Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Physical Sciences and Mathematics (8556)
- Computer Sciences (8539)
- Databases and Information Systems (3453)
- Software Engineering (2164)
- Artificial Intelligence and Robotics (1697)
-
- Information Security (1071)
- Numerical Analysis and Scientific Computing (1025)
- Graphics and Human Computer Interfaces (933)
- Engineering (888)
- Social and Behavioral Sciences (681)
- Business (642)
- Theory and Algorithms (493)
- Computer Engineering (451)
- Operations Research, Systems Engineering and Industrial Engineering (410)
- Programming Languages and Compilers (379)
- OS and Networks (324)
- Communication (297)
- Social Media (240)
- Public Affairs, Public Policy and Public Administration (217)
- Transportation (195)
- Medicine and Health Sciences (181)
- Education (170)
- Management Information Systems (167)
- Data Storage Systems (162)
- E-Commerce (148)
- International and Area Studies (111)
- Asian Studies (110)
- Health Information Technology (110)
- Digital Communications and Networking (104)
- Technology and Innovation (104)
- Keyword
-
- Deep learning (123)
- Machine learning (122)
- Social media (74)
- Artificial intelligence (72)
- Reinforcement learning (69)
-
- Privacy (65)
- Data mining (64)
- Cloud computing (58)
- Deep Learning (56)
- Empirical study (54)
- Optimization (54)
- Security (54)
- Visualization (51)
- Task analysis (50)
- Online learning (49)
- Software engineering (49)
- Training (49)
- Neural networks (48)
- Singapore (48)
- Anomaly detection (47)
- Feature extraction (46)
- Twitter (46)
- Large Language Models (45)
- Blockchain (44)
- Collaboration (44)
- Semantics (43)
- Access control (41)
- Algorithms (40)
- Android (39)
- Natural language processing (39)
- Publication Year
- File Type
Articles 2521 - 2550 of 8597
Full-Text Articles in Entire DC Network
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 …
Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss, Cheng Yan, Guansong Pang, Xiao Bai, Changhong Liu, Xin Ning, Jun Zhou
Beyond Triplet Loss: Person Re-Identification With Fine-Grained Difference-Aware Pairwise Loss, Cheng Yan, Guansong Pang, Xiao Bai, Changhong Liu, Xin Ning, Jun Zhou
Research Collection School Of Computing and Information Systems
Person Re-IDentification (ReID) aims at re-identifying persons from different viewpoints across multiple cameras. Capturing the fine-grained appearance differences is often the key to accurate person ReID, because many identities can be differentiated only when looking into these fine-grained differences. However, most state-of-the-art person ReID approaches, typically driven by a triplet loss, fail to effectively learn the fine-grained features as they are focused more on differentiating large appearance differences. To address this issue, we introduce a novel pairwise loss function that enables ReID models to learn the fine-grained features by adaptively enforcing an exponential penalization on the images of small differences …
Interest Points Analysis For Internet Forum Based On Long-Short Windows Similarity, Xinghai Ju, Jicang Lu, Xiangyang Luo, Gang Zhou, Shiyu Wang, Shunhang Li, Yang Yang
Interest Points Analysis For Internet Forum Based On Long-Short Windows Similarity, Xinghai Ju, Jicang Lu, Xiangyang Luo, Gang Zhou, Shiyu Wang, Shunhang Li, Yang Yang
Research Collection School Of Computing and Information Systems
For Internet forum Points of Interest (PoI), existing analysis methods are usually lack of usability analysis under different conditions and ignore the long-term variation, which lead to blindness in method selection. To address this problem, this paper proposed a PoI variation prediction framework based on similarity analysis between long and short windows. Based on the framework, this paper presented 5 PoI analysis algorithms which can be categorized into 2 types, i.e., the traditional sequence analysis methods such as autoregressive integrated moving average model (ARIMA), support vector regressor (SVR), and the deep learning methods such as convolutional neural network (CNN), long-short …
Approximate K-Nn Graph Construction: A Generic Online Approach, Wan-Lei Zhao, Hui Wang, Chong-Wah Ngo
Approximate K-Nn Graph Construction: A Generic Online Approach, Wan-Lei Zhao, Hui Wang, Chong-Wah Ngo
Research Collection School Of Computing and Information Systems
Nearest neighbor search and k-nearest neighbor graph construction are two fundamental issues that arise from many disciplines such as multimedia information retrieval, data-mining, and machine learning. They become more and more imminent given the big data emerge in various fields in recent years. In this paper, a simple but effective solution both for approximate k-nearest neighbor search and approximate k-nearest neighbor graph construction is presented. These two issues are addressed jointly in our solution. On one hand, the approximate k-nearest neighbor graph construction is treated as a search task. Each sample along with its k-nearest neighbors is joined into the …
