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Digitalization In Practice: The Fifth Discipline Advantage, Siu Loon Hoe Dec 2019

Digitalization In Practice: The Fifth Discipline Advantage, Siu Loon Hoe

Research Collection School Of Computing and Information Systems

Purpose The purpose of this paper is to provide advice to organizations on how to become successful in the digital age. The paper revisits Peter Senge's (1990) notion of the learning organization and discusses the relevance of systems thinking and the other four disciplines, namely, personal mastery, mental models, shared vision and team learning in the context of the current digitalization megatrend. Design/methodology/approach This paper is based on content analysis of essays from international organizations, strategy experts and management scholars, and insights gained from the author's consulting experience. A comparative case study from the health and social sector is also …

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Implementing Security-Enhanced Phr System In The Cloud Using Fame, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu Dec 2019

Implementing Security-Enhanced Phr System In The Cloud Using Fame, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu

Research Collection School Of Computing and Information Systems

Personal Health Record (PHR) plays an important role in realizing the exchange and interaction of medical information as well as in managing chronic diseases. In the recent decade, plenty of researches have been carried out to resolve the secure sharing issue of PHR in the cloud via access control schemes. Attribute-based Encryption (ABE) is a promising technique, which implements access control by introducing the access policy. When ABE is used to construct access control scheme in the cloud, hiding and updating access policy have always been two research hotspots. However, all of the existing solutions only consider one of these …

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Strongly Secure Authenticated Key Exchange From Supersingular Isogenies, Xiu Xu, Haiyang Xue, Kunpeng Wang, Ho Man Au, Song Tian Dec 2019

Strongly Secure Authenticated Key Exchange From Supersingular Isogenies, Xiu Xu, Haiyang Xue, Kunpeng Wang, Ho Man Au, Song Tian

Research Collection School Of Computing and Information Systems

This paper aims to address the open problem, namely, to find new techniques to design and prove security of supersingular isogeny-based authenticated key exchange (AKE) protocols against the widest possible adversarial attacks, raised by Galbraith in 2018. Concretely, we present two AKEs based on a double-key PKE in the supersingular isogeny setting secure in the sense of CK+, one of the strongest security models for AKE. Our contributions are summarised as follows. Firstly, we propose a strong OW-CPA secure PKE, 2PKEsidh, based on SI-DDH assumption. By applying modified Fujisaki-Okamoto transformation, we obtain a [OW-CCA, OW-CPA] secure KEM, 2KEMsidh. Secondly, we …

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Multi-Hop Knowledge Base Question Answering With An Iterative Sequence Matching Model, Yunshi Lan, Shuohang Wang, Jing Jiang Nov 2019

Multi-Hop Knowledge Base Question Answering With An Iterative Sequence Matching Model, Yunshi Lan, Shuohang Wang, Jing Jiang

Research Collection School Of Computing and Information Systems

Knowledge Base Question Answering (KBQA) has attracted much attention and recently there has been more interest in multi-hop KBQA. In this paper, we propose a novel iterative sequence matching model to address several limitations of previous methods for multi-hop KBQA. Our method iteratively grows the candidate relation paths that may lead to answer entities. The method prunes away less relevant branches and incrementally assigns matching scores to the paths. Empirical results demonstrate that our method can significantly outperform existing methods on three different benchmark datasets.

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Scompile: Critical Path Identification And Analysis For Smart Contracts, Jialiang Chang, Bo Gao, Hao Xiao, Jun Sun, Yan Cai, Zijiang Yang Nov 2019

Scompile: Critical Path Identification And Analysis For Smart Contracts, Jialiang Chang, Bo Gao, Hao Xiao, Jun Sun, Yan Cai, Zijiang Yang

Research Collection School Of Computing and Information Systems

Ethereum smart contracts are an innovation built on top of the blockchain technology, which provides a platform for automatically executing contracts in an anonymous, distributed, and trusted way. The problem is magnified by the fact that smart contracts, unlike ordinary programs, cannot be patched easily once deployed. It is important for smart contracts to be checked against potential vulnerabilities. In this work, we propose an alternative approach to automatically identify critical program paths (with multiple function calls including inter-contract function calls) in a smart contract, rank the paths according to their criticalness, discard them if they are infeasible or otherwise …

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Recommendation-Based Team Formation For On-Demand Taxi-Calling Platforms, Lingyu Zhang, Tianshu Song, Yongxin Tong, Zimu Zhou, Dan Li, Wei Ai, Lulu Zhang, Guobin Wu, Yan Liu, Jieping Ye Nov 2019

