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Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo
Treecaps: Tree-Structured Capsule Networks For Program Source Code Processing, Vinoj Jayasundara, Duy Quoc Nghi Bui, Lingxiao Jiang, David Lo
Research Collection School Of Computing and Information Systems
Program comprehension is a fundamental task in software development and maintenance processes. Software developers often need to understand a large amount of existing code before they can develop new features or fix bugs in existing programs. Being able to process programming language code automatically and provide summaries of code functionality accurately can significantly help developers to reduce time spent in code navigation and understanding, and thus increase productivity. Different from natural language articles, source code in programming languages often follows rigid syntactical structures and there can exist dependencies among code elements that are located far away from each other through …
Online Content Consumption: Social Endorsements, Observational Learning And Word-Of-Mouth, Qian Tang, Tingting Song, Liangfei Qiu, Ashish Agarwal
Online Content Consumption: Social Endorsements, Observational Learning And Word-Of-Mouth, Qian Tang, Tingting Song, Liangfei Qiu, Ashish Agarwal
Research Collection School Of Computing and Information Systems
The consumption of online content can occur through observational learning (OL) whereby consumers follow previous consumers’ choices or social endorsement (SE) wherein consumers receive content sharing from their social ties. As users consume content, they also generate post-consumption word-of-mouth (WOM) signals. OL, SE and WOM together shape the diffusion of the content. This study examines the drivers of SE and the effect of SE on content consumption and post-consumption WOM. In particular, we compare SE with OL. Using a random sample of 8,945 new videos posted on YouTube, we collected a multi-platform dataset consisting of data on video consumption and …
Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen
Harmony Search Algorithm For Time-Dependent Vehicle Routing Problem With Time Windows, Yun-Chia Liang, Vanny Minanda, Aldy Gunawan, Angela Hsiang-Ling Chen
Research Collection School Of Computing and Information Systems
Vehicle Routing Problem (VRP) is a combinatorial problem where a certain set of nodes must be visited within a certain amount of time as well as the vehicle’s capacity. There are numerous variants of VRP such as VRP with time windows, where each node has opening and closing time, therefore, the visiting time must be during that interval. Another variant takes time-dependent constraint into account. This variant fits real-world scenarios, where at different period of time, the speed on the road varies depending on the traffic congestion. In this study, three objectives – total traveling time, total traveling distance, and …
Guest Editorial: Special Issue On Software Engineering For Mobile Applications, Sebastiano Panichella, Fabio Palomba, David Lo, Meiyappan Nagappan
Guest Editorial: Special Issue On Software Engineering For Mobile Applications, Sebastiano Panichella, Fabio Palomba, David Lo, Meiyappan Nagappan
Research Collection School Of Computing and Information Systems
As Andreessen stated “software is eating the world” (Andreessen 2011). Most of todays industries, from engineering, manufacturing, logistics to health, are run on enterprise software applications and can efficiently automate the analysis and manipulation of several, heterogeneous types of data. One of the most prominent examples of such software diffusion is represented by the widespread adoption of mobile applications. Indeed, during the recent years, the Global App Economy experienced unprecedented growth, driven by the increasing usage of apps and by the greater adoption of mobile devices (e.g., smartphone) around the globe. This mobile application market, which is expected in few …
Simulation Model To Evaluate Effectiveness Of Queue Management Tool In Supermarket Retail Chain, Michelle L. F. Cheong, Yong Qing Chia
Simulation Model To Evaluate Effectiveness Of Queue Management Tool In Supermarket Retail Chain, Michelle L. F. Cheong, Yong Qing Chia
Research Collection School Of Computing and Information Systems
As part of its continuous process to improve operational excellence and productivity, a retail company in South East Asia trialed the usage of a commercial market retail tool, known as Queue Buster, to improve queue management processes in its physical stores. We employed Queuing Theory to determine the effectiveness of implementing the Queue Buster. Specifically, we constructed a queuing simulation model based on input parameters derived from queuing data collected at a pilot store. Three main performance metrics - Wait Time, System Time and System Length were measured fortwo different queue systems, with and without the implementation of Queue Buster. …
