Computer Sciences Commons^™

Efficient Filters For Geometric Intersection Computations Using Gpu, Yiming Liu, Satish Puri

Computer Science Faculty Research and Publications

Geometric intersection algorithms are fundamental in spatial analysis in Geographic Information System (GIS). Applying high performance computing to perform geometric intersection on huge amount of spatial data to get real-time results is necessary. Given two input geometries (polygon or polyline) of a candidate pair, we introduce a new two-step geospatial filter that first creates sketches of the geometries and uses it to detect workload and then refines the sketches by the common areas of sketches to decrease the overall computations in the refine phase. We call this filter PolySketch-based CMBR (PSCMBR) filter. We show the application of this filter in …

Beyond Accuracy: Assessing Software Documentation Quality, Christoph Treude, Justin Middleton, Thushari Atapattu

Research Collection School Of Computing and Information Systems

Good software documentation encourages good software engineering, but the meaning of “good” documentation is vaguely defined in the software engineering literature. To clarify this ambiguity, we draw on work from the data and information quality community to propose a framework that decomposes documentation quality into ten dimensions of structure, content, and style. To demonstrate its application, we recruited technical editors to apply the framework when evaluating examples from several genres of software documentation. We summarise their assessments—for example, reference documentation and README files excel in quality whereas blog articles have more problems—and we describe our vision for reasoning about software …

Capitalising Product Associations In A Supermarket Retail Environment, Michelle L. F. Cheong, Yong Qing Chia

Research Collection School Of Computing and Information Systems

This paper explores methods to capitalise on retail companies’ transactional databases, to mine meaningful product associations, and to design product placement strategies as a means to drive sales. We implemented three in-store initiatives based on our hypotheses – placing products with high associations together will induce an increase in sales of consequent; introducing an antecedent that is new to store will bring about a similar impact on sales of consequent based on established product association rules uncovered from other stores. Sales tracking over twelve weeks revealed that there were improvements in sales of consequents across all three initiatives performed in-store.

Exploring And Evaluating Attributes, Values, And Structures For Entity Alignment, Zhiyuan Liu, Yixin Cao, Liangming Pan, Juanzi Li, Zhiyuan Liu, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling the KG structure defined by relation triples. However, attribute triples can also provide crucial alignment signal but have not been well explored yet. In this paper, we propose to utilize an attributed value encoder and partition the KG into subgraphs to model the various types of attribute triples efficiently. Besides, the performances of current EA methods are overestimated because of the name-bias of existing EA datasets. To make an objective …

Machine Learning Integrated Design For Additive Manufacturing, Jingchao Jiang, Yi Xiong, Zhiyuan Zhang, David W. Rosen

Research Collection School Of Computing and Information Systems

For improving manufacturing efficiency and minimizing costs, design for additive manufacturing (AM) has been accordingly proposed. The existing design for AM methods are mainly surrogate model based. Due to the increasingly available data nowadays, machine learning (ML) has been applied to medical diagnosis, image processing, prediction, classification, learning association, etc. A variety of studies have also been carried out to use machine learning for optimizing the process parameters of AM with corresponding objectives. In this paper, a ML integrated design for AM framework is proposed, which takes advantage of ML that can learn the complex relationships between the design and …

Efficient And Fair Data Valuation For Horizontal Federated Learning, Shuyue Wei, Yongxin Tong, Zimu Zhou, Tianshu Song

Research Collection School Of Computing and Information Systems

Availability of big data is crucial for modern machine learning applications and services. Federated learning is an emerging paradigm to unite different data owners for machine learning on massive data sets without worrying about data privacy. Yet data owners may still be reluctant to contribute unless their data sets are fairly valuated and paid. In this work, we adapt Shapley value, a widely used data valuation metric to valuating data providers in federated learning. Prior data valuation schemes for machine learning incur high computation cost because they require training of extra models on all data set combinations. For efficient data …

Erica: Enabling Real-Time Mistake Detection And Corrective Feedback For Free-Weights Exercises, Meeralakshmi Radhakrishnan, Darshana Rathnayake, Koon Han Ong, Inseok Hwang, Archan Misra

