Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (8549)
- Computer Sciences (8532)
- Databases and Information Systems (3451)
- Software Engineering (2164)
- Artificial Intelligence and Robotics (1697)
-
- Information Security (1067)
- Numerical Analysis and Scientific Computing (1024)
- Engineering (956)
- Graphics and Human Computer Interfaces (933)
- Social and Behavioral Sciences (734)
- Business (686)
- Computer Engineering (512)
- Theory and Algorithms (493)
- Operations Research, Systems Engineering and Industrial Engineering (417)
- Programming Languages and Compilers (379)
- OS and Networks (323)
- Communication (310)
- Social Media (248)
- Public Affairs, Public Policy and Public Administration (227)
- Transportation (204)
- Medicine and Health Sciences (186)
- Education (180)
- Management Information Systems (178)
- Data Storage Systems (176)
- E-Commerce (151)
- International and Area Studies (125)
- Asian Studies (124)
- Digital Communications and Networking (123)
- Technology and Innovation (114)
- Health Information Technology (113)
- Keyword
-
- Machine learning (125)
- Deep learning (124)
- Social media (80)
- Artificial intelligence (72)
- Reinforcement learning (69)
-
- Data mining (65)
- Privacy (65)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (55)
- Optimization (55)
- Security (54)
- Singapore (52)
- Visualization (52)
- Task analysis (50)
- Twitter (50)
- Neural networks (49)
- Online learning (49)
- Software engineering (49)
- Training (49)
- Anomaly detection (47)
- Feature extraction (46)
- Collaboration (45)
- Large Language Models (45)
- Blockchain (44)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Natural language processing (39)
- Publication Year
- File Type
Articles 2521 - 2550 of 8722
Full-Text Articles in Entire DC Network
Just-In-Time Defect Identification And Localization: A Two-Phase Framework, Meng Yan, Xin Xia, Yuanrui Fan, Ahmed E. Hassan, David Lo, Shanping Li
Just-In-Time Defect Identification And Localization: A Two-Phase Framework, Meng Yan, Xin Xia, Yuanrui Fan, Ahmed E. Hassan, David Lo, Shanping Li
Research Collection School Of Computing and Information Systems
Defect localization aims to locate buggy program elements (e.g., buggy files, methods or lines of code) based on defect symptoms, e.g., bug reports or program spectrum. However, when we receive the defect symptoms, the defect has been exposed and negative impacts have been introduced. Thus, one challenging task is: whether we can locate buggy program prior to appearance of the defect symptom at an early time (e.g., when buggy program elements are being checked-in). We refer to this type of defect localization as “Just-In-Time (JIT) Defect localization”. Although many prior studies have proposed various JIT defect identification methods to identify …
Action-Centric Relation Transformer Network For Video Question Answering, Jipeng Zhang, Jie Shao, Rui Cao, Lianli Gao, Xing Xu, Heng Tao Shen
Action-Centric Relation Transformer Network For Video Question Answering, Jipeng Zhang, Jie Shao, Rui Cao, Lianli Gao, Xing Xu, Heng Tao Shen
Research Collection School Of Computing and Information Systems
Video question answering (VideoQA) has emerged as a popular research topic in recent years. Enormous efforts have been devoted to developing more effective fusion strategies and better intra-modal feature preparation. To explore these issues further, we identify two key problems. (1) Current works take almost no account of introducing action of interest in video representation. Additionally, there exists insufficient labeling data on where the action of interest is in many datasets. However, questions in VideoQA are usually action-centric. (2) Frame-to-frame relations, which can provide useful temporal attributes (e.g., state transition, action counting), lack relevant research. Based on these observations, we …
Journey With Ting Peng Liang In Pacific Asia Information Systems Field, J. Lee, J. Jiang, P. Chau, A. Kankanhalli, M. Nelson, W. Huang, K. Wei, C. Sia, D. Vogel, M. Myers, G. Gable, N. Shen, C. Wei, S. Hung, Keng Siau, D. Liang
Journey With Ting Peng Liang In Pacific Asia Information Systems Field, J. Lee, J. Jiang, P. Chau, A. Kankanhalli, M. Nelson, W. Huang, K. Wei, C. Sia, D. Vogel, M. Myers, G. Gable, N. Shen, C. Wei, S. Hung, Keng Siau, D. Liang
Research Collection School Of Computing and Information Systems