Self-Organizing Neural Networks For Universal Learning And Multimodal Memory Encoding, Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng
Self-Organizing Neural Networks For Universal Learning And Multimodal Memory Encoding, Ah-Hwee Tan, Budhitama Subagdja, Di Wang, Lei Meng
Research Collection School Of Computing and Information Systems
Learning and memory are two intertwined cognitive functions of the human brain. This paper shows how a family of biologically-inspired self-organizing neural networks, known as fusion Adaptive Resonance Theory (fusion ART), may provide a viable approach to realizing the learning and memory functions. Fusion ART extends the single-channel Adaptive Resonance Theory (ART) model to learn multimodal pattern associative mappings. As a natural extension of ART, various forms of fusion ART have been developed for a myriad of learning paradigms, ranging from unsupervised learning to supervised learning, semi-supervised learning, multimodal learning, reinforcement learning, and sequence learning. In addition, fusion ART models …
Implementing Security-Enhanced Phr System In The Cloud Using Fame, Zuobin Ying, Wenjie Jiang, Ximeng Liu, Shengmin Xu
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 …
Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele
Learning To Self-Train For Semi-Supervised Few-Shot Classification, Xinzhe Li, Qianru Sun, Yaoyao Liu, Shibao Zheng, Qin Zhou, Tat-Seng Chua, Bernt Schiele
Research Collection School Of Computing and Information Systems
Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method called learning to self-train (LST) that leverages unlabeled data and specifically meta-learns how to cherry-pick and label such unsupervised data to further improve performance. To this end, we train the LST model through a large number of semi-supervised few-shot tasks. On each task, we train a few-shot model to predict pseudo labels for …
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Finding Needles In A Haystack: Leveraging Co-Change Dependencies To Recommend Refactorings, Marcos César De Oliveira, Davi Freitas, Rodrigo Bonifacio, Gustavo Pinto, David Lo
Research Collection School Of Computing and Information Systems
A fine-grained co-change dependency arises when two fine-grained source-code entities, e.g., a method,change frequently together. This kind of dependency is relevant when considering remodularization efforts (e.g., to keep methods that change together in the same class). However, existing approaches forrecommending refactorings that change software decomposition (such as a move method) do not explorethe use of fine-grained co-change dependencies. In this paper we present a novel approach for recommending move method and move field refactorings, which removes co-change dependencies and evolutionary smells, a particular type of dependency that arise when fine-grained entities that belong to different classes frequently change together. First …
Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman
Clustering Models For Topic Analysis In Graduate Discussion Forums, Mallika Gokarn Nitin, Swapna Gottipati, Venky Shankararaman
Research Collection School Of Computing and Information Systems
Discussion forums provide the base content for creating a knowledge repository. It contains discussion threads related to key course topics that are debated by the students. In order to better understand the student learning experience, the instructor needs to analyse these discussion threads. This paper proposes the use of clustering models and interactive visualizations to conduct a qualitative analysis of graduate discussion forums. Our goal is to identify the sub-topics and topic evolutions in the discussion forums by applying text mining techniques. Our approach generates insights into the topic analysis in the forums and discovers the students’ cognitive understanding within …
Multi-Hop Knowledge Base Question Answering With An Iterative Sequence Matching Model, Yunshi Lan, Shuohang Wang, Jing Jiang
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.
Scompile: Critical Path Identification And Analysis For Smart Contracts, Jialiang Chang, Bo Gao, Hao Xiao, Jun Sun, Yan Cai, Zijiang Yang
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 …
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
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 …
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
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 …
Aspect And Opinion Aware Abstractive Review Summarization With Reinforced Hard Typed Decoder, Yufei Tian, Jianfei Yu, Jing Jiang
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.
