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Articles 4531 - 4560 of 8495
Full-Text Articles in Computer Sciences
Vurle: Automatic Vulnerability Detection And Repair By Learning From Examples, Ma Siqi, Ferdian Thung, David Lo, Cong Sun, Robert H. Deng
Vurle: Automatic Vulnerability Detection And Repair By Learning From Examples, Ma Siqi, Ferdian Thung, David Lo, Cong Sun, Robert H. Deng
Research Collection School Of Computing and Information Systems
Vulnerability becomes a major threat to the security of many systems. Attackers can steal private information and perform harmful actions by exploiting unpatched vulnerabilities. Vulnerabilities often remain undetected for a long time as they may not affect typical systems’ functionalities. Furthermore, it is often difficult for a developer to fix a vulnerability correctly if he/she is not a security expert. To assist developers to deal with multiple types of vulnerabilities, we propose a new tool, called VuRLE, for automatic detection and repair of vulnerabilities. VuRLE (1) learns transformative edits and their contexts (i.e., code characterizing edit locations) from examples of …
Stylizing Face Images Via Multiple Exemplars, Yibing Song, Linchao Bao, Shengfeng He, Qingxiong Yang, Ming-Hsuan Yang
Stylizing Face Images Via Multiple Exemplars, Yibing Song, Linchao Bao, Shengfeng He, Qingxiong Yang, Ming-Hsuan Yang
Research Collection School Of Computing and Information Systems
We address the problem of transferring the style of a headshot photo to face images. Existing methods using a single exemplar lead to inaccurate results when the exemplar does not contain sufficient stylized facial components for a given photo. In this work, we propose an algorithm to stylize face images using multiple exemplars containing different subjects in the same style. Patch correspondences between an input photo and multiple exemplars are established using a Markov Random Field (MRF), which enables accurate local energy transfer via Laplacian stacks. As image patches from multiple exemplars are used, the boundaries of facial components on …
Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang
Sugarmate: Non-Intrusive Blood Glucose Monitoring With Smartphones, Weixi Gu, Yuxun Zhou, Zimu Zhou, Xi Liu, Han Zou, Pei Zhang, Costas J. Spanos, Lin Zhang
Research Collection School Of Computing and Information Systems
Inferring abnormal glucose events such as hyperglycemia and hypoglycemia is crucial for the health of both diabetic patients and non-diabetic people. However, regular blood glucose monitoring can be invasive and inconvenient in everyday life. We present SugarMate, a first smartphone-based blood glucose inference system as a temporary alternative to continuous blood glucose monitors (CGM) when they are uncomfortable or inconvenient to wear. In addition to the records of food, drug and insulin intake, it leverages smartphone sensors to measure physical activities and sleep quality automatically. Provided with the imbalanced and often limited measurements, a challenge of SugarMate is the inference …
Nlp2code: Code Snippet Content Assist Via Natural Language Tasks, Brock A. Campbell, Christoph Treude
Nlp2code: Code Snippet Content Assist Via Natural Language Tasks, Brock A. Campbell, Christoph Treude
Research Collection School Of Computing and Information Systems
Developers increasingly take to the Internet for code snippets to integrate into their programs. To save developers the time required to switch from their development environments to a web browser in the quest for a suitable code snippet, we introduce NLP2Code, a content assist for code snippets. Unlike related tools, NLP2Code integrates directly into the source code editor and provides developers with a content assist feature to close the vocabulary gap between developers’ needs and code snippet meta data. Our preliminary evaluation of NLP2Code shows that the majority of invocations lead to code snippets rated as helpful by users and …
On-Demand Developer Documentation, Martin P. Robillard, Andrian Marcus, Christoph Treude, Gabriele Bavota, Oscar Chaparro, Neil Ernst, Marco Aurélio Gerosa, Michael Godfrey, Michele Lanza, Mario Linares-Vasquez, Gail C. Murphy, Laura Moreno, David Shepherd, Edmund Wong
On-Demand Developer Documentation, Martin P. Robillard, Andrian Marcus, Christoph Treude, Gabriele Bavota, Oscar Chaparro, Neil Ernst, Marco Aurélio Gerosa, Michael Godfrey, Michele Lanza, Mario Linares-Vasquez, Gail C. Murphy, Laura Moreno, David Shepherd, Edmund Wong
Research Collection School Of Computing and Information Systems
We advocate for a paradigm shift in supporting the information needs of developers, centered around the concept of automated on-demand developer documentation. Currently, developer information needs are fulfilled by asking experts or consulting documentation. Unfortunately, traditional documentation practices are inefficient because of, among others, the manual nature of its creation and the gap between the creators and consumers. We discuss the major challenges we face in realizing such a paradigm shift, highlight existing research that can be leveraged to this end, and promote opportunities for increased convergence in research on software documentation.