Our respectful old friend Professor Ting-Peng Liang (in short, TP) whom we loved suddenly passed away on May 20, 2021. But we cannot forget his smile and passion, and his inerasable footprints in PACIS, PAJAIS, and AIS Community. He was the founder of PACIS, founding editor-in-chief of PAJAIS, and past president of AIS to list just a few. He was the pioneer who received the first AIS Fellow and the first LEO Award from Asia Pacific. That is why the leaders of the information systems field organized the first ever special tribute session in PACIS 2021 in memory of TP
Accessibility In Software Practice: A Practitioner's Perspective, Tingting Bi, Xin Xia, David Lo, John C. Grundy, Thomas Zimmermann, Denae Ford
Accessibility In Software Practice: A Practitioner's Perspective, Tingting Bi, Xin Xia, David Lo, John C. Grundy, Thomas Zimmermann, Denae Ford
Research Collection School Of Computing and Information Systems
Being able to access software in daily life is vital for everyone, and thus accessibility is a fundamental challenge for software development. However, given the number of accessibility issues reported by many users, e.g., in app reviews, it is not clear if accessibility is widely integrated into current software projects and how software projects address accessibility issues. In this article, we report a study of the critical challenges and benefits of incorporating accessibility into software development and design. We applied a mixed qualitative and quantitative approach for gathering data from 15 interviews and 365 survey respondents from 26 countries across …
Codematcher: Searching Code Based On Sequential Semantics Of Important Query Words, Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E. Hassan, Shanping Li
Codematcher: Searching Code Based On Sequential Semantics Of Important Query Words, Chao Liu, Xin Xia, David Lo, Zhiwei Liu, Ahmed E. Hassan, Shanping Li
Research Collection School Of Computing and Information Systems
To accelerate software development, developers frequently search and reuse existing code snippets from a large-scale codebase, e.g., GitHub. Over the years, researchers proposed many information retrieval (IR)-based models for code search, but they fail to connect the semantic gap between query and code. An early successful deep learning (DL)-based model DeepCS solved this issue by learning the relationship between pairs of code methods and corresponding natural language descriptions. Two major advantages of DeepCS are the capability of understanding irrelevant/noisy keywords and capturing sequential relationships between words in query and code. In this article, we proposed an IR-based model CodeMatcher that …
Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy
Why Do Smart Contracts Self-Destruct? Investigating The Selfdestruct Function On Ethereum, Jiachi Chen, Xin Xia, David Lo, John C. Grundy
Research Collection School Of Computing and Information Systems
The selfdestruct function is provided by Ethereum smart contracts to destroy a contract on the blockchain system. However, it is a double-edged sword for developers. On the one hand, using the selfdestruct function enables developers to remove smart contracts (SCs) from Ethereum and transfers Ethers when emergency situations happen, e.g., being attacked. On the other hand, this function can increase the complexity for the development and open an attack vector for attackers. To better understand the reasons why SC developers include or exclude the selfdestruct function in their contracts, we conducted an online survey to collect feedback from them and …
Toward Video-Conferencing Tools For Hands-On Activities In Online Teaching, Audrey Labrie, Terrance Mok, Anthony Tang, Michelle Lui, Lora Oehlberg, Lev Poretski
Toward Video-Conferencing Tools For Hands-On Activities In Online Teaching, Audrey Labrie, Terrance Mok, Anthony Tang, Michelle Lui, Lora Oehlberg, Lev Poretski
Research Collection School Of Computing and Information Systems
Many instructors in computing and HCI disciplines use hands-on activities for teaching and training new skills. Beyond simply teaching hands-on skills like sketching and programming, instructors also use these activities so students can acquire tacit skills. Yet, current video-conferencing technologies may not effectively support hands-on activities in online teaching contexts. To develop an understanding of the inadequacies of current video-conferencing technologies for hands-on activities, we conducted 15 interviews with university-level instructors who had quickly pivoted their use of hands-on activities to an online context during the early part of the COVID-19 pandemic. Based on our analysis, we uncovered four pedagogical …
