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Giving Back: Contributions Congruent To Library Dependency Changes In A Software Ecosystem, Supatsara Wattanakriengkrai, Dong Wang, Raula Gaikovina Kula, Christoph Treude, Patanamon Thongtanunam, Takashi Ishio, Kenichi Matsumoto
Giving Back: Contributions Congruent To Library Dependency Changes In A Software Ecosystem, Supatsara Wattanakriengkrai, Dong Wang, Raula Gaikovina Kula, Christoph Treude, Patanamon Thongtanunam, Takashi Ishio, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
The widespread adoption of third-party libraries for contemporary software development has led to the creation of large inter-dependency networks, where sustainability issues of a single library can have widespread network effects. Maintainers of these libraries are often overworked, relying on the contributions of volunteers to sustain these libraries. To understand these contributions, in this work, we leverage socio-technical techniques to introduce and formalise dependency-contribution congruence (DC congruence) at both ecosystem and library level, i.e., to understand the degree and origins of contributions congruent to dependency changes, analyze whether they contribute to library dormancy (i.e., a lack of activity), and investigate …
Mimusa: Mimicking Human Language Understanding For Fine-Grained Multi-Class Sentiment Analysis, Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan
Mimusa: Mimicking Human Language Understanding For Fine-Grained Multi-Class Sentiment Analysis, Zhaoxia Wang, Zhenda Hu, Seng-Beng Ho, Erik Cambria, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Sentiment analysis is an important natural language processing (NLP) task due to a wide range of applications. Most existing sentiment analysis techniques are limited to the analysis carried out at the aggregate level, merely providing negative, neutral and positive sentiments. The latest deep learning-based methods have been leveraged to provide more than three sentiment classes. However, such learning-based methods are still black-box-based methods rather than explainable language processing methods. To address this gap, this paper proposes a new explainable fine-grained multi-class sentiment analysis method, namely MiMuSA, which mimics the human language understanding processes. The proposed method involves a multi-level modular …
Stargazer: An Interactive Camera Robot For Capturing How-To Videos Based On Subtle Instructor Cues, Jiannan Li, Mauricio Sousa, Karthik Mahadevan, Bryan Wang, Paula Akemi Aoyagui, Nicole Yu, Angela Yang, Ravin Balakrishnan, Anthony Tang, Tovi Grossman
Stargazer: An Interactive Camera Robot For Capturing How-To Videos Based On Subtle Instructor Cues, Jiannan Li, Mauricio Sousa, Karthik Mahadevan, Bryan Wang, Paula Akemi Aoyagui, Nicole Yu, Angela Yang, Ravin Balakrishnan, Anthony Tang, Tovi Grossman
Research Collection School Of Computing and Information Systems
Live and pre-recorded video tutorials are an effective means for teaching physical skills such as cooking or prototyping electronics. A dedicated cameraperson following an instructor’s activities can improve production quality. However, instructors who do not have access to a cameraperson’s help often have to work within the constraints of static cameras. We present Stargazer, a novel approach for assisting with tutorial content creation with a camera robot that autonomously tracks regions of interest based on instructor actions to capture dynamic shots. Instructors can adjust the camera behaviors of Stargazer with subtle cues, including gestures and speech, allowing them to fluidly …
Subgraph Centralization: A Necessary Step For Graph Anomaly Detection, Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song
Subgraph Centralization: A Necessary Step For Graph Anomaly Detection, Zhong Zhuang, Kai Ming Ting, Guansong Pang, Shuaibin Song
Research Collection School Of Computing and Information Systems
Abstract Graph anomaly detection has attracted a lot of interest recently. Despite their successes, existing detectors have at least two of the three weaknesses: (a) high computational cost which limits them to small-scale networks only; (b) existing treatment of subgraphs produces suboptimal detection accuracy; and (c) unable to provide an explanation as to why a node is anomalous, once it is identified. We identify that the root cause of these weaknesses is a lack of a proper treatment for subgraphs. A treatment called Subgraph Centralization for graph anomaly detection is proposed to address all the above weaknesses. Its importance is …
Parsing-Conditioned Anime Translation: A New Dataset And Method, Zhansheng Li, Yangyang Xu, Nanxuan Zhao, Yang Zhou, Yongtuo Liu, Dahua Lin, Shengfeng He
Parsing-Conditioned Anime Translation: A New Dataset And Method, Zhansheng Li, Yangyang Xu, Nanxuan Zhao, Yang Zhou, Yongtuo Liu, Dahua Lin, Shengfeng He
Research Collection School Of Computing and Information Systems