On The Reproducibility And Replicability Of Deep Learning In Software Engineering, Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John C. Grundy, Xiaohu Yang
On The Reproducibility And Replicability Of Deep Learning In Software Engineering, Chao Liu, Cuiyun Gao, Xin Xia, David Lo, John C. Grundy, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Context: Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge.Objective: Although many DL studies have reported substantial advantages over other state-of-the-art models on effectiveness, they often ignore two factors: (1) reproducibility—whether the reported experimental results can be obtained by other researchers using authors’ artifacts (i.e., source code and datasets) with the same experimental setup; and (2) replicability—whether the reported experimental result can be obtained by other researchers using their re-implemented artifacts with a …
Correlating Automated And Human Evaluation Of Code Documentation Generation Quality, Xing Hu, Qiuyuan Chen, Haoye Wang, Xin Xia, David Lo, Thomas Zimmermann
Correlating Automated And Human Evaluation Of Code Documentation Generation Quality, Xing Hu, Qiuyuan Chen, Haoye Wang, Xin Xia, David Lo, Thomas Zimmermann
Research Collection School Of Computing and Information Systems
Automatic code documentation generation has been a crucial task in the field of software engineering. It not only relieves developers from writing code documentation but also helps them to understand programs better. Specifically, deep-learning-based techniques that leverage large-scale source code corpora have been widely used in code documentation generation. These works tend to use automatic metrics (such as BLEU, METEOR, ROUGE, CIDEr, and SPICE) to evaluate different models. These metrics compare generated documentation to reference texts by measuring the overlapping words. Unfortunately, there is no evidence demonstrating the correlation between these metrics and human judgment. We conduct experiments on two …
Delta Debugging Microservice Systems With Parallel Optimization, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Wenha Li, Dan Ding
Delta Debugging Microservice Systems With Parallel Optimization, Xiang Zhou, Xin Peng, Tao Xie, Jun Sun, Chao Ji, Wenha Li, Dan Ding
Research Collection School Of Computing and Information Systems
Microservice systems are complicated due to their runtime environments and service communications. Debugging a failure involves the deployment and manipulation of microservice systems on a containerized environment and faces unique challenges due to the high complexity and dynamism of microservices. To address these challenges, we propose a debugging approach for microservice systems based on the delta debugging algorithm, which is to minimize failure-inducing deltas of circumstances (e.g., deployment, environmental configurations). Our approach includes novel techniques for defining, deploying/manipulating, and executing deltas during delta debugging. In particular, to construct a (failing) circumstance space for delta debugging to minimize, our approach defines …
Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren
Novel Secure Outsourcing Of Modular Inversion For Arbitrary And Variable Modulus, Chengliang Tian, Jia Yu, Hanlin Zhang, Haiyang Xue, Cong Wang, Kui Ren
Research Collection School Of Computing and Information Systems
In cryptography and algorithmic number theory, modular inversion is viewed as one of the most common and time-consuming operations. It is hard to be directly accomplished on resource-constrained clients (e.g., mobile devices and IC cards) since modular inversion involves a great amount of operations on large numbers in practice. To address the above problem, this paper proposes a novel unimodular matrix transformation technique to realize secure outsourcing of modular inversion. This technique makes our algorithm achieve several amazing properties. First, to the best of our knowledge, it is the first secure outsourcing computation algorithm that supports arbitrary and variable modulus, …
Building Action Sets In A Deep Reinforcement Learner, Yongzhao Wang, Arunesh Sinha, Sky C.H. Wang, Michael P. Wellman
Building Action Sets In A Deep Reinforcement Learner, Yongzhao Wang, Arunesh Sinha, Sky C.H. Wang, Michael P. Wellman
Research Collection School Of Computing and Information Systems