"More Than Deep Learning": Post-Processing For Api Sequence Recommendation, Chi Chen, Xin Peng, Bihuan Chen, Jun Sun, Zhenchang Xing, Xin Wang, Wenyun Zhao
"More Than Deep Learning": Post-Processing For Api Sequence Recommendation, Chi Chen, Xin Peng, Bihuan Chen, Jun Sun, Zhenchang Xing, Xin Wang, Wenyun Zhao
Research Collection School Of Computing and Information Systems
In the daily development process, developers often need assistance in finding a sequence of APIs to accomplish their development tasks. Existing deep learning models, which have recently been developed for recommending one single API, can be adapted by using encoder-decoder models together with beam search to generate API sequence recommendations. However, the generated API sequence recommendations heavily rely on the probabilities of API suggestions at each decoding step, which do not take into account other domain-specific factors (e.g., whether an API suggestion satisfies the program syntax and how diverse the API sequence recommendations are). Moreover, it is difficult for developers …
Kg4vis: A Knowledge Graph-Based Approach For Visualization Recommendation, Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, Huamin. Qu
Kg4vis: A Knowledge Graph-Based Approach For Visualization Recommendation, Haotian Li, Yong Wang, Songheng Zhang, Yangqiu Song, Huamin. Qu
Research Collection School Of Computing and Information Systems
Visualization recommendation or automatic visualization generation can significantly lower the barriers for general users to rapidly create effective data visualizations, especially for those users without a background in data visualizations. However, existing rule-based approaches require tedious manual specifications of visualization rules by visualization experts. Other machine learning-based approaches often work like black-box and are difficult to understand why a specific visualization is recommended, limiting the wider adoption of these approaches. This paper fills the gap by presenting KG4Vis, a knowledge graph (KG)-based approach for visualization recommendation. It does not require manual specifications of visualization rules and can also guarantee good …
M2lens: Visualizing And Explaining Multimodal Models For Sentiment Analysis, Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, Huamin Qu
M2lens: Visualizing And Explaining Multimodal Models For Sentiment Analysis, Xingbo Wang, Jianben He, Zhihua Jin, Muqiao Yang, Yong Wang, Huamin Qu
Research Collection School Of Computing and Information Systems
Multimodal sentiment analysis aims to recognize people's attitudes from multiple communication channels such as verbal content (i.e., text), voice, and facial expressions. It has become a vibrant and important research topic in natural language processing. Much research focuses on modeling the complex intra- and inter-modal interactions between different communication channels. However, current multimodal models with strong performance are often deep-learning-based techniques and work like black boxes. It is not clear how models utilize multimodal information for sentiment predictions. Despite recent advances in techniques for enhancing the explainability of machine learning models, they often target unimodal scenarios (e.g., images, sentences), and …
Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew
Do Sequels Outperform Or Disappoint? Insights From An Analysis Of Amazon Echo Consumer Reviews, Kyong Jin Shim, Siaw Ling Lo, Su Yee Liew
Research Collection School Of Computing and Information Systems
Rapid technological advances in recent years drastically transformed our world. Amidst modern technological inventions such as smart phones, smart watches and smart home devices, consumers of electronic digital devices experience greatly improved automation, productivity, and efficiency in conducting routine daily tasks, information searching, shopping as well as finding entertainment. In the last few years, the global smart speaker market has undergone significant growth. As technology continues to advance and smart speakers are equipped with innovative features, the adoption of smart speakers will increase and so will consumer expectations. This research paper presents an aspect-specific sentiment analysis of consumer reviews of …
Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen
Defining Smart Contract Defects On Ethereum, Jiachi Chen, Xin Xia, David Lo, John Grundy, Xiapu Luo, Ting Chen
Research Collection School Of Computing and Information Systems