Recommendation-Based Team Formation For On-Demand Taxi-Calling Platforms, Lingyu Zhang, Tianshu Song, Yongxin Tong, Zimu Zhou, Dan Li, Wei Ai, Lulu Zhang, Guobin Wu, Yan Liu, Jieping Ye

Research Collection School Of Computing and Information Systems

On-demand taxi-calling platforms often ignore the social engagement of individual drivers. The lack of social incentives impairs the work enthusiasms of drivers and will affect the quality of service. In this paper, we propose to form teams among drivers to promote participation. A team consists of a leader and multiple members, which acts as the basis for various group-based incentives such as competition. We define the Recommendation-based Team Formation (RTF) problem to form as many teams as possible while accounting for the choices of drivers. The RTF problem is challenging. It needs both accurate recommendation and coordination among recommendations, since …

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Using Customer Service Dialogues For Satisfaction Analysis With Context-Assisted Multiple Instance Learning, Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang Nov 2019

Using Customer Service Dialogues For Satisfaction Analysis With Context-Assisted Multiple Instance Learning, Kaisong Song, Lidong Bing, Wei Gao, Jun Lin, Lujun Zhao, Jiancheng Wang, Changlong Sun, Xiaozhong Liu, Qiong Zhang

Research Collection School Of Computing and Information Systems

Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms. Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative). In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues. We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service …

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Eurecom At Trecvid Avs 2019, Danny Francis, Phuong Anh Nguyen, Benoit Huet, Chong-Wah Ngo Nov 2019

Eurecom At Trecvid Avs 2019, Danny Francis, Phuong Anh Nguyen, Benoit Huet, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

This notebook reports the model and results of the EURECOM runs at TRECVID AVS 2019.

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Special Issue On Multimedia Recommendation And Multi-Modal Data Analysis, Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang Nov 2019

Special Issue On Multimedia Recommendation And Multi-Modal Data Analysis, Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang

Research Collection School Of Computing and Information Systems

Rich multimedia contents are dominating the Web. In popular social media platforms such as FaceBook, Twitter, and Instagram, there are over millions of multimedia contents being created by users on a daily basis. In the meantime, multimedia data consist of data in multiple modalities, such as text, images, audio, and so on. Users are heavily overloaded by the massive multi-modal data, and it becomes critical to explore advanced techniques for heterogeneous big data analytics and multimedia recommendation. Traditional multimedia recommendation and data analysis technologies cannot well address the problem of understanding users’ preference in the feature-rich multimedia contents, and have …

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Medtruth: A Semi-Supervised Approach To Discovering Knowledge Condition Information From Multi-Source Medical Data, Yang Deng, Yaliang Li, Ying Shen, Nan Du, Wei Fan, Min Yang, Kai Lei Nov 2019

Medtruth: A Semi-Supervised Approach To Discovering Knowledge Condition Information From Multi-Source Medical Data, Yang Deng, Yaliang Li, Ying Shen, Nan Du, Wei Fan, Min Yang, Kai Lei

Research Collection School Of Computing and Information Systems

Knowledge Graph (KG) contains entities and the relations between entities. Due to its representation ability, KG has been successfully applied to support many medical/healthcare tasks. However, in the medical domain, knowledge holds under certain conditions. Such conditions for medical knowledge are crucial for decisionmaking in various medical applications, which is missing in existing medical KGs. In this paper, we aim to discovery medical knowledge conditions from texts to enrich KGs. Electronic Medical Records (EMRs) are systematized collection of clinical data and contain detailed information about patients, thus EMRs can be a good resource to discover medical knowledge conditions. Unfortunately, the …

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Coverage-Guided Fuzzing For Feedforward Neural Networks, Xiaofei Xie, Hongxu Chen, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao Nov 2019

Coverage-Guided Fuzzing For Feedforward Neural Networks, Xiaofei Xie, Hongxu Chen, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep neural network (DNN) has been widely applied to safety-critical scenarios such as autonomous vehicle, security surveillance, and cyber-physical control systems. Yet, the incorrect behaviors of DNNs can lead to severe accidents and tremendous losses due to hidden defects. In this paper, we present DeepHunter, a general-purpose fuzzing framework for detecting defects of DNNs. DeepHunter is inspired by traditional grey-box fuzzing and aims to increase the overall test coverage by applying adaptive heuristics according to runtime feedback. Specifically, DeepHunter provides a series of seed selection strategies, metamorphic mutation strategies, and testing criteria customized to DNN testing; all these components support …