Research Collection School Of Computing and Information Systems

We present ERICA, a digital personal trainer for users performing free weights exercises, with two key differentiators: (a) First, unlike prior approaches that either require multiple on-body wearables or specialized infrastructural sensing, ERICA uses a single in-ear "earable" device (piggybacking on a form factor routinely used by millions of gym-goers) and a simple inertial sensor mounted on each weight equipment; (b) Second, unlike prior work that focuses primarily on quantifying a workout, ERICA additionally identifies a variety of fine-grained exercising mistakes and delivers real-time, in-situ corrective instructions. To achieve this, we (a) design a robust approach for user-equipment association that …

A Secure Flexible And Tampering-Resistant Data Sharing System For Vehicular Social Networks, Jianfei Sun, Hu Xiong, Shufan Zhang, Ximeng Liu, Jiaming Yuan, Robert H. Deng

Research Collection School Of Computing and Information Systems

Vehicular social networks (VSNs) have emerged as the promising paradigm of vehicular networks that can improve traffic safety, relieve traffic congestion and even provide comprehensive social services by sharing vehicular sensory data. To selectively share the sensory data with other vehicles in the vicinity and reduce the local storage burden of vehicles, the vehicular sensory data are usually outsourced to vehicle cloud server for sharing and searching. However, existing data sharing systems for VSNs can neither provide secure selective one-to-many data sharing and verifiable data retrieval over encrypted data nor ensure that the integrity of retrieved data. In this paper, …

A Survey Of Typical Attributed Graph Queries, Yanhao Wang, Yuchen Li, Ju Fan, Chang Ye, Mingke Chai

Research Collection School Of Computing and Information Systems

Graphs are commonly used for representing complex structures such as social relationships, biological interactions, and knowledge bases. In many scenarios, graphs not only represent topological relationships but also store the attributes that denote the semantics associated with their vertices and edges, known as attributed graphs. Attributed graphs can meet demands for a wide range of applications, and thus a variety of queries on attributed graphs have been proposed. However, these diverse types of attributed graph queries have not been systematically investigated yet. In this paper, we provide an extensive survey of several typical types of attributed graph queries. We propose …

Effort-Aware Just-In-Time Defect Identification In Practice: A Case Study At Alibaba, Meng Yan, Xin Xia, Yuanrui Fan, David Lo, Ahmed E. Hassan, Xindong Zhang

Research Collection School Of Computing and Information Systems

Effort-aware Just-in-Time (JIT) defect identification aims at identifying defect-introducing changes just-in-time with limited code inspection effort. Such identification has two benefits compared with traditional module-level defect identification, i.e., identifying defects in a more cost-effective and efficient manner. Recently, researchers have proposed various effort-aware JIT defect identification approaches, including supervised (e.g., CBS+, OneWay) and unsupervised approaches (e.g., LT and Code Churn). The comparison of the effectiveness between such supervised and unsupervised approaches has attracted a large amount of research interest. However, the effectiveness of the recently proposed approaches and the comparison among them have never been investigated in an industrial setting.In …

Deep-Learning-Based App Sensitive Behavior Surveillance For Android Powered Cyber-Physical Systems, Haoyu Ma, Jianwen Tian, Kefan Qiu, David Lo, Debin Gao, Daoyuan Wu, Chunfu Jia, Thar Baker

Research Collection School Of Computing and Information Systems

Android as an operating system is now increasingly being adopted in industrial information systems, especially with Cyber-Physical Systems (CPS). This also puts Android devices onto the front line of handling security-related data and conducting sensitive behaviors, which could be misused by the increasing number of polymorphic and metamorphic malicous applications targeting the platform. The existence of such malware threats therefore call for more accurate identification and surveillance of sensitive Android app behaviors, which is essential to the security of CPS and IoT devices powered by Android. Nevertheless, achieving dynamic app behavior monitoring and identification on real CPS powered by Android …

Uniconv: A Unified Conversational Neural Architecture For Multi-Domain Task-Oriented Dialogues, Hung Le, Doyen Sahoo, Chenghao Liu, Nancy F. Chen, Steven C. H. Hoi

Research Collection School Of Computing and Information Systems

Building an end-to-end conversational agent for multi-domain task-oriented dialogues has been an open challenge for two main reasons. First, tracking dialogue states of multiple domains is non-trivial as the dialogue agent must obtain complete states from all relevant domains, some of which might have shared slots among domains as well as unique slots specifically for one domain only. Second, the dialogue agent must also process various types of information across domains, including dialogue context, dialogue states, and database, to generate natural responses to users. Unlike the existing approaches that are often designed to train each module separately, we propose “UniConv" …

