretability and only support retrospective analysis of specific malice types. To address these challenges, we propose Intention-Monitor for early malice detection in Bitcoin. Our model, utilizing Decision-Tree based feature Selection and Complement (DT-SC), builds different feature sets for different malice types. The Status Proposal Module (SPM) and hierarchical self-attention predictor provide real-time global status and address label predictions. A survival module determines the stopping point and proposes the status sequence (intention). Our model detects various malicious activities with strong interpretability, outperforming state-of-the-art methods in extensive …
Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li
Instance-Specific Algorithm Configuration Via Unsupervised Deep Graph Clustering, Wen Song, Yi Liu, Zhiguang Cao, Yaoxin Wu, Qiqiang Li
Research Collection School Of Computing and Information Systems
Instance-specific Algorithm Configuration (AC) methods are effective in automatically generating high-quality algorithm parameters for heterogeneous NP-hard problems from multiple sources. However, existing works rely on manually designed features to describe training instances, which are simple numerical attributes and cannot fully capture structural differences. Targeting at Mixed-Integer Programming (MIP) solvers, this paper proposes a novel instances-specific AC method based on end-to-end deep graph clustering. By representing an MIP instance as a bipartite graph, a random walk algorithm is designed to extract raw features with both numerical and structural information from the instance graph. Then an auto-encoder is designed to learn dense …
Multi-Representation Variational Autoencoder Via Iterative Latent Attention And Implicit Differentiation, Nhu Thuat Tran, Hady Wirawan Lauw
Multi-Representation Variational Autoencoder Via Iterative Latent Attention And Implicit Differentiation, Nhu Thuat Tran, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Variational Autoencoder (VAE) offers a non-linear probabilistic modeling of user's preferences. While it has achieved remarkable performance at collaborative filtering, it typically samples a single vector for representing user's preferences, which may be insufficient to capture the user's diverse interests. Existing solutions extend VAE to model multiple interests of users by resorting a variant of self-attentive method, i.e., employing prototypes to group items into clusters, each capturing one topic of user's interests. Despite showing improvements, the current design could be more effective since prototypes are randomly initialized and shared across users, resulting in uninformative and non-personalized clusters.To fill the gap, …
Configuring Timing Parameters To Ensure Execution-Time Opacity In Timed Automata, Étienne André, Engel Lefaucheux, Didier Lime, Dylan Marinho, Jun Sun
Configuring Timing Parameters To Ensure Execution-Time Opacity In Timed Automata, Étienne André, Engel Lefaucheux, Didier Lime, Dylan Marinho, Jun Sun
Research Collection School Of Computing and Information Systems
Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as events with timestamps. Timed automata are an extension of finite-state automata with a set of clocks evolving linearly and that can be tested or reset, making this formalism able to reason on systems involving concurrency and timing constraints. In this paper, we summarize a recent line of works using timed automata as the input formalism, in which we assume that the attacker has access (only) to the system execution time. First, we address the following execution-time opacity problem: given a timed …
Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Deep Reinforcement Learning With Explicit Context Representation, Francisco Munguia-Galeano, Ah-Hwee Tan, Ze Ji
Research Collection School Of Computing and Information Systems
Though reinforcement learning (RL) has shown an outstanding capability for solving complex computational problems, most RL algorithms lack an explicit method that would allow learning from contextual information. On the other hand, humans often use context to identify patterns and relations among elements in the environment, along with how to avoid making wrong actions. However, what may seem like an obviously wrong decision from a human perspective could take hundreds of steps for an RL agent to learn to avoid. This article proposes a framework for discrete environments called Iota explicit context representation (IECR). The framework involves representing each state …
Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen
Grasp Solution Approach For The E-Waste Collection Problem, Aldy Gunawan, Dang Viet Anh Nguyen, Pham Kien Minh Nguyen, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …
