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Articles 3871 - 3900 of 9025
Full-Text Articles in Computer Sciences
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 …
Twitter And The Magic Pony, Singapore Management University
Twitter And The Magic Pony, Singapore Management University
Perspectives@SMU
London-based Magic Pony went from A.I. startup to a multimillion dollar cash-out in 18 months. Was selling to Twitter the right exit strategy?
Resource Characterisation Of Personal-Scale Sensing Models On Edge Accelerators, Mattia Antonini, Tran Huy Vu, Chulhong Min, Alessandro Montanari, Akhil Mathur, Fahim Kawsar
Resource Characterisation Of Personal-Scale Sensing Models On Edge Accelerators, Mattia Antonini, Tran Huy Vu, Chulhong Min, Alessandro Montanari, Akhil Mathur, Fahim Kawsar
Student Publications
Edge accelerator is a class of brand-new purpose-built System On a Chip (SoC) for running deep learning models efficiently on edge devices. These accelerators offer various benefits such as ultra-low latency, sensitive data protection, and high availability due to their locality and are opening up interminable opportunities for building sensory systems in the real world. Naturally, in the context of sensory awareness systems, e.g., IoT, wearables, and other sensory devices, the emergence of edge accelerators is pushing us to rethink how we design these systems at a personal-scale. To this end, in this paper we take a closer look at …
Gender And Racial Diversity In Commercial Brands’ Advertising Images On Social Media, Jisun An, Haewoon Kwak
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.
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 …
Visualizing The Invisible: Occluded Vehicle Segmentation And Recovery, Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, Jia Pan
Visualizing The Invisible: Occluded Vehicle Segmentation And Recovery, Xiaosheng Yan, Feigege Wang, Wenxi Liu, Yuanlong Yu, Shengfeng He, Jia Pan
Research Collection School Of Computing and Information Systems
In this paper, we propose a novel iterative multi-task framework to complete the segmentation mask of an occluded vehicle and recover the appearance of its invisible parts. In particular, firstly, to improve the quality of the segmentation completion, we present two coupled discriminators that introduce an auxiliary 3D model pool for sampling authentic silhouettes as adversarial samples. In addition, we propose a two-path structure with a shared network to enhance the appearance recovery capability. By iteratively performing the segmentation completion and the appearance recovery, the results will be progressively refined. To evaluate our method, we present a dataset, Occluded Vehicle …
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.
Gender And Racial Diversity In Commercial Brands' Advertising Images On Social Media, Jisun An, Haewoon Kwak
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.
Vireojd-Mm @ Trecvid 2019: Activities In Extended Video (Actev), Zhijian Hou, Ying-Wei Pan, Ting Yao, Chong-Wah Ngo
Vireojd-Mm @ Trecvid 2019: Activities In Extended Video (Actev), Zhijian Hou, Ying-Wei Pan, Ting Yao, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
In this paper, we describe the system developed for Activities in Extended Video(ActEV) task at TRECVid 2019 [1] and the achieved results. Activities in Extended Video(ActEV): The goal of Activities in Extended Video is to spatially and temporally localize the action instances in a surveillance setting. We have participated in previous ActEV prize challenge. Since the only difference between the two challenges is evaluation metric, we maintain previous pipeline [2] for this challenge. The pipeline has three stages: object detection, tubelet generation and temporal action localization. This time we extend the system for two aspects separately: better object detection and …
Eurecom At Trecvid Avs 2019, Danny Francis, Phuong Anh Nguyen, Benoit Huet, Chong-Wah Ngo
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.
Special Issue On Multimedia Recommendation And Multi-Modal Data Analysis, Xiangnan He, Zhenguang Liu, Hanwang Zhang, Chong-Wah Ngo, Svebor Karaman, Yongfeng Zhang
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 …
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 …
Learning-Guided Network Fuzzing For Testing Cyber-Physical System Defences, Yuqi Chen, Chris Poskitt, Jun Sun, Sridhar Adepu, Fan Zhang
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 …
Smrtfridge: Iot-Based, User Interaction-Driven Food Item & Quantity Sensing, Amit Sharma, Archan Misra, Vengateswaran Subramaniam, Youngki Lee
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 …
Data Security Issues In Deep Learning: Attacks, Countermeasures, And Opportunities, Guowen Xu, Hongwei Li, Hao Ren, Kan Yang, Robert H. Deng
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 …
Estimating Glycemic Impact Of Cooking Recipes Via Online Crowdsourcing And Machine Learning, Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney
Estimating Glycemic Impact Of Cooking Recipes Via Online Crowdsourcing And Machine Learning, Helena Lee, Palakorn Achananuparp, Yue Liu, Ee-Peng Lim, Lav R. Varshney
Research Collection School Of Computing and Information Systems
Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels. However, laboratory analysis of dietary glycemic potency is time-consuming and expensive. In this paper, we explore a data-driven approach utilizing online crowdsourcing and machine learning to estimate the glycemic impact of cooking recipes. We show that a commonly used healthiness metric may not always be effective in determining recipes suitable for diabetics, thus emphasizing the importance of the glycemic-impact estimation task. Our best classification model, trained on nutritional and crowdsourced data obtained from Amazon Mechanical Turk (AMT), can …
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 …
Secure Virtual Machine Placement In Cloud Data Centers, Amit Agarwal, Nguyen Binh Duong Ta
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 …
Autofocus: Interpreting Attention-Based Neural Networks By Code Perturbation, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
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 …
Saffron: Adaptive Grammar-Based Fuzzing For Worst-Case Analysis, Xuan Bach D. Le, Corina Pasareanu, Rohan Padhye, David Lo, Willem Visser, Koushik Sen
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 …
Automated Theme Search In Ico Whitepapers, Chuanjie Fu, Andrew Koh, Paul Griffin
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 …
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
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 …
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
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 …
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.
Vitamon: Measuring Heart Rate Variability Using Smartphone Front Camera, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko, Youngki Lee
Vitamon: Measuring Heart Rate Variability Using Smartphone Front Camera, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko, Youngki Lee
Research Collection School Of Computing and Information Systems
We present VitaMon, a mobile sensing system that can measure the inter-heartbeat interval (IBI) from the facial video captured by a commodity smartphone's front camera. The continuous IBI measurement is used to compute heart rate variability (HRV), one of the most important markers of the autonomic nervous system (ANS) regulation. The underlying idea of VitaMon is that video recording of human face contains multiple cardiovascular pulse signals with different phase shift. Our measurement on 10 participants shows the significant time delay (36.79 ms) between the pulse signals measured at the jaw region and forehead region. VitaMon leverages deep neural network …
Map-Coverage: A Novel Coverage Criterion For Testing Thread-Safe Classes, Zan Wang, Yingquan Zhao, Shuang Liu, Jun Sun, Xiang Chen, Huarui Lin
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 …
Statistical Log Differencing, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Statistical Log Differencing, Lingfeng Bao, Nimrod Busany, David Lo, Shahar Maoz
Research Collection School Of Computing and Information Systems
Recent works have considered the problem of log differencing: given two or more system’s execution logs, output a model of their differences. Log differencing has potential applications in software evolution, testing, and security. In this paper we present statistical log differencing, which accounts for frequencies of behaviors found in the logs. We present two algorithms, s2KDiff for differencing two logs, and snKDiff, for differencing of many logs at once, both presenting their results over a single inferred model. A unique aspect of our algorithms is their use of statistical hypothesis testing: we let the engineer control the sensitivity of the …