Answerfact: Fact Checking In Product Question Answering, Wenxuan Zhang, Yang Deng, Jing Ma, Wai Lam

Research Collection School Of Computing and Information Systems

Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping. However, the misinformation in the answers on those platforms poses unprecedented challenges for users to obtain reliable and truthful product information, which may even cause a commercial loss in E-commerce business. To tackle this issue, we investigate to predict the veracity of answers in this paper and introduce AnswerFact, a large scale fact checking dataset from product question answering forums. Each answer is accompanied by its veracity label and associated evidence sentences, providing a …

Api Method Recommendation Via Explicit Matching Of Functionality Verb Phrases, Wenkai Xie, Xin Peng, Mingwei Liu, Christoph Treude, Zhenchang Xing, Xiaoxin Zhang, Wenyun Zhao

Research Collection School Of Computing and Information Systems

Due to the lexical gap between functionality descriptions and user queries, documentation-based API retrieval often produces poor results. Verb phrases and their phrase patterns are essential in both describing API functionalities and interpreting user queries. Thus we hypothesize that API retrieval can be facilitated by explicitly recognizing and matching between the fine-grained structures of functionality descriptions and user queries. To verify this hypothesis, we conducted a large-scale empirical study on the functionality descriptions of 14,733 JDK and Android API methods. We identified 356 different functionality verbs from the descriptions, which were grouped into 87 functionality categories, and we extracted 523 …

Multi-User Verifiable Searchable Symmetric Encryption For Cloud Storage, Xueqiao Liu, Guomin Yang, Guomin Yang

Research Collection School Of Computing and Information Systems

In a cloud data storage system, symmetric key encryption is usually used to encrypt files due to its high efficiency. In order allow the untrusted/semi-trusted cloud storage server to perform searching over encrypted data while maintaining data confidentiality, searchable symmetric encryption (SSE) has been proposed. In a typical SSE scheme, a users stores encrypted files on a cloud storage server and later can retrieve the encrypted files containing specific keywords. The basic security requirement of SSE is that the cloud server learns no information about the files or the keywords during the searching process. Some SSE schemes also offer additional …

A Tripartite Model Of Trust In Facebook: Acceptance Of Information Personalization, Privacy Concern, And Privacy Literacy, Sonny Rosenthal, Ole-Christian Wasenden, Gorm-Andreas Gronnevet, Rich Ling

Research Collection College of Integrative Studies

This study draws on the mental accounting perspective and a tripartite model of trust to explain why users trust Facebook. We argue that trust in Facebook is related to (1) trust in companies that collect personal data, (2) acceptance of information personalization, (3) low privacy concern, and (4) low privacy literacy. Further, we argue that privacy literacy amplifies the relationship between privacy concern and the other factors. This is because, among individuals with high privacy literacy, privacy concern is especially diagnostic of the potential harms of a loss of privacy. These arguments align broadly with theorizations about factors influencing privacy-related …

Bugsinpy: A Database Of Existing Bugs In Python Programs To Enable Controlled Testing And Debugging Studies, Ratnadira Widyasari, Sheng Qin Sim, Camellia Lok, Haodi Qi, Jack Phan, Qijin Tay, Constance Tan, Fiona Wee, Jodie Ethelda Tan, Yuheng Yieh, Brian Goh, Ferdian Thung, Hong Jin Kang, Thong Hoang, David Lo, Eng Lieh Ouh

Research Collection School Of Computing and Information Systems

The 2019 edition of Stack Overflow developer survey highlights that, for the first time, Python outperformed Java in terms of popularity. The gap between Python and Java further widened in the 2020 edition of the survey. Unfortunately, despite the rapid increase in Python's popularity, there are not many testing and debugging tools that are designed for Python. This is in stark contrast with the abundance of testing and debugging tools for Java. Thus, there is a need to push research on tools that can help Python developers.One factor that contributed to the rapid growth of Java testing and debugging tools …

Deepcommenter: A Deep Code Comment Generation Tool With Hybrid Lexical And Syntactical Information, Boao Li, Meng Yan, Xin Xia, Xing Hu, Ge Li, David Lo