K-St: A Formal Executable Semantics Of The Structured Text Language For Plcs, Kun Wang, Jingyi Wang, Christopher M. Poskitt, Xiangxiang Chen, Jun Sun, Peng Cheng
K-St: A Formal Executable Semantics Of The Structured Text Language For Plcs, Kun Wang, Jingyi Wang, Christopher M. Poskitt, Xiangxiang Chen, Jun Sun, Peng Cheng
Research Collection School Of Computing and Information Systems
Programmable Logic Controllers (PLCs) are responsible for automating process control in many industrial systems (e.g. in manufacturing and public infrastructure), and thus it is critical to ensure that they operate correctly and safely. The majority of PLCs are programmed in languages such as Structured Text (ST). However, a lack of formal semantics makes it difficult to ascertain the correctness of their translators and compilers, which vary from vendor-to-vendor. In this work, we develop K-ST, a formal executable semantics for ST in the K framework. Defined with respect to the IEC 61131-3 standard and PLC vendor manuals, K-ST is a high-level …
Carbon-Aware Mine Planning With A Novel Multi-Objective Framework, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi
Carbon-Aware Mine Planning With A Novel Multi-Objective Framework, Nurul Asyikeen Binte Azhar, Aldy Gunawan, Shih-Fen Cheng, Erwin Leonardi
Research Collection School Of Computing and Information Systems
The logistical complication of long-term mine planning involves deciding the sequential extraction of materials from the mine pit and their subsequent processing steps based on geological, geometrical, and resource constraints. The net present value (NPV) of profit over the mine's lifespan usually forms the sole objective for this problem, which is considered as the NP-hard precedence-constrained production scheduling problem (PCPSP) as well. However, increased pressure for more sustainable and carbon-aware industries also calls for environmental indicators to be considered. In this paper, we enhance the generic PCPSP formulation into a multi-objective optimization (MOO) problem whereby carbon cost forms an additional …
Threshold Attribute-Based Credentials With Redactable Signature, Rui Shi, Huamin Feng, Yang Yang, Feng Yuan, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng
Threshold Attribute-Based Credentials With Redactable Signature, Rui Shi, Huamin Feng, Yang Yang, Feng Yuan, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Threshold attribute-based credentials are suitable for decentralized systems such as blockchains as such systems generally assume that authenticity, confidentiality, and availability can still be guaranteed in the presence of a threshold number of dishonest or faulty nodes. Coconut (NDSS'19) was the first selective disclosure attribute-based credentials scheme supporting threshold issuance. However, it does not support threshold tracing of user identities and threshold revocation of user credentials, which is desired for internal governance such as identity management, data auditing, and accountability. The communication and computation complexities of Coconut for verifying credentials are linear in the number of each user's attributes and …
Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves
Uncertainty-Adjusted Inductive Matrix Completion With Graph Neural Networks, Petr Kasalicky, Antoine Ledent, Rodrigo Alves
Research Collection School Of Computing and Information Systems
We propose a robust recommender systems model which performs matrix completion and a ratings-wise uncertainty estimation jointly. Whilst the prediction module is purely based on an implicit low-rank assumption imposed via nuclear norm regularization, our loss function is augmented by an uncertainty estimation module which learns an anomaly score for each individual rating via a Graph Neural Network: data points deemed more anomalous by the GNN are downregulated in the loss function used to train the low-rank module. The whole model is trained in an end-to-end fashion, allowing the anomaly detection module to tap on the supervised information available in …
Quantifying Taxi Drivers' Behaviors With Behavioral Game Theory, Mengyu Ji, Yuhong Xu, Shih-Fen Cheng
Quantifying Taxi Drivers' Behaviors With Behavioral Game Theory, Mengyu Ji, Yuhong Xu, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
With their flexibility and convenience, taxis play a vital role in urban transportation systems. Understanding how human drivers make decisions in a context of uncertainty and competition is crucial for taxi fleets that depend on drivers to provide their services. As part of this paper, we propose modeling taxi drivers’ behaviors based on behavioral game theory. Based on real-world data, we demonstrate that the behavioral game theory model we select is superior to state-of-the-art baselines. These results provide a solid foundation for improving taxi fleet efficiency in the future.
Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau
Learning To Send Reinforcements: Coordinating Multi-Agent Dynamic Police Patrol Dispatching And Rescheduling Via Reinforcement Learning, Waldy Joe, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We address the problem of coordinating multiple agents in a dynamic police patrol scheduling via a Reinforcement Learning (RL) approach. Our approach utilizes Multi-Agent Value Function Approximation (MAVFA) with a rescheduling heuristic to learn dispatching and rescheduling policies jointly. Often, police operations are divided into multiple sectors for more effective and efficient operations. In a dynamic setting, incidents occur throughout the day across different sectors, disrupting initially-planned patrol schedules. To maximize policing effectiveness, police agents from different sectors cooperate by sending reinforcements to support one another in their incident response and even routine patrol. This poses an interesting research challenge …
Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. Nguyen, Pieter. Vansteenwegen
Grasp Based Metaheuristic To Solve The Mixed Fleet E-Waste Collection Route Planning Problem, Aldy Gunawan, Dang V.A. Nguyen, Pham K.M. Nguyen, Pieter. Vansteenwegen
Research Collection School Of Computing and Information Systems
The digital economy has brought significant advancements in electronic devices, increasing convenience and comfort in people’s lives. However, this progress has also led to a shorter life cycle for these devices due to rapid advancements in hardware and software technology. As a result, e-waste collection and recycling have become vital for protecting the environment and people’s health. From the operations research perspective, the e-waste collection problem can be modeled as the Heterogeneous Vehicle Routing Problem with Multiple Time Windows (HVRP-MTW). This study proposes a metaheuristic based on the Greedy Randomized Adaptive Search Procedure complemented by Path Relinking (GRASP-PR) to solve …
Document-Level Relation Extraction Via Separate Relation Representation And Logical Reasoning, Heyan Huang, Changsen Yuan, Qian Liu, Yixin Cao
Document-Level Relation Extraction Via Separate Relation Representation And Logical Reasoning, Heyan Huang, Changsen Yuan, Qian Liu, Yixin Cao
Research Collection School Of Computing and Information Systems
Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model, SRLR, using Separate Relation Representation and Logical Reasoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct …
Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
Estimation Of Recursive Route Choice Models With Incomplete Trip Observations, Tien Mai, The Viet Bui, Quoc Phong Nguyen, Tho V. Le
Research Collection School Of Computing and Information Systems
This work concerns the estimation of recursive route choice models in the situation that the trip observations are incomplete, i.e., there are unconnected links (or nodes) in the observations. A direct approach to handle this issue could be intractable because enumerating all paths between unconnected links (or nodes) in a real network is typically not possible. We exploit an expectation–maximization (EM) method that allows dealing with the missing-data issue by alternatively performing two steps of sampling the missing segments in the observations and solving maximum likelihood estimation problems. Moreover, observing that the EM method could be expensive, we propose a …
Glocal Energy-Based Learning For Few-Shot Open-Set Recognition, Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang
Glocal Energy-Based Learning For Few-Shot Open-Set Recognition, Haoyu Wang, Guansong Pang, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang
Research Collection School Of Computing and Information Systems
Few-shot open-set recognition (FSOR) is a challenging task of great practical value. It aims to categorize a sample to one of the pre-defined, closed-set classes illustrated by few examples while being able to reject the sample from unknown classes. In this work, we approach the FSOR task by proposing a novel energy-based hybrid model. The model is composed of two branches, where a classification branch learns a metric to classify a sample to one of closedset classes and the energy branch explicitly estimates the open-set probability. To achieve holistic detection of openset samples, our model leverages both class-wise and pixelwise …
Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo
Techsumbot: A Stack Overflow Answer Summarization Tool For Technical Query, Chengran Yang, Bowen Xu, Jiakun Liu, David Lo
Research Collection School Of Computing and Information Systems
Stack Overflow is a popular platform for developers to seek solutions to programming-related problems. However, prior studies identified that developers may suffer from the redundant, useless, and incomplete information retrieved by the Stack Overflow search engine. To help developers better utilize the Stack Overflow knowledge, researchers proposed tools to summarize answers to a Stack Overflow question. However, existing tools use hand-craft features to assess the usefulness of each answer sentence and fail to remove semantically redundant information in the result. Besides, existing tools only focus on a certain programming language and cannot retrieve up-to-date new posted knowledge from Stack Overflow. …
Deep Isolation Forest For Anomaly Detection, Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang
Deep Isolation Forest For Anomaly Detection, Hongzuo Xu, Guansong Pang, Yijie Wang, Yongjun Wang
Research Collection School Of Computing and Information Systems
Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation …
Morphologically-Aware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw
Morphologically-Aware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
We propose SubText, a compression mechanism via vocabulary reduction. The crux is to judiciously select a subset of word embeddings which support the reconstruction of the remaining word embeddings based on their form alone. The proposed algorithm considers the preservation of the original embeddings, as well as a word’s relationship to other words that are morphologically or semantically similar. Comprehensive evaluation of the compressed vocabulary reveals SubText’s efficacy on diverse tasks over traditional vocabulary reduction techniques, as validated on English, as well as a collection of inflected languages.