Smart contracts are programs running on a blockchain. They are immutable to change, and hence can not be patched for bugs once deployed. Thus it is critical to ensure they are bug-free and well-designed before deployment. A Contract defect is an error, flaw or fault in a smart contract that causes it to produce an incorrect or unexpected result, or to behave in unintended ways. The detection of contract defects is a method to avoid potential bugs and improve the design of existing code. Since smart contracts contain numerous distinctive features, such as the gas system. decentralized, it is important …
On Discovering Motifs And Frequent Patterns In Spatial Trajectories With Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Jiahao Zhang, Kai Wang
On Discovering Motifs And Frequent Patterns In Spatial Trajectories With Discrete Fréchet Distance, Bo Tang, Man Lung Yiu, Kyriakos Mouratidis, Jiahao Zhang, Kai Wang
Research Collection School Of Computing and Information Systems
The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between two trajectories. It has been successfully adopted in a multitude of applications, such as signature and handwriting recognition, computer graphics, as well as geographic applications. Spatial applications, e.g., sports analysis, traffic analysis, etc. require discovering similar subtrajectories within a single trajectory or across multiple trajectories. In this paper, we adopt DFD as the similarity measure, and study two representative trajectory analysis problems, namely, motif discovery and frequent pattern discovery. Due to the time complexity of DFD, these tasks are computationally challenging. We address that challenge with a suite of …
Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen
Towards An Instant Structure-Property Prediction Quality Control Tool For Additive Manufactured Steel Using A Crystal Plasticity Trained Deep Learning Surrogate, Yuhui Tu, Zhongzhou Liu, Luiz Carneiro, Caitriona M. Ryan, Andrew C. Parnell, Sean B. Leen
Research Collection School Of Computing and Information Systems
The ability to conduct in-situ real-time process-structure-property checks has the potential to overcome process and material uncertainties, which are key obstacles to improved uptake of metal powder bed fusion in industry. Efforts are underway for live process monitoring such as thermal and image-based data gathering for every layer printed. Current crystal plasticity finite element (CPFE) modelling is capable of predicting the associated strength based on a microstructural image and material data but is computationally expensive. This work utilizes a large database of input–output samples from CPFE modelling to develop a trained deep neural network (DNN) model which instantly estimates the …
Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan
Learning From Web Recipe-Image Pairs For Food Recognition: Problem, Baselines And Performance, Bin Zhu, Chong-Wah Ngo, Wing-Kwong Chan
Research Collection School Of Computing and Information Systems
Cross-modal recipe retrieval has recently been explored for food recognition and understanding. Text-rich recipe provides not only visual content information (e.g., ingredients, dish presentation) but also procedure of food preparation (cutting and cooking styles). The paired data is leveraged to train deep models to retrieve recipes for food images. Most recipes on the Web include sample pictures as the references. The paired multimedia data is not noise-free, due to errors such as pairing of images containing partially prepared dishes with recipes. The content of recipes and food images are not always consistent due to free-style writing and preparation of food …
Lightweight And Expressive Fine-Grained Access Control For Healthcare Internet-Of-Things, Shengmin Xu, Yingjiu Li, Robert H. Deng, Yinghui Zhang, Xiangyang Luo, Ximeng Liu
Lightweight And Expressive Fine-Grained Access Control For Healthcare Internet-Of-Things, Shengmin Xu, Yingjiu Li, Robert H. Deng, Yinghui Zhang, Xiangyang Luo, Ximeng Liu
Research Collection School Of Computing and Information Systems
Healthcare Internet-of-Things (IoT) is an emerging paradigm that enables embedded devices to monitor patients vital signals and allows these data to be aggregated and outsourced to the cloud. The cloud enables authorized users to store and share data to enjoy on-demand services. Nevertheless, it also causes many security concerns because of the untrusted network environment, dishonest cloud service providers and resource-limited devices. To preserve patients' privacy, existing solutions usually apply cryptographic tools to offer access controls. However, fine-grained access control among authorized users is still a challenge, especially for lightweight and resource-limited end-devices. In this paper, we propose a novel …
Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng
Secure Cloud Data Deduplication With Efficient Re-Encryption, Haoran Yuan, Xiaofeng Chen, Jin Li, Tao Jiang, Jianfeng Wang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Data deduplication technique has been widely adopted by commercial cloud storage providers, which is both important and necessary in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware …
A Blockchain-Based Self-Tallying Voting Protocol In Decentralized Iot, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Dongxi Liu, Xiaojiang Du, Mohsen Guizani
A Blockchain-Based Self-Tallying Voting Protocol In Decentralized Iot, Yannan Li, Willy Susilo, Guomin Yang, Yong Yu, Dongxi Liu, Xiaojiang Du, Mohsen Guizani
Research Collection School Of Computing and Information Systems
The Internet of Things (IoT) is experiencing explosive growth and has gained extensive attention from academia and industry in recent years. However, most of the existing IoT infrastructures are centralized, which may cause the issues of unscalability and single-point-of-failure. Consequently, decentralized IoT has been proposed by taking advantage of the emerging technology called blockchain. Voting systems are widely adopted in IoT, for example a leader election in wireless sensor networks. Self-tallying voting systems are alternatives to unsuitable, traditional centralized voting systems in decentralized IoT. Unfortunately, self-tallying voting systems inherently suffer from fairness issues, such as adaptive and abortive issues caused …
Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang
Predictive Models In Software Engineering: Challenges And Opportunities, Yanming Yang, Xin Xia, David Lo, Tingting Bi, John C. Grundy, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging, and software maintenance. This article is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application …
Github Discussions: An Exploratory Study Of Early Adoption, Hideaki Hata, Nicole Novielli, Sebastian Baltes, Raula Kula, Christoph Treude
Github Discussions: An Exploratory Study Of Early Adoption, Hideaki Hata, Nicole Novielli, Sebastian Baltes, Raula Kula, Christoph Treude
Research Collection School Of Computing and Information Systems
Discussions is a new feature of GitHub for asking questions or discussing topics outside of specific Issues or Pull Requests. Before being available to all projects in December 2020, it had been tested on selected open source software projects. To understand how developers use this novel feature, how they perceive it, and how it impacts the development processes, we conducted a mixed-methods study based on early adopters of GitHub discussions from January until July 2020. We found that: (1) errors, unexpected behavior, and code reviews are prevalent discussion categories; (2) there is a positive relationship between project member involvement and …
Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection With Knowledge, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Contextualized Knowledge-Aware Attentive Neural Network: Enhancing Answer Selection With Knowledge, Yang Deng, Yuexiang Xie, Yaliang Li, Min Yang, Wai Lam, Ying Shen
Research Collection School Of Computing and Information Systems
Answer selection, which is involved in many natural language processing applications, such as dialog systems and question answering (QA), is an important yet challenging task in practice, since conventional methods typically suffer from the issues of ignoring diverse real-world background knowledge. In this article, we extensively investigate approaches to enhancing the answer selection model with external knowledge from knowledge graph (KG). First, we present a context-knowledge interaction learning framework, Knowledge-aware Neural Network, which learns the QA sentence representations by considering a tight interaction with the external knowledge from KG and the textual information. Then, we develop two kinds of knowledge-aware …
The Two-Echelon Vehicle Routing Problem With Transshipment Nodes And Occasional Drivers: Formulation And Adaptive Large Neighborhood Search Heuristic, Vincent F. Yu, Minh P. K. Nguyen, Kuza Putra, Aldy Gunawan, I. Gusti Bagus Budi Dharma