Research Collection School Of Computing and Information Systems

As the scale of software projects increases, the code comments are more and more important for program comprehension. Unfortunately, many code comments are missing, mismatched or outdated due to tight development schedule or other reasons. Automatic code comment generation is of great help for developers to comprehend source code and reduce their workload. Thus, we propose a code comment generation tool (DeepCommenter) to generate descriptive comments for Java methods. DeepCommenter formulates the comment generation task as a machine translation problem and exploits a deep neural network that combines the lexical and structural information of Java methods. We implement DeepCommenter in …

Contextual-Bandit Anomaly Detection For Iot Data In Distributed Hierarchical Edge Computing, Mao V. Ngo, Tie Luo, Hakima Chaouchi, Tony Q.S. Quek

Computer Science Faculty Research & Creative Works

Advances in deep neural networks (DNN) greatly bolster real-time detection of anomalous IoT data. However, IoT devices can hardly afford complex DNN models, and offloading anomaly detection tasks to the cloud incurs long delay. In this paper, we propose and build a demo for an adaptive anomaly detection approach for distributed hierarchical edge computing (HEC) systems to solve this problem, for both univariate and multivariate IoT data. First, we construct multiple anomaly detection DNN models with increasing complexity and associate each model with a layer in HEC from bottom to top. Then, we design an adaptive scheme to select one …

Law Library Blog (November 2020): Legal Beagle's Blog Archive, Roger Williams University School Of Law

Law Library Newsletters/Blog

No abstract provided.

A Geospatial Analytics Approach To Delineate Trade Areas For Quick Service Restaurants (Qsr) In Singapore, Hui Ting Lim

Research Collection School Of Computing and Information Systems

According to Huff, trade area is defined as “a geographically delineated region containing potential customers for whom there exists a probability greater than zero of their purchasing a given class of products or services offered for sale by a particular firm or by a particular agglomeration of firms”. Several methods to delineate a store trade area have been proposed over the years. For drive-time or travel distance analysis method, the trade area is delineated according to how far or how long the customers are willing to travel to patronise the store. Another commonly used method is the Huff Model which …

Coinwatch: A Clone-Based Approach For Detecting Vulnerabilities In Cryptocurrencies, Qingze Hum, Wei Jin Tan, Shi Ying Tey, Latasha Lenus, Ivan Homoliak, Yun Lin, Jun Sun

Research Collection School Of Computing and Information Systems

Cryptocurrencies have become very popular in recent years. Thousands of new cryptocurrencies have emerged, proposing new and novel techniques that improve on Bitcoin's core innovation of the blockchain data structure and consensus mechanism. However, cryptocurrencies are a major target for cyber-attacks, as they can be sold on exchanges anonymously and most cryptocurrencies have their codebases publicly available. One particular issue is the prevalence of code clones in cryptocurrencies, which may amplify security threats. If a vulnerability is found in one cryptocurrency, it might be propagated into other cloned cryptocurrencies. In this work, we propose a systematic remedy to this problem, …

Cache-Based Optimization For Block Commit Of Hyperledger Fabric, Qinqi Xu, Yimin Lin, Qingshan Jiang, Mengqiu Zhang

Research Collection Lee Kong Chian School Of Business

Blockchain technology has been applied to various domains in recent years, and Hyperledger Fabric is one of the most popular blockchain systems. This paper conducts a fine analysis on the block commit of Hyperledger Fabric through latency evaluation as well as flame graph. To overcome the bottlenecks pointed out by the analysts, this paper proposes two cache-based schemata. In particular, we add a cache for index and state storage to accelerate commit. We also cache deserialized block globally for block delivery to avoid repeat operations. The experiments reveal performance improvement of 2×. Notably, our optimizations can also be implemented easily …

A Theory Of The Engagement In Open Source Projects Via Summer Of Code Programs, Jefferson Silva, Igor Wiese, Daniel M. German, Christoph Treude, Marco A. Gerosa, Igor Steinmacher

Research Collection School Of Computing and Information Systems

Summer of code programs connect students to open source software (OSS) projects, typically during the summer break from school. Analyzing consolidated summer of code programs can reveal how college students, who these programs usually target, can be motivated to participate in OSS, and what onboarding strategies OSS communities adopt to receive these students. In this paper, we study the well-established Google Summer of Code (GSoC) and devise an integrated engagement theory grounded in multiple data sources to explain motivation and onboarding in this context. Our analysis shows that OSS communities employ several strategies for planning and executing student participation, socially …