Nftdisk: Visual Detection Of Wash Trading In Nft Markets, Xiaolin Wen, Yong Wang, Xuanwu Yue, Feida Zhu, Min Zhu
Nftdisk: Visual Detection Of Wash Trading In Nft Markets, Xiaolin Wen, Yong Wang, Xuanwu Yue, Feida Zhu, Min Zhu
Research Collection School Of Computing and Information Systems
With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with …
Mirror: Mining Implicit Relationships Via Structure-Enhanced Graph Convolutional Networks, Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, Lianhua Chi
Mirror: Mining Implicit Relationships Via Structure-Enhanced Graph Convolutional Networks, Jiaying Liu, Feng Xia, Jing Ren, Bo Xu, Guansong Pang, Lianhua Chi
Research Collection School Of Computing and Information Systems
Data explosion in the information society drives people to develop more effective ways to extract meaningful information. Extracting semantic information and relational information has emerged as a key mining primitive in a wide variety of practical applications. Existing research on relation mining has primarily focused on explicit connections and ignored underlying information, e.g., the latent entity relations. Exploring such information (defined as implicit relationships in this article) provides an opportunity to reveal connotative knowledge and potential rules. In this article, we propose a novel research topic, i.e., how to identify implicit relationships across heterogeneous networks. Specially, we first give a …
Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He
Layout Generation As Intermediate Action Sequence Prediction, Huiting Yang, Danqing Huang, Chin-Yew Lin, Shengfeng He
Research Collection School Of Computing and Information Systems
Layout generation plays a crucial role in graphic design intelligence. One important characteristic of the graphic layouts is that they usually follow certain design principles. For example, the principle of repetition emphasizes the reuse of similar visual elements throughout the design. To generate a layout, previous works mainly attempt at predicting the absolute value of bounding box for each element, where such target representation has hidden the information of higher-order design operations like repetition (e.g. copy the size of the previously generated element). In this paper, we introduce a novel action schema to encode these operations for better modeling the …
Generalizing Math Word Problem Solvers Via Solution Diversification, Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, Xiangliang Zhang
Generalizing Math Word Problem Solvers Via Solution Diversification, Zhenwen Liang, Jipeng Zhang, Lei Wang, Yan Wang, Jie Shao, Xiangliang Zhang
Research Collection School Of Computing and Information Systems
Current math word problem (MWP) solvers are usually Seq2Seq models trained by the (one-problem; one-solution) pairs, each of which is made of a problem description and a solution showing reasoning flow to get the correct answer. However, one MWP problem naturally has multiple solution equations. The training of an MWP solver with (one-problem; one-solution) pairs excludes other correct solutions, and thus limits the generalizability of the MWP solver. One feasible solution to this limitation is to augment multiple solutions to a given problem. However, it is difficult to collect diverse and accurate augment solutions through human efforts. In this paper, …