In many policy-learning applications, the agent may execute a set of actions at each decision stage. Choosing among an exponential number of alternatives poses a computational challenge, and even representing actions naturally expressed as sets can be a tricky design problem. Building upon prior approaches that employ deep neural networks and iterative construction of action sets, we introduce a reward-shaping approach to apportion reward to each atomic action based on its marginal contribution within an action set, thereby providing useful feedback for learning to build these sets. We demonstrate our method in two environments where action spaces are combinatorial. Experiments …
Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng
Early Prediction Of Hate Speech Propagation, Ken-Yu Lin, Roy Ka-Wei Lee, Wei Gao, Wen-Chih Peng
Research Collection School Of Computing and Information Systems
Online hate speech has disrupted the social connectedness in online communities and raises public safety concerns in our societies. Motivated by this rising issue, researchers have developed many machine learning and deep learning methods to detect hate speech in social media automatically. However, most of the existing automated solutions have focused on detecting hate speech in a single post, neglecting the network and information propagation effects of social media platforms. Ideally, the content moderators would want to identify the hateful posts and monitor posts and threads that are likely to induce hate. This paper aims to address this research gap …
Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter
Efficient Reinforcement Learning In Resource Allocation Problems Through Permutation Invariant Multi-Task Learning, Desmond Cai, Shiau Hong Lim, Laura Wynter
Research Collection School Of Computing and Information Systems
One of the main challenges in real-world reinforcement learning is to learn successfully from limited training samples. We show that in certain settings, the available data can be dramatically increased through a form of multi-task learning, by exploiting an invariance property in the tasks. We provide a theoretical performance bound for the gain in sample efficiency under this setting. This motivates a new approach to multi-task learning, which involves the design of an appropriate neural network architecture and a prioritized task-sampling strategy. We demonstrate empirically the effectiveness of the proposed approach on two real-world sequential resource allocation tasks where this …
Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang
Self-Supervised Learning Disentangled Group Representation As Feature, Tan Wang, Zhongqi Yue, Jianqiang Huang, Qianru Sun, Hanwang Zhang
Research Collection School Of Computing and Information Systems
A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics). In this paper, we formulate the notion of “good” representation from a group-theoretic view using Higgins’ definition of disentangled representation [38], and show that existing Self-Supervised Learning (SSL) only disentangles simple augmentation features such as rotation and colorization, thus unable to modularize the remaining semantics. To break the limitation, we propose an iterative SSL algorithm: Iterative Partition-based Invariant Risk Minimization (IP-IRM), which successfully grounds the abstract semantics and the group acting on them into concrete contrastive learning. …
Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Automated Doubt Identification From Informal Reflections Through Hybrid Sentic Patterns And Machine Learning Approach, Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
Research Collection School Of Computing and Information Systems
Do my students understand? The question that lingers in every instructor’s mind after each lesson. With the focus on learner-centered pedagogy, is it feasible to provide timely and relevant guidance to individual learners according to their levels of understanding? One of the options available is to collect reflections from learners after each lesson to extract relevant feedback so that doubts or questions can be addressed in a timely manner. In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students’ …
Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport
Ai And The Future Of Work: What We Know Today, Steven M. Miller, Thomas H. Davenport
Research Collection School Of Computing and Information Systems
To contribute to a better understanding of the contemporary realities of AI workplace deployments, the authors recently completed 29 case studies of people doing their everyday work with AI-enabled smart machines. Twenty-three of these examples were from North America, mostly in the US. Six were from Southeast Asia, mostly in Singapore. In this essay, we compare our findings on job and workplace impacts to those reported in the MIT Task Force on the Work of the Future report, as we consider that to be the most comprehensive recent study on this topic.
Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko
Imon: Appearance-Based Gaze Tracking System On Mobile Devices, Sinh Huynh, Rajesh Krishna Balan, Jeonggil Ko
Research Collection School Of Computing and Information Systems
Gaze tracking is a key building block used in many mobile applications including entertainment, personal productivity, accessibility, medical diagnosis, and visual attention monitoring. In this paper, we present iMon, an appearance-based gaze tracking system that is both designed for use on mobile phones and has significantly greater accuracy compared to prior state-of-the-art solutions. iMon achieves this by comprehensively considering the gaze estimation pipeline and then overcoming three different sources of errors. First, instead of assuming that the user's gaze is fixed to a single 2D coordinate, we construct each gaze label using a probabilistic 2D heatmap gaze representation input to …
On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan
On Analysing Student Resilience In Higher Education Programs Using A Data-Driven Approach, Audrey Tedja Widjaja, Ee-Peng Lim, Aldy Gunawan
Research Collection School Of Computing and Information Systems
Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a data-driven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we …
Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun
Rmm: Reinforced Memory Management For Class-Incremental Learning, Yaoyao Liu, Qianru Sun, Qianru Sun
Research Collection School Of Computing and Information Systems
Class-Incremental Learning (CIL) [38] trains classifiers under a strict memory budget: in each incremental phase, learning is done for new data, most of which is abandoned to free space for the next phase. The preserved data are exemplars used for replaying. However, existing methods use a static and ad hoc strategy for memory allocation, which is often sub-optimal. In this work, we propose a dynamic memory management strategy that is optimized for the incremental phases and different object classes. We call our method reinforced memory management (RMM), leveraging reinforcement learning. RMM training is not naturally compatible with CIL as the …
Etherlearn: Decentralizing Learning Via Blockchain, Nguyen Binh Duong Ta, Tian Jun Joel Yang
Etherlearn: Decentralizing Learning Via Blockchain, Nguyen Binh Duong Ta, Tian Jun Joel Yang
Research Collection School Of Computing and Information Systems
In institutes of higher learning, most of the time course material development and delivery follow a centralized model which is fully lecturer-controlled. In this model, engaging students as partners in learning is a challenging problem as: 1) students are usually hesitant to contribute due to the fear of getting it wrong, 2) not much incentive for them to put in the extra effort, and 3) current online learning systems lack adequate facilities to support seamless and anonymous interactions between students. In this work, we propose EtherLearn, a blockchain based peer-learning system to distribute the control of how course material and …
Checking Smart Contracts With Structural Code Embedding, Zhipeng Gao, Lingxiao Jiang, Xin Xia, David Lo, John Grundy
Checking Smart Contracts With Structural Code Embedding, Zhipeng Gao, Lingxiao Jiang, Xin Xia, David Lo, John Grundy
Research Collection School Of Computing and Information Systems
Smart contracts have been increasingly used together with blockchains to automate financial and business transactions. However, many bugs and vulnerabilities have been identified in many contracts which raises serious concerns about smart contract security, not to mention that the blockchain systems on which the smart contracts are built can be buggy. Thus, there is a significant need to better maintain smart contract code and ensure its high reliability. In this paper, we propose an automated approach to learn characteristics of smart contracts in Solidity, useful for repetitive contract code, bug detection and contract validation. Our new approach is based on …