Loopster: Static Loop Termination Analysis, Xiaofei Xie, Bihuan Chen, Liang Zou, Shang-Wei Lin, Yang Liu, Xiaohong Li
Loopster: Static Loop Termination Analysis, Xiaofei Xie, Bihuan Chen, Liang Zou, Shang-Wei Lin, Yang Liu, Xiaohong Li
Research Collection School Of Computing and Information Systems
Loop termination is an important problem for proving the correctness of a system and ensuring that the system always reacts. Existing loop termination analysis techniques mainly depend on the synthesis of ranking functions, which is often expensive. In this paper, we present a novel approach, named Loopster, which performs an efficient static analysis to decide the termination for loops based on path termination analysis and path dependency reasoning. Loopster adopts a divide-and-conquer approach: (1) we extract individual paths from a target multi-path loop and analyze the termination of each path, (2) analyze the dependencies between each two paths, and then …
Inferring Spread Of Readers’ Emotion Affected By Online News, Agus Sulistya, Ferdian Thung, David Lo
Inferring Spread Of Readers’ Emotion Affected By Online News, Agus Sulistya, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Depending on the reader, A news article may be viewed from many different perspectives, thus triggering different (and possibly contradicting) emotions. In this paper, we formulate a problem of predicting readers’ emotion distribution affected by a news article. Our approach analyzes affective annotations provided by readers of news articles taken from a non-English online news site. We create a new corpus from the annotated articles, and build a domain-specific emotion lexicon and word embedding features. We finally construct a multi-target regression model from a set of features extracted from online news articles. Our experiments show that by combining lexicon and …
Vcksm: Verifiable Conjunctive Keyword Search Over Mobile E-Health Cloud In Shared Multi-Owner Settings, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Qi Jiang, Junwei Zhang, Limin Shen, Zhiquan Liu
Vcksm: Verifiable Conjunctive Keyword Search Over Mobile E-Health Cloud In Shared Multi-Owner Settings, Yinbin Miao, Jianfeng Ma, Ximeng Liu, Qi Jiang, Junwei Zhang, Limin Shen, Zhiquan Liu
Research Collection School Of Computing and Information Systems
Searchable encryption (SE) is a promising technique which enables cloud users to conduct search over encrypted cloud data in a privacy-preserving way, especially for the electronic health record (EHR) system that contains plenty of medical history, diagnosis, radiology images, etc. In this paper, we focus on a more practical scenario, also named as the shared multi-owner settings, where each e-health record is co-owned by a fixed number of parties. Although the existing SE schemes under the unshared multi-owner settings can be adapted to this shared scenario, these schemes have to build multiple indexes,which definitely incur higher computational overhead. To save …
Automated Android Application Permission Recommendation, Lingfeng Bao, David Lo, Xin Xia, Shanping Li
Automated Android Application Permission Recommendation, Lingfeng Bao, David Lo, Xin Xia, Shanping Li
Research Collection School Of Computing and Information Systems
The number of Android applications has increased rapidly as Android is becoming the dominant platform in the smartphone market. Security and privacy are key factors for an Android application to be successful. Android provides a permission mechanism to ensure security and privacy. This permission mechanism requires that developers declare the sensitive resources required by their applications. On installation or during runtime, users are required to agree with the permission request. However, in practice, there are numerous popular permission misuses, despite Android introducing official documents stating how to use these permissions properly. Some data mining techniques (e.g., association rule mining) have …
Audiosense: Sound-Based Shopper Behavior Analysis System, Amit Sharma, Youngki Lee
Audiosense: Sound-Based Shopper Behavior Analysis System, Amit Sharma, Youngki Lee
Research Collection School Of Computing and Information Systems
