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Articles 2671 - 2700 of 7471
Full-Text Articles in Physical Sciences and Mathematics
Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Sentence-Level Evidence Embedding For Claim Verification With Hierarchical Attention Networks, Jing Ma, Wei Gao, Shafiq Joty, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications. It is cumbersome and inefficient for human fact-checkers to find consistent pieces of evidence, from which solid verdict could be inferred against the claim. In this paper, we propose a novel end-to-end hierarchical attention network focusing on learning to represent coherent evidence as well as their semantic relatedness with the claim. Our model consists of three main components: 1) A coherence-based attention layer embeds coherent evidence considering the claim and sentences from relevant articles; 2) An entailment-based attention layer …
The Spirulina Strategy, Singapore Management University
The Spirulina Strategy, Singapore Management University
Perspectives@SMU
This is an adapted version of the SMU Case, “Navigating an Eternal Ocean: EnerGaia’s Emergent Strategy in the Market for Spirulina" written by Research Fellow Ryan Merrill and Assistant Professor Simon Schillebeeckx. To see the full case, please click on the following link: https://cmp.smu.edu.sg/case/3986
EnerGaia began life trying to address nutrition issues in the developing world. The prospect of profit is testing its founder to balance both or choose one over the other
Commentary: How Effectively Can Singapore Adapt To Sea Level Rise?, Winston T. L. Chow
Commentary: How Effectively Can Singapore Adapt To Sea Level Rise?, Winston T. L. Chow
Research Collection School of Social Sciences
Even as Singapore strives to adapt to rising sea levels, let’s not win that battle yet end up losing the larger war against climate change, says the Singapore Management University’s Winston Chow.
Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic
Bidding Mechanisms In Graph Games, Guy Avni, Thomas A. Henzinger, Dorde Zikelic
Research Collection School Of Computing and Information Systems
In two-player games on graphs, the players move a token through a graph to produce a finite or infinite path, which determines the qualitative winner or quantitative payoff of the game. We study bidding games in which the players bid for the right to move the token. Several bidding rules were studied previously. In Richman bidding, in each round, the players simultaneously submit bids, and the higher bidder moves the token and pays the other player. Poorman bidding is similar except that the winner of the bidding pays the “bank” rather than the other player. Taxman bidding spans the spectrum …
Creating Top Ranking Options In The Continuous Option And Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung Yiu, Zhenyu Chen
Creating Top Ranking Options In The Continuous Option And Preference Space, Bo Tang, Kyriakos Mouratidis, Man Lung Yiu, Zhenyu Chen
Research Collection School Of Computing and Information Systems
Top-k queries are extensively used to retrieve the k most relevantoptions (e.g., products, services, accommodation alternatives, etc)based on a weighted scoring function that captures user preferences. In this paper, we take the viewpoint of a business owner whoplans to introduce a new option to the market, with a certain type ofclientele in mind. Given a target region in the consumer spectrum,we determine what attribute values the new option should have,so that it ranks among the top-k for any user in that region. Ourmethodology can also be used to improve an existing option, at theminimum modification cost, so that it ranks …
Inspect: Iterated Local Search For Solving Path Conditions, Fuxiang Chen, Aldy Gunawan, David Lo, Sunghun Kim
Inspect: Iterated Local Search For Solving Path Conditions, Fuxiang Chen, Aldy Gunawan, David Lo, Sunghun Kim
Research Collection School Of Computing and Information Systems
Automated test case generation is attractive as it can reduce developer workload. To generate test cases, many Symbolic Execution approaches first produce Path Conditions (PCs), a set of constraints, and pass them to a Satisfiability Modulo Theories (SMT) solver. Despite numerous prior studies, automated test case generation by Symbolic Execution is still slow, partly due to SMT solvers’ high computationally complexity. We introduce InSPeCT, a Path Condition solver, that leverages elements of ILS (Iterated Local Search) and Tabu List. ILS is not computational intensive and focuses on generating solutions in search spaces while Tabu List prevents the use of previously …
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
A Survey On Bluetooth 5.0 And Mesh: New Milestones Of Iot, Juenjie Yin, Zheng Yang, Hao Cao, Tongtong Liu, Zimu Zhou, Chenshu Wu
Research Collection School Of Computing and Information Systems
No abstract provided.