Anime is an abstract art form that is substantially different from the human portrait, leading to a challenging misaligned image translation problem that is beyond the capability of existing methods. This can be boiled down to a highly ambiguous unconstrained translation between two domains. To this end, we design a new anime translation framework by deriving the prior knowledge of a pre-Trained StyleGAN model. We introduce disentangled encoders to separately embed structure and appearance information into the same latent code, governed by four tailored losses. Moreover, we develop a FaceBank aggregation method that leverages the generated data of the StyleGAN, …
Nftdisk: Visual Detection Of Wash Trading In Nft Markets, Xiaolin Wen, Yong Wang, Xuanwu Yue, Feida Zhu, Min Zhu
Nftdisk: Visual Detection Of Wash Trading In Nft Markets, Xiaolin Wen, Yong Wang, Xuanwu Yue, Feida Zhu, Min Zhu
Research Collection School Of Computing and Information Systems
With the growing popularity of Non-Fungible Tokens (NFT), a new type of digital assets, various fraudulent activities have appeared in NFT markets. Among them, wash trading has become one of the most common frauds in NFT markets, which attempts to mislead investors by creating fake trading volumes. Due to the sophisticated patterns of wash trading, only a subset of them can be detected by automatic algorithms, and manual inspection is usually required. We propose NFTDisk, a novel visualization for investors to identify wash trading activities in NFT markets, where two linked visualization modules are presented: a radial visualization module with …
Asdf: A Differential Testing Framework For Automatic Speech Recognition Systems, Daniel Hao Xian Yuen, Andrew Yong Chen Pang, Zhou Yang, Chun Yong Chong, Mei Kuan Lim, David Lo
Asdf: A Differential Testing Framework For Automatic Speech Recognition Systems, Daniel Hao Xian Yuen, Andrew Yong Chen Pang, Zhou Yang, Chun Yong Chong, Mei Kuan Lim, David Lo
Research Collection School Of Computing and Information Systems
Recent years have witnessed wider adoption of Automated Speech Recognition (ASR) techniques in various domains. Consequently, evaluating and enhancing the quality of ASR systems is of great importance. This paper proposes Asdf, an Automated Speech Recognition Differential Testing Framework to test ASR systems. Asdf extends an existing ASR testing tool, the CrossASR++, which synthesizes test cases from a text corpus. However, CrossASR++ fails to make use of the text corpus efficiently and provides limited information on how the failed test cases can improve ASR systems. To address these limitations, our tool incorporates two novel features: (1) a text transformation module …
Code Will Tell: Visual Identification Of Ponzi Schemes On Ethereum, Xiaolin Wen, Kim Siang Yeo, Yong Wang, Ling Cheng, Feida Zhu, Min Zhu
Code Will Tell: Visual Identification Of Ponzi Schemes On Ethereum, Xiaolin Wen, Kim Siang Yeo, Yong Wang, Ling Cheng, Feida Zhu, Min Zhu
Research Collection School Of Computing and Information Systems
Ethereum has become a popular blockchain with smart contracts for investors nowadays. Due to the decentralization and anonymity of Ethereum, Ponzi schemes have been easily deployed and caused significant losses to investors. However, there are still no explainable and effective methods to help investors easily identify Ponzi schemes and validate whether a smart contract is actually a Ponzi scheme. To fill the research gap, we propose PonziLens, a novel visualization approach to help investors achieve early identification of Ponzi schemes by investigating the operation codes of smart contracts. Specifically, we conduct symbolic execution of opcode and extract the control flow …
Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun
Open-Set Domain Adaptation By Deconfounding Domain Gaps, Xin Zhao, Shengsheng Wang, Qianru Sun
Research Collection School Of Computing and Information Systems
Open-Set Domain Adaptation (OSDA) aims to adapt the model trained on a source domain to the recognition tasks in a target domain while shielding any distractions caused by open-set classes, i.e., the classes “unknown” to the source model. Compared to standard DA, the key of OSDA lies in the separation between known and unknown classes. Existing OSDA methods often fail the separation because of overlooking the confounders (i.e., the domain gaps), which means their recognition of “unknown classes” is not because of class semantics but domain difference (e.g., styles and contexts). We address this issue by explicitly deconfounding domain gaps …
Morphologically-Aware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw
Morphologically-Aware Vocabulary Reduction Of Word Embeddings, Chong Cher Chia, Maksim Tkachenko, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
We propose SubText, a compression mechanism via vocabulary reduction. The crux is to judiciously select a subset of word embeddings which support the reconstruction of the remaining word embeddings based on their form alone. The proposed algorithm considers the preservation of the original embeddings, as well as a word’s relationship to other words that are morphologically or semantically similar. Comprehensive evaluation of the compressed vocabulary reveals SubText’s efficacy on diverse tasks over traditional vocabulary reduction techniques, as validated on English, as well as a collection of inflected languages.