This paper presents AudioSense, the system to monitor user-item interactions inside a store hence enabling precisely customized promotions. A shopper's smartwatch emits sound every time the shopper picks up or touches an item inside a store. This sound is then localized, in 2D space, by calculating the angles of arrival captured by multiple microphones deployed on the racks. Lastly, the 2D location is mapped to specific items on the rack based on the rack layout information. In our initial experiments conducted with a single rack with 16 compartments, we could localize the shopper's smartwatch with a median estimation error of …
Combining Machine-Based And Econometrics Methods For Policy Analytics Insights, Robert J. Kauffman, Kwansoo Kim, Sang-Yong Tom Lee, Ai Phuong Hoang, Jing Ren
Combining Machine-Based And Econometrics Methods For Policy Analytics Insights, Robert J. Kauffman, Kwansoo Kim, Sang-Yong Tom Lee, Ai Phuong Hoang, Jing Ren
Research Collection School Of Computing and Information Systems
Computational Social Science (CSS) has become a mainstream approach in the empirical study of policy analytics issues in various domains of e-commerce research. This article is intended to represent recent advances that have been made for the discovery of new policy-related insights in business, consumer, and social settings. The approach discussed is fusion analytics, which combines machine-based methods from Computer Science (CS) and explanatory empiricism involving advanced Econometrics and Statistics. It explores several efforts to conduct research inquiry in different functional areas of Electronic Commerce and Information Systems (IS), with applications that represent different functional areas of business, as well …
Github And Stack Overflow: Analyzing Developer Interests Across Multiple Social Collaborative Platforms, Ka Wei Roy Lee, David Lo
Github And Stack Overflow: Analyzing Developer Interests Across Multiple Social Collaborative Platforms, Ka Wei Roy Lee, David Lo
Research Collection School Of Computing and Information Systems
Increasingly, software developers are using a wide array of social collaborative platforms for software development and learning. In this work, we examined the similarities in developer’s interests within and across GitHub and Stack Overflow. Our study finds that developers share common interests in GitHub and Stack Overflow; on average, 39% of the GitHub repositories and Stack Overflow questions that a developer had participated fall in the common interests. Also, developers do share similar interests with other developers who co-participated activities in the two platforms. In particular, developers who co-commit and co-pull-request same GitHub repositories and co-answer same Stack Overflow questions, …
Detect Rumors In Microblog Posts Using Propagation Structure Via Kernel Learning, Jing Ma, Wei Gao, Kam-Fai Wong
Detect Rumors In Microblog Posts Using Propagation Structure Via Kernel Learning, Jing Ma, Wei Gao, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted and developed over time. We then propose a kernel-based method called Propagation Tree Kernel, which captures high-order patterns differentiating different types of rumors by evaluating the similarities between their propagation tree structures. Experimental results on two real-world datasets demonstrate …
From Retweet To Believability: Utilizing Trust To Identify Rumor Spreaders On Twitter, Bhavtosh Rath, Wei Gao, Jing Ma, Jaideep Srivastava
From Retweet To Believability: Utilizing Trust To Identify Rumor Spreaders On Twitter, Bhavtosh Rath, Wei Gao, Jing Ma, Jaideep Srivastava
Research Collection School Of Computing and Information Systems
Ubiquitous use of social media such as microblogging platforms brings about ample opportunities for the false information to diffuse online. It is very important not just to determine the veracity of information but also the authenticity of the users who spread the information, especially in time-critical situations like real-world emergencies, where urgent measures have to be taken for stopping the spread of fake information. In this work, we propose a novel machine learning based approach for automatic identification of the users spreading rumorous information by leveraging the concept of believability, i.e., the extent to which the propagated information is likely …
Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Learning To Hallucinate Face Images Via Component Generation And Enhancement, Yibing Song, Jiawei Zhang, Shengfeng He, Linchao Bao, Qingxiong Yang
Research Collection School Of Computing and Information Systems
We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methods.