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Cold-Start Aware Deep Memory Networks For Multi-Entity Aspect-Based Sentiment Analysis, Kaisong Song, Wei Gao, Lujun Zhao, Changlong Sun, Xiaozhong Liu
Research Collection School Of Computing and Information Systems
Various types of target information have been considered in aspect-based sentiment analysis, such as entities and aspects. Existing research has realized the importance of targets and developed methods with the goal of precisely modeling their contexts via generating target-specific representations. However, all these methods ignore that these representations cannot be learned well due to the lack of sufficient human-annotated target-related reviews, which leads to the data sparsity challenge, a.k.a. cold-start problem here. In this paper, we focus on a more general multiple entity aspect-based sentiment analysis (ME-ABSA) task which aims at identifying the sentiment polarity of different aspects of multiple …
Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu
Improving Urban Crowd Flow Prediction On Flexible Region Partition, Xu Wang, Zimu Zhou, Yi Zhao, Xinglin Zhang, Kai Xing, Fu Xiao, Zheng Yang, Yunhao Liu
Research Collection School Of Computing and Information Systems
Accurate forecast of citywide crowd flows on flexible region partition benefits urban planning, traffic management, and public safety. Previous research either fails to capture the complex spatiotemporal dependencies of crowd flows or is restricted on grid region partition that loses semantic context. In this paper, we propose DeepFlowFlex, a graph-based model to jointly predict inflows and outflows for each region of arbitrary shape and size in a city. Analysis on cellular datasets covering 2.4 million users in China reveals dependencies and distinctive patterns of crowd flows in not only the conventional space and time domains, but also the speed domain, …
Kgat: Knowledge Graph Attention Network For Recommendation, Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua
Kgat: Knowledge Graph Attention Network For Recommendation, Xiang Wang, Xiangnan He, Yixin Cao, Meng Liu, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account. Traditional methods like factorization machine (FM) cast it as a supervised learning problem, which assumes each interaction as an independent instance with side information encoded. Due to the overlook of the relations among instances or items (e.g., the director of a movie is also an actor of another movie), these methods are insufficient to distill the collaborative signal from the collective behaviors of users. In this work, we investigate the utility of knowledge graph (KG), which breaks …
Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa
Let Me In: Guidelines For The Successful Onboarding Of Newcomers To Open Source Projects, Igor Steinmacher, Christoph Treude, Marco Aurélio Gerosa
Research Collection School Of Computing and Information Systems
Many community-based open source software (OSS) projects depend on a continuous influx of newcomers for their survival and continuity, yet newcomers face many barriers to contributing to a project. We provide guidelines based on our previous work for both OSS communities and newcomers to OSS projects.