The Two-Echelon Vehicle Routing Problem With Transshipment Nodes And Occasional Drivers: Formulation And Adaptive Large Neighborhood Search Heuristic, Vincent F. Yu, Minh P. K. Nguyen, Kuza Putra, Aldy Gunawan, I. Gusti Bagus Budi Dharma
Research Collection School Of Computing and Information Systems
This research introduces a new variant of the two-echelon vehicle routing problem (2EVRP) called the two-echelon vehicle routing problem with transshipment nodes and occasional drivers (2EVRP-TN-OD). In addition to city freighters in the second-echelon network, a set of occasional drivers (ODs) is available to serve customers. ODs are the basis of a crowd-shipping system in which crowds with planned trips are willing to take detours to deliver packages in exchange for some compensation. To serve customers, ODs collect the assigned packages at either satellite served by first-echelon trucks or transshipment nodes served by city freighters. We formulate this problem as …
A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System, Jianfei Sun, Guowen Xu, Tianwei Zhang, Mamoun Alazab, Robert H. Deng
A Practical Fog-Based Privacy-Preserving Online Car-Hailing Service System, Jianfei Sun, Guowen Xu, Tianwei Zhang, Mamoun Alazab, Robert H. Deng
Research Collection School Of Computing and Information Systems
Aiming for minimizing passengers’ waiting time and vehicles’ vacancy rate, online car-hailing service systems with fog computing has been deployed in various scenarios. In this paper, we focus on addressing the security and privacy issues in such a promising system by customizing a new cryptographic primitive to provide the following security guarantees: (1) private, finegrained and bilateral order matching between passengers and drivers; (2) authenticity verification of passengers’ orders in the form of ciphertext, and (3) temporal assurance of passengers’ ciphertext orders. To the best of our knowledge, no previous system has been designed to meet all three requirements. Existing …
A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo
A Survey On Deep Learning For Software Engineering, Yanming Yang, Xin Xia, David Lo
Research Collection School Of Computing and Information Systems
In 2006, Geoffrey Hinton proposed the concept of training "Deep Neural Networks (DNNs)" and an improved model training method to break the bottleneck of neural network development. More recently, the introduction of AlphaGo in 2016 demonstrated the powerful learning ability of deep learning and its enormous potential. Deep learning has been increasingly used to develop state-of-the-art software engineering (SE) research tools due to its ability to boost performance for various SE tasks. There are many factors, e.g., deep learning model selection, internal structure differences, and model optimization techniques, that may have an impact on the performance of DNNs applied in …
Github Repositories With Links To Academic Papers: Public Access, Traceability, And Evolution, Supatsara Wattanakriengkrai, Bodin Chinthanet, Hideaki Hata, Raula Kula, Christoph Treude, Jin Guo, Kenichi Matsumoto
Github Repositories With Links To Academic Papers: Public Access, Traceability, And Evolution, Supatsara Wattanakriengkrai, Bodin Chinthanet, Hideaki Hata, Raula Kula, Christoph Treude, Jin Guo, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
Traceability between published scientific breakthroughs and their implementation is essential, especially in the case of open-source scientific software which implements bleeding-edge science in its code. However, aligning the link between GitHub repositories and academic papers can prove difficult, and the current practice of establishing and maintaining such links remains unknown. This paper investigates the role of academic paper references contained in these repositories. We conduct a large-scale study of 20 thousand GitHub repositories that make references to academic papers. We use a mixed-methods approach to identify public access, traceability and evolutionary aspects of the links. Although referencing a paper is …
Mind The Gap: Reimagining An Interactive Programming Course For The Synchronous Hybrid Classroom, Christopher M. Poskitt, Kyong Jin Shim, Yi Meng Lau, Hong Seng Ong
Mind The Gap: Reimagining An Interactive Programming Course For The Synchronous Hybrid Classroom, Christopher M. Poskitt, Kyong Jin Shim, Yi Meng Lau, Hong Seng Ong
Research Collection School Of Computing and Information Systems