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Transaction Cost Optimization For Online Portfolio Selection, Bin Li, Jialei Wang, Dingjiang Huang, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
To improve existing online portfolio selection strategies in the case of non-zero transaction costs, we propose a novel framework named Transaction Cost Optimization (TCO). The TCO framework incorporates the L1 norm of the difference between two consecutive allocations together with the principles of maximizing expected log return. We further solve the formulation via convex optimization, and obtain two closed-form portfolio update formulas, which follow the same principle as Proportional Portfolio Rebalancing (PPR) in industry. We empirically evaluate the proposed framework using four commonly used data-sets. Although these data-sets do not consider delisted firms and are thus subject to survival bias, …
Well-Tuned Algorithms For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen, Kun Lu
Well-Tuned Algorithms For The Team Orienteering Problem With Time Windows, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen, Kun Lu
Research Collection School Of Computing and Information Systems
The Team Orienteering Problem with Time Windows (TOPTW) is the extension of the Orienteering Problem (OP) where each node is limited by a predefined time window during which the service has to start. The objective of the TOPTW is to maximize the total collected score by visiting a set of nodes with a limited number of paths. We propose two algorithms, Iterated Local Search and a hybridization of Simulated Annealing and Iterated Local Search (SAILS), to solve the TOPTW. As indicated in multiple research works on algorithms for the OP and its variants, determining appropriate parameter values in a statistical …
Multiplex Media Attention And Disregard Network Among 129 Countries, Haewoon Kwak, Jisun An
Multiplex Media Attention And Disregard Network Among 129 Countries, Haewoon Kwak, Jisun An
Research Collection School Of Computing and Information Systems
We built a multiplex media attention and disregard network (MADN) among 129 countries over 212 days. By characterizing the MADN from multiple levels, we found that it is formed primarily by skewed, hierarchical, and asymmetric relationships. Also, we found strong evidence that our news world is becoming a "global village." However, at the same time, unique attention blocks of the Middle East and North Africa (MENA) region, as well as Russia and its neighbors, still exist.
Encoding And Recall Of Spatio-Temporal Episodic Memory In Real Time, Poo-Hee Chang, Ah-Hwee Tan
Encoding And Recall Of Spatio-Temporal Episodic Memory In Real Time, Poo-Hee Chang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Episodic memory enables a cognitive system to improve its performance by reflecting upon past events. In this paper, we propose a computational model called STEM for encoding and recall of episodic events together with the associated contextual information in real time. Based on a class of self-organizing neural networks, STEM is designed to learn memory chunks or cognitive nodes, each encoding a set of co-occurring multi-modal activity patterns across multiple pattern channels. We present algorithms for recall of events based on partial and inexact input patterns. Our empirical results based on a public domain data set show that STEM displays …
Formresnet: Formatted Residual Learning For Image Restoration, Jianbo Jiao, Wei-Chih Tu, Shengfeng He
Formresnet: Formatted Residual Learning For Image Restoration, Jianbo Jiao, Wei-Chih Tu, Shengfeng He
Research Collection School Of Computing and Information Systems
In this paper, we propose a deep CNN to tackle the image restoration problem by learning the structured residual. Previous deep learning based methods directly learn the mapping from corrupted images to clean images, and may suffer from the gradient exploding/vanishing problems of deep neural networks. We propose to address the image restoration problem by learning the structured details and recovering the latent clean image together, from the shared information between the corrupted image and the latent image. In addition, instead of learning the pure difference (corruption), we propose to add a 'residual formatting layer' to format the residual to …