Dsdnet: Toward Single Image Deraining With Self-Paced Curricular Dual Stimulations, Yong Du, Junjie Deng, Yulong Zheng, Junyu Dong, Shengfeng He
Dsdnet: Toward Single Image Deraining With Self-Paced Curricular Dual Stimulations, Yong Du, Junjie Deng, Yulong Zheng, Junyu Dong, Shengfeng He
Research Collection School Of Computing and Information Systems
A crucial challenge regarding the single image deraining task is to completely remove rain streaks while still preserving explicit image details. Due to the inherent overlapping between rain streaks and background scenes, the texture details could be inevitably lost when clearing rain away from the degraded image, making the two purposes contradictory. Existing deep learning based approaches endeavor to resolve the two issues successively in a cascaded framework or to treat them as independent tasks in a parallel structure. However, none of the models explores a proper interaction between rain distributions and hidden feature responses, which intuitively would provide more …
Does Deep Learning Improve The Performance Of Duplicate Bug Report Detection? An Empirical Study, Yuan Jiang, Xiaohong Su, Christoph Treude, Chao Shang, Tiantian Wang
Does Deep Learning Improve The Performance Of Duplicate Bug Report Detection? An Empirical Study, Yuan Jiang, Xiaohong Su, Christoph Treude, Chao Shang, Tiantian Wang
Research Collection School Of Computing and Information Systems
Do Deep Learning (DL) techniques actually help to improve the performance of duplicate bug report detection? Prior studies suggest that they do, if the duplicate bug report detection task is treated as a binary classification problem. However, in realistic scenarios, the task is often viewed as a ranking problem, which predicts potential duplicate bug reports by ranking based on similarities with existing historical bug reports. There is little empirical evidence to support that DL can be effectively applied to detect duplicate bug reports in the ranking scenario. Therefore, in this paper, we investigate whether well-known DL-based methods outperform classic information …
A Learner-Verifier Framework For Neural Network Controllers And Certificates Of Stochastic Systems, Krishnendu Chatterjee, Thomas A. Henzinger, Dorde Zikelic, Dorde Zikelic
A Learner-Verifier Framework For Neural Network Controllers And Certificates Of Stochastic Systems, Krishnendu Chatterjee, Thomas A. Henzinger, Dorde Zikelic, Dorde Zikelic
Research Collection School Of Computing and Information Systems
Reinforcement learning has received much attention for learning controllers of deterministic systems. We consider a learner-verifer framework for stochastic control systems and survey recent methods that formally guarantee a conjunction of reachability and safety properties. Given a property and a lower bound on the probability of the property being satisfied, our framework jointly learns a control policy and a formal certificate to ensure the satisfaction of the property with a desired probability threshold. Both the control policy and the formal certificate are continuous functions from states to reals, which are learned as parameterized neural networks. While in the deterministic case, …
Topic Recommendation For Github Repositories: How Far Can Extreme Multi-Label Learning Go?, Ratnadira Widyasari, Zhipeng Zhao, Thanh Le Cong, Hong Jin Kang, David Lo
Topic Recommendation For Github Repositories: How Far Can Extreme Multi-Label Learning Go?, Ratnadira Widyasari, Zhipeng Zhao, Thanh Le Cong, Hong Jin Kang, David Lo
Research Collection School Of Computing and Information Systems
GitHub is one of the most popular platforms forversion control and collaboration. In GitHub, developers are ableto assign related topics to their repositories, which is helpfulfor finding similar repositories. The topics that are assigned torepositories are varied and provide salient descriptions of therepository; some topics describe the technology employed in aproject, while others describe functionality of the project, itsgoals, and its features. Topics are part of the metadata of arepository and are useful for the organization and discoverabilityof the repository. However, the number of topics is large andthis makes it challenging to assign a relevant set of topics to arepository. …
Detecting C++ Compiler Front-End Bugs Via Grammar Mutation And Differential Testing, Haoxin Tu, He Jiang, Zhide Zhou, Yixuan Tang, Zhilei Ren, Lei Qiao, Lingxiao Jiang
Detecting C++ Compiler Front-End Bugs Via Grammar Mutation And Differential Testing, Haoxin Tu, He Jiang, Zhide Zhou, Yixuan Tang, Zhilei Ren, Lei Qiao, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
C++ is a widely used programming language and the C++ front-end is a critical part of a C++ compiler. Although many techniques have been proposed to test compilers, few studies are devoted to detecting bugs in C++ compiler. In this study, we take the first step to detect bugs in C++ compiler front-ends. To do so, two main challenges need to be addressed, namely, the acquisition of test programs that are more likely to trigger bugs in compiler front-ends and the bug identification from complicated compiler outputs. In this article, we propose a novel framework named Ccoft to detect bugs …
Towards A Design Space For Storytelling On The Fashion Technology Runway, Sydney Pratte, Anthony Tang, Shannon Hoover, Maria Elena Hoover, Matt Laprarie, Catherine Larose, Lora Oehlberg