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
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 …
Ridesourcing Systems: A Framework And Review, Hai Wang, Hai Yang
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 …
Safe Inputs Approximation For Black-Box Systems, Bai Xue, Yang Liu, Lei Ma, Xiyue Zhang, Meng Sun, Xiaofei Xie
Safe Inputs Approximation For Black-Box Systems, Bai Xue, Yang Liu, Lei Ma, Xiyue Zhang, Meng Sun, Xiaofei Xie
Research Collection School Of Computing and Information Systems
Given a family of independent and identically distributed samples extracted from the input region and their corresponding outputs, in this paper we propose a method to under-approximate the set of safe inputs that lead the blackbox system to respect a given safety specification. Our method falls within the framework of probably approximately correct (PAC) learning. The computed under-approximation comes with statistical soundness provided by the underlying PAC learning process. Such a set, which we call a PAC under-approximation, is obtained by computing a PAC model of the black-box system with respect to the specified safety specification. In our method, the …
Predicting Audience Engagement Across Social Media Platforms In The News Domain, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen
Predicting Audience Engagement Across Social Media Platforms In The News Domain, Kholoud Khalil Aldous, Jisun An, Bernard J. Jansen
Research Collection School Of Computing and Information Systems
We analyze cross-platform factors for posts on both single and multiple social media platforms for numerous news outlets to better predict audience engagement, precisely the number of likes and comments. We collect 676,779 social media posts from 53 news outlets during eight months on four social media platforms (Facebook, Instagram, Twitter, and YouTube), along with the associated comments (more than 31 million) and the number of likes (more than 840 million). We develop a framework for predicting the audience engagement based on both linguistic features of the post and social media platform factors. Among other findings, results show that content …
Vireo-Eurecom @ Trecvid 2019: Ad-Hoc Video Search (Avs), Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Francis Danny, Benoit Huet
Vireo-Eurecom @ Trecvid 2019: Ad-Hoc Video Search (Avs), Phuong Anh Nguyen, Jiaxin Wu, Chong-Wah Ngo, Francis Danny, Benoit Huet
Research Collection School Of Computing and Information Systems
In this paper, we describe the systems developed for Ad-hoc Video Search (AVS) task at TRECVID 2019[1] and the achieved results.
Low-Resource Name Tagging Learned With Weakly Labeled Data, Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, Heng Ji
Low-Resource Name Tagging Learned With Weakly Labeled Data, Yixin Cao, Zikun Hu, Tat-Seng Chua, Zhiyuan Liu, Heng Ji
Research Collection School Of Computing and Information Systems
Name tagging in low-resource languages or domains suffers from inadequate training data. Existing work heavily relies on additional information, while leaving those noisy annotations unexplored that extensively exist on the web. In this paper, we propose a novel neural model for name tagging solely based on weakly labeled (WL) data, so that it can be applied in any low-resource settings. To take the best advantage of all WL sentences, we split them into high-quality and noisy portions for two modules, respectively: (1) a classification module focusing on the large portion of noisy data can efficiently and robustly pretrain the tag …
Agile Earth Observation Satellite Scheduling: An Orienteering Problem With Time-Dependent Profits And Travel Times, Guansheng Peng, Reginald Dewil, Cédric Verbeeck, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen
Agile Earth Observation Satellite Scheduling: An Orienteering Problem With Time-Dependent Profits And Travel Times, Guansheng Peng, Reginald Dewil, Cédric Verbeeck, Aldy Gunawan, Lining Xing, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
The scheduling problem of an Agile Earth Observation Satellite is to schedule a subset of weighted observation tasks with each a specific “profit” in order to maximize the total collected profit, under its operational constraints. The “time-dependent transition time” and the “time-dependent profit” are two crucial features of this problem. The former relates to the fact that each pair of consecutive tasks requires a transition time to maneuver the look angle of the camera from the previous task to the next task. The latter follows from the fact that a different look angle of an observation leads to a different …
Ad-Link: An Adaptive Approach For User Identity Linkage, Xin Mu, Wei Xie, Ka Wei, Roy Lee, Feida Zhu, Ee Peng Lim
Ad-Link: An Adaptive Approach For User Identity Linkage, Xin Mu, Wei Xie, Ka Wei, Roy Lee, Feida Zhu, Ee Peng Lim
Research Collection School Of Computing and Information Systems