Multiagent Decision Making And Learning In Urban Environments, Akshat Kumar
Multiagent Decision Making And Learning In Urban Environments, Akshat Kumar
Research Collection School Of Computing and Information Systems
Our increasingly interconnected urban environments provide several opportunities to deploy intelligent agents—from self-driving cars, ships to aerial drones—that promise to radically improve productivity and safety. Achieving coordination among agents in such urban settings presents several algorithmic challenges—ability to scale to thousands of agents, addressing uncertainty, and partial observability in the environment. In addition, accurate domain models need to be learned from data that is often noisy and available only at an aggregate level. In this paper, I will overview some of our recent contributions towards developing planning and reinforcement learning strategies to address several such challenges present in largescale urban …
A Secure Iot Cloud Storage System With Fine-Grained Access Control And Decryption Key Exposure Resistance, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu
A Secure Iot Cloud Storage System With Fine-Grained Access Control And Decryption Key Exposure Resistance, Shengmin Xu, Guomin Yang, Yi Mu, Ximeng Liu
Research Collection School Of Computing and Information Systems
Internet of Things (IoT) cloud provides a practical and scalable solution to accommodate the data management in large-scale IoT systems by migrating the data storage and management tasks to cloud service providers (CSPs). However, there also exist many data security and privacy issues that must be well addressed in order to allow the wide adoption of the approach. To protect data confidentiality, attribute-based cryptosystems have been proposed to provide fine-grained access control over encrypted data in loT cloud. Unfortunately, the existing attributed-based solutions are still insufficient in addressing some challenging security problems, especially when dealing with compromised or leaked user …
Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang
Who Should Make Decision On This Pull Request? Analyzing Time-Decaying Relationships And File Similarities For Integrator Prediction, Jing Jiang, David Lo, Jiateng Zheng, Xin Xia, Yun Yang, Li Zhang
Research Collection School Of Computing and Information Systems
In pull-based development model, integrators are responsible for making decisions about whether to accept pull requests andintegrate code contributions. Ideally, pull requests are assigned to integrators and evaluated within a short time after their submissions. However, the volume of incoming pull requests is large in popular projects, and integrators often encounter difficulties inprocessing pull requests in a timely fashion. Therefore, an automatic integrator prediction approach is required to assign appropriate pull requests to integrators. In this paper, we propose an approach TRFPre which analyzes Time-decaying Relationships andFile similarities to predict integrators. We evaluate the effectiveness of TRFPre on 24 projects …
Knowledge Base Question Answering With Topic Units, Yunshi Lan, Shuohang Wang, Jing Jiang
Knowledge Base Question Answering With Topic Units, Yunshi Lan, Shuohang Wang, Jing Jiang
Research Collection School Of Computing and Information Systems
Knowledge base question answering (KBQA) is an important task in natural language processing. Existing methods for KBQA usually start with entity linking, which considers mostly named entities found in a question as the starting points in the KB to search for answers to the question. However, relying only on entity linking to look for answer candidates may not be sufficient. In this paper, we propose to perform topic unit linking where topic units cover a wider range of units of a KB. We use a generation-and-scoring approach to gradually refine the set of topic units. Furthermore, we use reinforcement learning …
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Biker: A Tool For Bi-Information Source Based Api Method Recommendation, Liang Cai, Haoye Wang, Qiao Huang, Xin Xia, Zhenchang Xing, David Lo
Research Collection School Of Computing and Information Systems
No abstract provided.
Answerbot: An Answer Summary Generation Tool Based On Stack Overflow, Liang Cai, Haoye Wang, Bowen Xu, Qiao Huang, Xin Xia, David Lo, Zhenchang Xing
Answerbot: An Answer Summary Generation Tool Based On Stack Overflow, Liang Cai, Haoye Wang, Bowen Xu, Qiao Huang, Xin Xia, David Lo, Zhenchang Xing
Research Collection School Of Computing and Information Systems
The prevalence of questions and answers on domainspecific Q&A sites like Stack Overflow constitutes a core knowledge asset for software engineering domain. Although search engines can return a list of questions relevant to a user query of some technical question, the abundance of relevant posts and the sheer amount of information in them makes it difficult for developers to digest them and find the most needed answers to their questions. In this work, we aim to help developers who want to quickly capture the key points of several answer posts relevant to a technical question before they read the details …
Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model, Bhavtosh Rath, Wei Gao, Jaideep Srivastava
Evaluating Vulnerability To Fake News In Social Networks: A Community Health Assessment Model, Bhavtosh Rath, Wei Gao, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
Understanding the spread of false information in social networks has gained a lot of recent attention. In this paper, we explore the role community structures play in determining how people get exposed to fake news. Inspired by approaches in epidemiology, we propose a novel Community Health Assessment model, whose goal is to understand the vulnerability of communities to fake news spread. We define the concepts of neighbor, boundary and core nodes of a community and propose appropriate metrics to quantify the vulnerability of nodes (individual-level) and communities (group-level) to spreading fake news. We evaluate our model on communities identified using …
Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau
Decision Making For Improving Maritime Traffic Safety Using Constraint Programming, Saumya Bhatnagar, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of …
Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam
Data-Driven Surgical Duration Prediction Model For Surgery Scheduling: A Case-Study For A Practice-Feasible Model In A Public Hospital, Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam
Research Collection School Of Computing and Information Systems
Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and …
Language And Robotics: Complex Sentence Understanding, Seng-Beng Ho, Zhaoxia Wang
Language And Robotics: Complex Sentence Understanding, Seng-Beng Ho, Zhaoxia Wang
Research Collection School Of Computing and Information Systems
Existing robotic systems can take actions based on natural language commands but they tend to be only simple commands. On the other hand, in the domain of Natural Language Processing (NLP), complex sentences are processed, but this NLP domain does not make close contact with robotics. The beginning of computer processing of natural language, when traced back to a system such as Winograd’s SHRUDLU, conceived in 1973, actually aimed to address the issues of Natural Language Understanding (NLU) of relatively complex sentences by a robotic system which in turn takes actions accordingly based on the natural language input. NLU, in …
Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue
Constructing Strong Designated Verifier Signatures From Key Encapsulation Mechanisms, Borui Gong, Ho Man Au, Haiyang Xue
Research Collection School Of Computing and Information Systems
A designated verifier signature (DVS) allows a signer to convince a verifier that a message has been endorsed in a way that the conviction cannot be transferred to any third party. This is achieved by the property that the signature can be generated by one of them. Since DVS is publicly verifiable, a valid DVS implies that the signature must be created by either the signer or the verifier. To enhance privacy of signers' identity, a strong DVS (SDVS) disallows public verification. In this paper, we investigate various aspects of SDVS with making two contributions. Firstly, we consider SDVS in …
Generalized Majorization-Minimization For Non-Convex Optimization, Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng
Generalized Majorization-Minimization For Non-Convex Optimization, Hu Zhang, Pan Zhou, Yi Yang, Jiashi Feng
Research Collection School Of Computing and Information Systems
Majorization-Minimization (MM) algorithms optimize an objective function by iteratively minimizing its majorizing surrogate and offer attractively fast convergence rate for convex problems. However, their convergence behaviors for non-convex problems remain unclear. In this paper, we propose a novel MM surrogate function from strictly upper bounding the objective to bounding the objective in expectation. With this generalized surrogate conception, we develop a new optimization algorithm, termed SPI-MM, that leverages the recent proposed SPIDER for more efficient non-convex optimization. We prove that for finite-sum problems, the SPI-MM algorithm converges to an stationary point within deterministic and lower stochastic gradient complexity. To our …
How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau
How Can Ai Help To Enhance Diversity And Inclusion?, Keng Siau
Research Collection School Of Computing and Information Systems
In many organizations, promoting diversity and enhancing inclusion are still major concerns. Unconscious biases and stereotyping cause us to have preconceived ideas about what an ideal employee or leader should look like. Unconscious biases are also a major roadblock to an inclusive environment and business culture. Organizations have been investing heavily in training programs for their employees attempting to changes these patterns. Human habits, especially unconscious ones, are not easy to overcome. This research looks at the use of AI to enhance diversity and inclusion in organizations. Literature has shown that a more diverse and inclusive workforce has a competitive …
Higher Education In The Ai Age, Yizhi Ma, Keng Siau
Higher Education In The Ai Age, Yizhi Ma, Keng Siau