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Deepmutation++: A Mutation Testing Framework For Deep Learning Systems, Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, Jianjun Zhao Nov 2019

Deepmutation++: A Mutation Testing Framework For Deep Learning Systems, Qiang Hu, Lei Ma, Xiaofei Xie, Bing Yu, Yang Liu, Jianjun Zhao

Research Collection School Of Computing and Information Systems

Deep neural networks (DNNs) are increasingly expanding their real-world applications across domains, e.g., image processing, speech recognition and natural language processing. However, there is still limited tool support for DNN testing in terms of test data quality and model robustness. In this paper, we introduce a mutation testing-based tool for DNNs, DeepMutation++, which facilitates the DNN quality evaluation, supporting both feed-forward neural networks (FNNs) and stateful recurrent neural networks (RNNs). It not only enables static analysis of the robustness of a DNN model against the input as a whole, but also allows the identification of the vulnerable segments of a …

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Characterizing And Predicting Repeat Food Consumption Behavior For Just-In-Time Interventions, Yue Liu, Helena Huey Chong Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin Nov 2019

Characterizing And Predicting Repeat Food Consumption Behavior For Just-In-Time Interventions, Yue Liu, Helena Huey Chong Lee, Palakorn Achananuparp, Ee-Peng Lim, Tzu-Ling Cheng, Shou-De Lin

Research Collection School Of Computing and Information Systems

Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community …

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Data Security Issues In Deep Learning: Attacks, Countermeasures, And Opportunities, Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert H. Deng Nov 2019

Data Security Issues In Deep Learning: Attacks, Countermeasures, And Opportunities, Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Benefiting from the advancement of algorithms in massive data and powerful computing resources, deep learning has been explored in a wide variety of fields and produced unparalleled performance results. It plays a vital role in daily applications and is also subtly changing the rules, habits, and behaviors of society. However, inevitably, data-based learning strategies are bound to cause potential security and privacy threats, and arouse public as well as government concerns about its promotion to the real world. In this article, we mainly focus on data security issues in deep learning. We first investigate the potential threats of deep learning …

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Secure Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta Nov 2019

Secure Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta

Research Collection School Of Computing and Information Systems

Due to an increasing number of avenues for conducting cross-VM side-channel attacks, the security of multi-tenant public IaaS cloud environments is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. In this paper, we focus on secure VM placement algorithms which a cloud provider can use for the automatic enforcement of security against such co-location based attacks. To do so, we first establish a metric for evaluating and quantifying co-location security of multi-tenant public IaaS clouds, and then propose a novel VM placement …

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Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin Nov 2019

Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin

Research Collection School Of Computing and Information Systems

The authors explore how topic modeling can be used to automate the categorization of initial coin offerings (ICOs) into different topics (e.g., finance, media, information, professional services, health and social, natural resources) based solely on the content within the whitepapers. This tool has been developed by fitting a latent Dirichlet allocation (LDA) model to the text extracted from the ICO whitepapers. After evaluating the automated categorization of whitepapers using statistical and human judgment methods, it is determined that there is enough evidence to conclude that the LDA model appropriately categorizes the ICO whitepapers. The results from a two-population proportion test …

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Map-Coverage: A Novel Coverage Criterion For Testing Thread-Safe Classes, Zan Wang, Yingquan Zhao, Shuang Liu, Jun Sun, Xiang Chen, Huarui Lin Nov 2019

Map-Coverage: A Novel Coverage Criterion For Testing Thread-Safe Classes, Zan Wang, Yingquan Zhao, Shuang Liu, Jun Sun, Xiang Chen, Huarui Lin

Research Collection School Of Computing and Information Systems

Concurrent programs must be thoroughly tested, as concurrency bugs are notoriously hard to detect. Code coverage criteria can be used to quantify the richness of a test suite (e.g., whether a program has been tested sufficiently) or provide practical guidelines on test case generation (e.g., as objective functions used in program fuzzing engines). Traditional code coverage criteria are, however, designed for sequential programs and thus ineffective for concurrent programs. In this work, we introduce a novel code coverage criterion for testing thread-safe classes called MAP-coverage (short for memory-access patterns). The motivation is that concurrency bugs are often correlated with certain …

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Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang Nov 2019

Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang

Research Collection School Of Computing and Information Systems

With the rapid development and popularization of mobile and wireless communication technologies, ridesourcing companies have been able to leverage internet-based platforms to operate e-hailing services in many cities around the world. These companies connect passengers and drivers in real time and are disruptively changing the transportation indus- try. As pioneers in a general sharing economy context, ridesourcing shared transportation platforms consist of a typical two-sided market. On the demand side, passengers are sensi- tive to the price and quality of the service. On the supply side, drivers, as freelancers, make working decisions flexibly based on their income from the platform …

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Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang Nov 2019

Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang

Research Collection School Of Computing and Information Systems

The threat of attack faced by cyber-physical systems (CPSs), especially when they play a critical role in automating public infrastructure, has motivated research into a wide variety of attack defence mechanisms. Assessing their effectiveness is challenging, however, as realistic sets of attacks to test them against are not always available. In this paper, we propose smart fuzzing, an automated, machine learning guided technique for systematically finding 'test suites' of CPS network attacks, without requiring any knowledge of the system's control programs or physical processes. Our approach uses predictive machine learning models and metaheuristic search algorithms to guide the fuzzing of …

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Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee Nov 2019

Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee

Research Collection School Of Computing and Information Systems

We present SmrtFridge, a consumer-grade smart fridge prototype that demonstrates two key capabilities: (a) identify the individual food items that users place in or remove from a fridge, and (b) estimate the residual quantity of food items inside a refrigerated container (opaque or transparent). Notably, both of these inferences are performed unobtrusively, without requiring any explicit user action or tagging of food objects. To achieve these capabilities, SmrtFridge uses a novel interaction-driven, multi-modal sensing pipeline, where Infrared (IR) and RGB video sensing, triggered whenever a user interacts naturally with the fridge, is used to extract a foreground visual image of …

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Autofocus: Interpreting Attention-Based Neural Networks By Code Perturbation, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang Nov 2019

Autofocus: Interpreting Attention-Based Neural Networks By Code Perturbation, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Despite being adopted in software engineering tasks, deep neural networks are treated mostly as a black box due to the difficulty in interpreting how the networks infer the outputs from the inputs. To address this problem, we propose AutoFocus, an automated approach for rating and visualizing the importance of input elements based on their effects on the outputs of the networks. The approach is built on our hypotheses that (1) attention mechanisms incorporated into neural networks can generate discriminative scores for various input elements and (2) the discriminative scores reflect the effects of input elements on the outputs of the …

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Saffron: Adaptive Grammar-Based Fuzzing For Worst-Case Analysis, Xuan Bach D. Le, Corina Pasareanu, Rohan Padhye, David Lo, Willem Visser, Koushik Sen Nov 2019

Saffron: Adaptive Grammar-Based Fuzzing For Worst-Case Analysis, Xuan Bach D. Le, Corina Pasareanu, Rohan Padhye, David Lo, Willem Visser, Koushik Sen

Research Collection School Of Computing and Information Systems

Fuzz testing has been gaining ground recently with substantial efforts devoted to the area. Typically, fuzzers take a set of seed inputs and leverage random mutations to continually improve the inputs with respect to a cost, e.g. program code coverage, to discover vulnerabilities or bugs. Following this methodology, fuzzers are very good at generating unstructured inputs that achieve high coverage. However fuzzers are less effective when the inputs are structured, say they conform to an input grammar. Due to the nature of random mutations, the overwhelming abundance of inputs generated by this common fuzzing practice often adversely hinders the effectiveness …

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Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu Nov 2019

Emotion-Aware Chat Machine: Automatic Emotional Response Generation For Human-Like Emotional Interaction, Wei Wei, Jiayi Liu, Xianling Mao, Guibing Guo, Feida Zhu, Pan Zhou, Yuchong Hu

Research Collection School Of Computing and Information Systems

The consistency of a response to a given post at semantic-level and emotional-level is essential for a dialogue system to deliver human-like 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 by proposing a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post for generating more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms …

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Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions, Nur Camellia Binte Zakaria, Rajesh Balan, Youngki Lee Nov 2019

Stressmon: Scalable Detection Of Perceived Stress And Depression Using Passive Sensing Of Changes In Work Routines And Group Interactions, Nur Camellia Binte Zakaria, Rajesh Balan, Youngki Lee