Don’T Bury Your Head In Warnings: A Game-Theoretic Approach For Intelligent Allocation Of Cyber-Security Alerts, Aaron Schlenker, Haifeng Xu, Mina Guirguis, Christopher Kiekintveld, Arunesh Sinha, Milind Tambe, Solomon Sonya, Darryl Balderas, Noah Dunstatter
Don’T Bury Your Head In Warnings: A Game-Theoretic Approach For Intelligent Allocation Of Cyber-Security Alerts, Aaron Schlenker, Haifeng Xu, Mina Guirguis, Christopher Kiekintveld, Arunesh Sinha, Milind Tambe, Solomon Sonya, Darryl Balderas, Noah Dunstatter
Research Collection School Of Computing and Information Systems
In recent years, there have been a number of successful cyber attacks on enterprise networks by malicious actors which have caused severe damage. These networks have Intrusion Detection and Prevention Systems in place to protect them, but they are notorious for producing a high volume of alerts. These alerts must be investigated by cyber analysts to determine whether they are an attack or benign. Unfortunately, there are magnitude more alerts generated than there are cyber analysts to investigate them. This trend is expected to continue into the future creating a need for tools which find optimal assignments of the incoming …
The Simpler The Better: A Unified Approach To Predicting Original Taxi Demands On Large-Scale Online Platforms, Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv
The Simpler The Better: A Unified Approach To Predicting Original Taxi Demands On Large-Scale Online Platforms, Yongxin Tong, Yuqiang Chen, Zimu Zhou, Lei Chen, Jie Wang, Qiang Yang, Jieping Ye, Weifeng Lv
Research Collection School Of Computing and Information Systems
No abstract provided.
Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang
Measuring Fine-Grained Metro Interchange Time Via Smartphones, Weixi Gu, Kai Zhang, Zimu Zhou, Ming Jin, Yuxun Zhou, Xi Liu, Costas J. Spanos, Zuo-Jun (Max) Shen, Wei-Hua Lin, Lin Zhang
Research Collection School Of Computing and Information Systems
High variability interchange times often significantly affect the reliability of metro travels. Fine-grained measurements of interchange times during metro transfers can provide valuable insights on the crowdedness of stations, usage of station facilities and efficiency of metro lines. Measuring interchange times in metro systems is challenging since agentoperated systems like automatic fare collection systems only provide coarse-grained trip information and popular localization services like GPS are often inaccessible underground. In this paper, we propose a smartphone-based interchange time measuring method from the passengers’ perspective. It leverages low-power sensors embedded in modern smartphones to record ambient contextual features, and utilizes a …
Sequence Aware Functional Encryption And Its Application In Searchable Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Fuchun Guo, Qiong Huang
Sequence Aware Functional Encryption And Its Application In Searchable Encryption, Tran Viet Xuan Phuong, Guomin Yang, Willy Susilo, Fuchun Guo, Qiong Huang
Research Collection School Of Computing and Information Systems
As a new broad vision of public-key encryption systems, functional encryption provides a promising solution for many challenging security problems such as expressive access control and searching on encrypted data. In this paper, we present two Sequence Aware Function Encryption (SAFE) schemes. Such a scheme is very useful in many forensics applications where the order (or pattern) of the attributes forms an important characteristic of an attribute sequence. Our first scheme supports the matching of two bit strings, while the second scheme can support the matching of general characters. These two schemes are constructed based on the standard Decision Linear …
Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu
Optimal Security Reductions For Unique Signatures: Bypassing Impossibilities With A Counterexample, Fuchun Fuo, Rongmao Chen, Willy Susilo, Jianchang Lai, Guomin Yang, Yi Mu
Research Collection School Of Computing and Information Systems
Optimal security reductions for unique signatures (Coron, Eurocrypt 2002) and their generalization, i.e., efficiently re-randomizable signatures (Hofheinz et al. PKC 2012 & Bader et al. Eurocrypt 2016) have been well studied in the literature. Particularly, it has been shown that under a non-interactive hard assumption, any security reduction (with or without random oracles) for a unique signature scheme or an efficiently re-randomizable signature scheme must loose a factor of at least qsqs in the security model of existential unforgeability against chosen-message attacks (EU-CMA), where qsqs denotes the number of signature queries. Note that the number qsqs can be as large …