Deepfrag-K: A Fragment-Based Deep Learning Approach For Protein Fold Recognition, Wessam Elhefnawy, Min Li, Jianxin Wang, Yaohang Li

Computer Science Faculty Publications

Background: One of the most essential problems in structural bioinformatics is protein fold recognition. In this paper, we design a novel deep learning architecture, so-called DeepFrag-k, which identifies fold discriminative features at fragment level to improve the accuracy of protein fold recognition. DeepFrag-k is composed of two stages: the first stage employs a multi-modal Deep Belief Network (DBN) to predict the potential structural fragments given a sequence, represented as a fragment vector, and then the second stage uses a deep convolutional neural network (CNN) to classify the fragment vector into the corresponding fold.

Results: Our results show that DeepFrag-k yields …

Perceptions, Expectations, And Challenges In Defect Prediction, Zhiyuan Wan, Xin Xia, Ahmed E. Hassan, David Lo, Jianwei Yin, Xiaohu Yang

Research Collection School Of Computing and Information Systems

Defect prediction has been an active research area for over four decades. Despite numerous studies on defect prediction, the potential value of defect prediction in practice remains unclear. To address this issue, we performed a mixed qualitative and quantitative study to investigate what practitioners think, behave and expect in contrast to research findings when it comes to defect prediction. We collected hypotheses from open-ended interviews and a literature review, followed by a validation survey. We received 395 responses from practitioners. Some of our key findings include: 1) Over 90% of respondents are willing to adopt defect prediction techniques. 2) There …

Sfuzz: An Efficient Adaptive Fuzzer For Solidity Smart Contracts, Tai D. Nguyen, Long H. Pham, Jun Sun, Yun Lin, Minh Quang Tran

Research Collection School Of Computing and Information Systems

Smart contracts are Turing-complete programs that execute on the infrastructure of the blockchain, which often manage valuable digital assets. Solidity is one of the most popular programming languages for writing smart contracts on the Ethereum platform. Like traditional programs, smart contracts may contain vulnerabilities. Unlike traditional programs, smart contracts cannot be easily patched once they are deployed. It is thus important that smart contracts are tested thoroughly before deployment. In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz. Compared to existing Solidity fuzzers, sFuzz combines the strategy in the AFL fuzzer and …

Exploring The Impact Of Covid-19 On Aviation Industry: A Text Mining Approach, Gottipati Swapna, Kyong Jin Shim, Weiling Angeline Jiang, Sheng Wei Andre Justin Lee

Research Collection School Of Computing and Information Systems

Our study presents a comprehensive analysis of news articles from FlightGlobal website during the first half of 2020. Our analyses reveal useful insights on themes and trends concerning the aviation industry during the COVID-19 period. We applied text mining and NLP techniques to analyse the articles for extracting the aviation themes and article sentiments (positive and negative). Our results show that there is a variation in the sentiment trends for themes aligned with the real-world developments of the pandemic. The article sentiment analysis can offer industry players a quick sense of the nature of developments in the industry. Our article …

Jito: A Tool For Just-In-Time Defect Identification And Localization, Fangcheng Qiu, Meng Yan, Xin Xia, Xinyu Wang, Yuanrui Fan, Ahmed E. Hassan, David Lo

Research Collection School Of Computing and Information Systems

In software development and maintenance, defect localization is necessary for software quality assurance. Current defect localization techniques mainly rely on defect symptoms (e.g., bug reports or program spectrum) when the defect has been exposed. One challenge task is: can we locate buggy program prior to the appearance of the defect symptom. Such kind of localization is conducted at an early stage (e.g., when buggy program elements are being checkedin) which can be an early step of continuous quality control.In this paper, we propose a Just-In-Time defect identification and lOcalization tool, named JITO, which can help developers to locate defective lines …

Cross-Thought For Sentence Encoder Pre-Training, Shuohang Wang, Yuwei Fang, Siqi Sun, Zhe Gan, Yu Cheng, Jingjing Liu, Jing Jiang

Research Collection School Of Computing and Information Systems

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also …