Research Collection School Of Computing and Information Systems
A perfect storm is hitting higher education. Decrease funding from traditional funding sources such as State Governments and transformative changes caused by artificial intelligence (AI) will revolutionize higher education (Siau 2018). Higher education needs to change and evolve quickly and continuously to prepare students for the upheavals in the job market caused by AI, machine learning, and automation. Further, continuous organizational and curriculum changes will be necessary for a higher education institution to stay relevant and to stay afloat. This qualitative research looks at higher education in the AI age. Stakeholders (i.e., administrators, faculty, students, industry recruiters) in higher education …
Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou
Industry 4.0: Challenges And Opportunities In Different Countries, Keng Siau, Yingrui Xi, Cui Zou
Research Collection School Of Computing and Information Systems
Along with the rapid development of artificial intelligence (AI), cyber-physical systems (CPSs), big data analytics, and cloud computing, Industry 4.0 — a subset of the fourth Industrial Revolution — has started to emerge and take root in many countries. Many expect that Industry 4.0 will be transformative and revolutionary for multiple industries and countries. Its impact will be much more significant than those of Industry 1.0, 2.0, and 3.0. Most studies and papers on Industry 4.0 have examined its impact on various industries, jobs, and organizations. In this article, we investigate the impact of Industry 4.0 on countries and groups …
Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu
Cerebro: Context-Aware Adaptive Fuzzing For Effective Vulnerability Detection, Yuekang Li, Yinxing Xue, Hongxu Chen, Xiuheng Wu, Cen Zhang, Xiaofei Xie, Haijun Wang, Yang Liu
Research Collection School Of Computing and Information Systems
Existing greybox fuzzers mainly utilize program coverage as the goal to guide the fuzzing process. To maximize their outputs, coverage-based greybox fuzzers need to evaluate the quality of seeds properly, which involves making two decisions: 1) which is the most promising seed to fuzz next (seed prioritization), and 2) how many efforts should be made to the current seed (power scheduling). In this paper, we present our fuzzer, Cerebro, to address the above challenges. For the seed prioritization problem, we propose an online multi-objective based algorithm to balance various metrics such as code complexity, coverage, execution time, etc. To address …
Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li
Diffchaser: Detecting Disagreements For Deep Neural Networks, Xiaofei Xie, Lei Ma, Haijun Wang, Yuekang Li, Yang Liu, Xiaohong Li
Research Collection School Of Computing and Information Systems
The platform migration and customization have become an indispensable process of deep neural network (DNN) development lifecycle. A highprecision but complex DNN trained in the cloud on massive data and powerful GPUs often goes through an optimization phase (e.g., quantization, compression) before deployment to a target device (e.g., mobile device). A test set that effectively uncovers the disagreements of a DNN and its optimized variant provides certain feedback to debug and further enhance the optimization procedure. However, the minor inconsistency between a DNN and its optimized version is often hard to detect and easily bypasses the original test set. This …
Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Deepstellar: Model-Based Quantitative Analysis Of Stateful Deep Learning Systems, Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has achieved tremendous success in many cutting-edge applications. However, the state-of-the-art DL systems still suffer from quality issues. While some recent progress has been made on the analysis of feed-forward DL systems, little study has been done on the Recurrent Neural Network (RNN)-based stateful DL systems, which are widely used in audio, natural languages and video processing, etc. In this paper, we initiate the very first step towards the quantitative analysis of RNN-based DL systems. We model RNN as an abstract state transition system to characterize its internal behaviors. Based on the abstract model, we design two …
Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang
Correlation-Sensitive Next-Basket Recommendation, Duc Trong Le, Hady Wirawan Lauw, Yuan Fang
Research Collection School Of Computing and Information Systems
Items adopted by a user over time are indicative ofthe underlying preferences. We are concerned withlearning such preferences from observed sequencesof adoptions for recommendation. As multipleitems are commonly adopted concurrently, e.g., abasket of grocery items or a sitting of media consumption, we deal with a sequence of baskets asinput, and seek to recommend the next basket. Intuitively, a basket tends to contain groups of relateditems that support particular needs. Instead of recommending items independently for the next basket, we hypothesize that incorporating informationon pairwise correlations among items would help toarrive at more coherent basket recommendations.Towards this objective, we develop a …