Bridge Text And Knowledge By Learning Multi-Prototype Entity Mention Embedding, Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li
Bridge Text And Knowledge By Learning Multi-Prototype Entity Mention Embedding, Yixin Cao, Lifu Huang, Heng Ji, Xu Chen, Juanzi Li
Research Collection School Of Computing and Information Systems
Integrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention phrase usually refers to various entities. In this paper, to deal with the ambiguity of entity mentions, we propose a novel Multi-Prototype Mention Embedding model, which learns multiple sense embeddings for each mention by jointly modeling words from textual contexts and entities derived from a knowledge base. In addition, we further design an efficient language model based approach to disambiguate each mention to a specific sense. In experiments, both qualitative …
Representativeness-Aware Aspect Analysis For Brand Monitoring In Social Media, Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Ta-Seng Chua
Representativeness-Aware Aspect Analysis For Brand Monitoring In Social Media, Lizi Liao, Xiangnan He, Zhaochun Ren, Liqiang Nie, Huan Xu, Ta-Seng Chua
Research Collection School Of Computing and Information Systems
Owing to the fast-responding nature and extreme success of social media, many companies resort to social media sites for monitoring their brands’ reputation and the opinions of general public. To help companies monitor their brands, in this work, we delve into the task of extracting representative aspects and posts from users’ free-text posts in social media. Previous efforts have treated it as a traditional information extraction task, and forgo the specific properties of social media, such as the possible noise in user generated posts and the varying impacts; In contrast, we extract aspects by maximizing their representativeness, which is a …
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Time-Aware Conversion Prediction, Wendi Ji, Xiaoling Wang, Feida Zhu
Research Collection School Of Computing and Information Systems
The importance of product recommendation has been well recognized as a central task in business intelligence for e-commerce websites. Interestingly, what has been less aware of is the fact that different products take different time periods for conversion. The “conversion” here refers to actually a more general set of pre-defined actions, including for example purchases or registrations in recommendation and advertising systems. The mismatch between the product’s actual conversion period and the application’s target conversion period has been the subtle culprit compromising many existing recommendation algorithms.The challenging question: what products should be recommended for a given time period to maximize …
Will This Localization Tool Be Effective For This Bug? Mitigating The Impact Of Unreliability Of Information Retrieval Based Bug Localization Tools, Tien-Duy B. Le, Ferdian Thung, David Lo
Will This Localization Tool Be Effective For This Bug? Mitigating The Impact Of Unreliability Of Information Retrieval Based Bug Localization Tools, Tien-Duy B. Le, Ferdian Thung, David Lo
Research Collection School Of Computing and Information Systems
Information retrieval (IR) based bug localization approaches process a textual bug report and a collection of source code files to find buggy files. They output a ranked list of files sorted by their likelihood to contain the bug. Recently, several IR-based bug localization tools have been proposed. However, there are no perfect tools that can successfully localize faults within a few number of most suspicious program elements for every single input bug report. Therefore, it is difficult for developers to decide which tool would be effective for a given bug report. Furthermore, for some bug reports, no bug localization tools …
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Indexing Metric Uncertain Data For Range Queries And Range Joins, Lu Chen, Yunjun Gao, Aoxiao Zhong, Christian S. Jensen, Gang Chen, Baihua Zheng
Research Collection School Of Computing and Information Systems
Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B+-tree (UPB-tree) and the uncertain pivot B+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set …