COVID-19 has significantly affected universities, forcing many courses to be delivered entirely online. As countries bring the pandemic under control, a potential way to safely resume some face-to-face teaching is the synchronous hybrid classroom, in which physically and remotely attending students are taught simultaneously. This comes with challenges, however, including the risk that remotely attending students perceive a ‘gap’ between their engagement and that of their physical peers. In this experience report, we describe how an interactive programming course was adapted to hybrid delivery in a way that mitigated this risk. Our solution centred on the use of a professional …
Partnering For Value Perfection And Business Sustainability In The Cloud Services Brokerage Market, Richard Shang, Robert John Kauffman
Partnering For Value Perfection And Business Sustainability In The Cloud Services Brokerage Market, Richard Shang, Robert John Kauffman
Research Collection School Of Computing and Information Systems
The cloud computing and services market has advanced in the past ten years. They now include most IT services from fundamental computing to cutting-edge AI capabilities. With the widespread adoption of cloud services, clients are facing the fact that they are utilizing cloud resources at a sub-optimal level. Cloud services brokers (CSBs) grew from the market to fill the needs for cloud resource management and risk mitigation. Based on analysis of the cloud market and the case of cloud services brokerage and related activities in North America, we offer theoretical analysis for how value creation works, its impacts on the …
Steps Before Syntax: Helping Novice Programmers Solve Problems Using The Pcdit Framework, Oka Kurniawan, Cyrille Jegourel, Norman Tiong Seng Lee, Matthieu De Mari, Christopher M. Poskitt
Steps Before Syntax: Helping Novice Programmers Solve Problems Using The Pcdit Framework, Oka Kurniawan, Cyrille Jegourel, Norman Tiong Seng Lee, Matthieu De Mari, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Novice programmers often struggle with problem solving due to the high cognitive loads they face. Furthermore, many introductory programming courses do not explicitly teach it, assuming that problem solving skills are acquired along the way. In this paper, we present 'PCDIT', a non-linear problem solving framework that provides scaffolding to guide novice programmers through the process of transforming a problem specification into an implemented and tested solution for an imperative programming language. A key distinction of PCDIT is its focus on developing concrete cases for the problem early without actually writing test code: students are instead encouraged to think about …
Authenticated Data Redaction With Accountability And Transparency, Jinhua Ma, Xinyi Huang, Yi Mu, Robert H. Deng
Authenticated Data Redaction With Accountability And Transparency, Jinhua Ma, Xinyi Huang, Yi Mu, Robert H. Deng
Research Collection School Of Computing and Information Systems
A common practice in data redaction is removing sensitive information prior to data publication or release. In data-driven applications, one must be convinced that the redacted data is still trustworthy. Meanwhile, the data redactor must be held accountable for (malicious) redaction, which could change/hide the meaning of the original data. Motivated by these concerns, we present a novel solution for authenticated data redaction based on a new Redactable Signature Scheme with Implicit Accountability (RSS - IA). In the event of a dispute, not only the original data signer but also the redactor can generate an evidence tag to unequivocally identify …
A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan
A Quantum Interpretation Of Separating Conjunction For Local Reasoning Of Quantum Programs Based On Separation Logic, Xuan Bach Le, Shang-Wei Lin, Jun Sun, David Sanan
Research Collection School Of Computing and Information Systems
It is well-known that quantum programs are not only complicated to design but also challenging to verify because the quantum states can have exponential size and require sophisticated mathematics to encode and manipulate. To tackle the state-space explosion problem for quantum reasoning, we propose a Hoare-style inference framework that supports local reasoning for quantum programs. By providing a quantum interpretation of the separating conjunction, we are able to infuse separation logic into our framework and apply local reasoning using a quantum frame rule that is similar to the classical frame rule. For evaluation, we apply our framework to verify various …