Research Collection School Of Computing and Information Systems

Stress and depression are a common affliction in all walks of life. When left unmanaged, stress can inhibit productivity or cause depression. Depression can occur independently of stress. There has been a sharp rise in mobile health initiatives to monitor stress and depression. However, these initiatives usually require users to install dedicated apps or multiple sensors, making such solutions hard to scale. Moreover, they emphasise sensing individual factors and overlook social interactions, which plays a significant role in influencing stress and depression while being a part of a social system. We present StressMon, a stress and depression detection system that …

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Aspect And Opinion Aware Abstractive Review Summarization With Reinforced Hard Typed Decoder, Yufei Tian, Jianfei Yu, Jing Jiang Nov 2019

Aspect And Opinion Aware Abstractive Review Summarization With Reinforced Hard Typed Decoder, Yufei Tian, Jianfei Yu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we study abstractive review summarization. Observing that review summaries often consist of aspect words, opinion words and context words, we propose a two-stage reinforcement learning approach, which first predicts the output word type from the three types, and then leverages the predicted word type to generate the final word distribution. Experimental results on two Amazon product review datasets demonstrate that our method can consistently outperform several strong baseline approaches based on ROUGE scores.

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Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak Nov 2019

Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak

Research Collection School Of Computing and Information Systems

Gender and racial diversity in the mediated images from the media shape our perception of different demographic groups. In this work, we investigate gender and racial diversity of 85,957 advertising images shared by the 73 top international brands on Instagram and Facebook. We hope that our analyses give guidelines on how to build a fully automated watchdog for gender and racial diversity in online advertisements.

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Traceable Dynamic Public Auditing With Identity Privacy Preserving For Cloud Storage, Yinghui Zhang, Tiantian Zhang, Rui Guo, Shengmin Xu, Dong Zheng Nov 2019

Traceable Dynamic Public Auditing With Identity Privacy Preserving For Cloud Storage, Yinghui Zhang, Tiantian Zhang, Rui Guo, Shengmin Xu, Dong Zheng

Research Collection School Of Computing and Information Systems

In cloud computing era, an increasing number of resource-constrained users outsource their data to cloud servers. Due to the untrustworthiness of cloud servers, it is important to ensure the integrity of outsourced data. However, most of existing solutions still have challenging issues needing to be addressed, such as the identity privacy protection of users, the traceability of users, the supporting of dynamic user operations, and the publicity of auditing. In order to tackle these issues simultaneously, in this paper, we propose a traceable dynamic public auditing scheme with identity privacy preserving for cloud storage. In the proposed scheme, a single …

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Semi-Supervised Entity Alignment Via Joint Knowledge Embedding Model And Cross-Graph Model, Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua Nov 2019

Semi-Supervised Entity Alignment Via Joint Knowledge Embedding Model And Cross-Graph Model, Chengjiang Li, Yixin Cao, Lei Hou, Jiaxin Shi, Juanzi Li, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment aims at integrating complementary knowledge graphs (KGs) from different sources or languages, which may benefit many knowledge-driven applications. It is challenging due to the heterogeneity of KGs and limited seed alignments. In this paper, we propose a semi-supervised entity alignment method by joint Knowledge Embedding model and Cross-Graph model (KECG). It can make better use of seed alignments to propagate over the entire graphs with KG-based constraints. Specifically, as for the knowledge embedding model, we utilize TransE to implicitly complete two KGs towards consistency and learn relational constraints between entities. As for the cross-graph model, we extend Graph …

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Shellnet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung Nov 2019

Shellnet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung

Research Collection School Of Computing and Information Systems

Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While being able to achieve good accuracies in various scene understanding tasks, previous methods often have low training speed and complex network architecture. In this paper, we address these problems by proposing an efficient end-to-end permutation invariant convolution for point cloud deep learning. Our simple yet effective convolution operator named ShellConv uses statistics from concentric spherical shells to define representative features and resolve the point order ambiguity, allowing traditional convolution to perform on such features. …

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Automatic Generation Of Pull Request Descriptions, Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li Nov 2019

Automatic Generation Of Pull Request Descriptions, Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li

Research Collection School Of Computing and Information Systems

Enabled by the pull-based development model, developers can easily contribute to a project through pull requests (PRs). When creating a PR, developers can add a free-form description to describe what changes are made in this PR and/or why. Such a description is helpful for reviewers and other developers to gain a quick understanding of the PR without touching the details and may reduce the possibility of the PR being ignored or rejected. However, developers sometimes neglect to write descriptions for PRs. For example, in our collected dataset with over 333K PRs, more than 34% of the PR descriptions are empty. …

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