User identity linkage (UIL) refers to linking accounts of the same user across different online social platforms. The state-of-the-art UIL methods usually perform account matching using user account’s features derived from the profile attributes, content and relationships. They are however static and do not adapt well to fast-changing online social data due to: (a) new content and activities generated by users; as well as (b) new platform functions introduced to users. In particular, the importance of features used in UIL methods may change over time and new important user features may be introduced. In this paper, we proposed AD-Link, a …
Choosing Protection: User Investments In Security Measures For Cyber Risk Management, Yoav Ben Yaakov, Xinrun Wang, Joachim Meyer, Bo An
Choosing Protection: User Investments In Security Measures For Cyber Risk Management, Yoav Ben Yaakov, Xinrun Wang, Joachim Meyer, Bo An
Research Collection School Of Computing and Information Systems
Firewalls, Intrusion Detection Systems (IDS), and cyber-insurance are widely used to protect against cyber-attacks and their consequences. The optimal investment in each of these security measures depends on the likelihood of threats and the severity of the damage they cause, on the user’s ability to distinguish between malicious and non-malicious content, and on the properties of the different security measures and their costs. We present a model of the optimal investment in the security measures, given that the effectiveness of each measure depends partly on the performance of the others. We also conducted an online experiment in which participants classified …
Shellnet: Efficient Point Cloud Convolutional Neural Networks Using Concentric Shells Statistics, Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
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. …
Automatic Generation Of Pull Request Descriptions, Zhongxin Liu, Xin Xia, Christoph Treude, David Lo, Shanping Li
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. …
Revisiting Collaboration Through Mixed Reality: The Evolution Of Groupware, Barrett Ens, Joel Lanir, Anthony Tang, Scott Bateman, Gun Lee, Thammathip Piumsomboon, Mark Billinghurst
Revisiting Collaboration Through Mixed Reality: The Evolution Of Groupware, Barrett Ens, Joel Lanir, Anthony Tang, Scott Bateman, Gun Lee, Thammathip Piumsomboon, Mark Billinghurst
Research Collection School Of Computing and Information Systems
Collaborative Mixed Reality (MR) systems are at a critical point in time as they are soon to become more commonplace. However, MR technology has only recently matured to the point where researchers can focus deeply on the nuances of supporting collaboration, rather than needing to focus on creating the enabling technology. In parallel, but largely independently, the field of Computer Supported Cooperative Work (CSCW) has focused on the fundamental concerns that underlie human communication and collaboration over the past 30-plus years. Since MR research is now on the brink of moving into the real world, we reflect on three decades …
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning, Yan Zheng, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, Yingfeng Chen, Changjie Fan
Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning, Yan Zheng, Xiaofei Xie, Ting Su, Lei Ma, Jianye Hao, Zhaopeng Meng, Yang Liu, Ruimin Shen, Yingfeng Chen, Changjie Fan
Research Collection School Of Computing and Information Systems
—Game testing has been long recognized as a notoriously challenging task, which mainly relies on manual playing and scripting based testing in game industry. Even until recently, automated game testing still remains to be largely untouched niche. A key challenge is that game testing often requires to play the game as a sequential decision process. A bug may only be triggered until completing certain difficult intermediate tasks, which requires a certain level of intelligence. The recent success of deep reinforcement learning (DRL) sheds light on advancing automated game testing, without human competitive intelligent support. However, the existing DRLs mostly focus …
Coverage-Guided Fuzzing For Feedforward Neural Networks, Xiaofei Xie, Hongxu Chen, Yi Li, Lei Ma, Yang Liu, Jianjun Zhao
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 …
An Empirical Study Towards Characterizing Deep Learning Development And Deployment Across Different Frameworks And Platforms, Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li
An Empirical Study Towards Characterizing Deep Learning Development And Deployment Across Different Frameworks And Platforms, Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li
Research Collection School Of Computing and Information Systems
Deep Learning (DL) has recently achieved tremendous success. A variety of DL frameworks and platforms play a key role to catalyze such progress. However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment. Till now, there is no study on how various mainstream frameworks and platforms influence both DL software development and deployment in practice.To fill this gap, we take the first step towards understanding how the most widely-used DL frameworks and platforms support the DL software development and deployment. We conduct a systematic study on these frameworks …