Towards A Design Space For Storytelling On The Fashion Technology Runway, Sydney Pratte, Anthony Tang, Shannon Hoover, Maria Elena Hoover, Matt Laprarie, Catherine Larose, Lora Oehlberg
Research Collection School Of Computing and Information Systems
Fashion is driven by a narrative, i.e. a story or idea that the designer wants to convey to the audience. Fashion-tech now adds another dimension to this narrative through dynamically changing aspects of the garments. Many factors of presentation in a runway show affect how fashion-tech garments communicate a story to the audience. In this pictorial, we review a set of twenty-eight storytelling fashion-tech garments. We identify, catalogue, and categorize the factors designers used to convey stories to the audience from the runway. The design space consists of three levels: (1) the artifact-level, (2) the viewer-level, and (3) the context-level. …
Investment And Risk Management With Online News And Heterogeneous Networks, Meng Kiat Gary Ang, Ee-Peng Lim
Investment And Risk Management With Online News And Heterogeneous Networks, Meng Kiat Gary Ang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Stock price movements in financial markets are influenced by large volumes of news from diverse sources on the web, e.g., online news outlets, blogs, social media. Extracting useful information from online news for financial tasks, e.g., forecasting stock returns or risks, is, however, challenging due to the low signal-to-noise ratios of such online information. Assessing the relevance of each news article to the price movements of individual stocks is also difficult, even for human experts. In this article, we propose the Guided Global-Local Attention-based Multimodal Heterogeneous Network (GLAM) model, which comprises novel attention-based mechanisms for multimodal sequential and graph encoding, …
Improving Rumor Detection By Promoting Information Campaigns With Transformer-Based Generative Adversarial Learning, Jing Ma, Jun Li, Wei Gao, Yang Yang, Kam-Fai Wong
Improving Rumor Detection By Promoting Information Campaigns With Transformer-Based Generative Adversarial Learning, Jing Ma, Jun Li, Wei Gao, Yang Yang, Kam-Fai Wong
Research Collection School Of Computing and Information Systems
Rumors can cause devastating consequences to individuals and our society. Analysis shows that the widespread of rumors typically results from deliberate promotion of information aiming to shape the collective public opinions on the concerned event. In this paper, we combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created to make rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, further polarizing the original conversational threads to boost …
Heart: Motion-Resilient Heart Rate Monitoring With In-Ear Microphones, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Mascolo
Heart: Motion-Resilient Heart Rate Monitoring With In-Ear Microphones, Kayla-Jade Butkow, Ting Dang, Andrea Ferlini, Dong Ma, Mascolo
Research Collection School Of Computing and Information Systems
With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable HR monitoring with wearable devices has therefore gained increasing attention in recent years. Existing HR detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient HR monitoring. …
Dminer: Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu
Dminer: Dashboard Design Mining And Recommendation, Yanna Lin, Haotian Li, Aoyu Wu, Yong Wang, Huamin Qu
Research Collection School Of Computing and Information Systems
Dashboards, which comprise multiple views on a single display, help analyze and communicate multiple perspectives of data simultaneously. However, creating effective and elegant dashboards is challenging since it requires careful and logical arrangement and coordination of multiple visualizations. To solve the problem, we propose a data-driven approach for mining design rules from dashboards and automating dashboard organization. Specifically, we focus on two prominent aspects of the organization: arrangement , which describes the position, size, and layout of each view in the display space; and coordination , which indicates the interaction between pairwise views. We build a new dataset containing 854 …
The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang
The Vehicle Routing Problem With Simultaneous Pickup And Delivery And Occasional Drivers, Vincent F. Yu, Grace Aloina, Panca Jodiawan, Aldy Gunawan, Tsung-C. Huang
Research Collection School Of Computing and Information Systems
This research addresses the Vehicle Routing Problem with Simultaneous Pickup and Delivery and Occasional Drivers (VRPSPDOD), which is inspired from the importance of addressing product returns and the emerging notion of involving available crowds to perform pickup and delivery activities in exchange for some compensation. At the depot, a set of regular vehicles is available to deliver and/or pick up customers’ goods. A set of occasional drivers, each defined by their origin, destination, and flexibility, is also able to help serve the customers. The objective of VRPSPDOD is to minimize the total traveling cost of operating regular vehicles and total …
Green Data Analytics Of Supercomputing From Massive Sensor Networks: Does Workload Distribution Matter?, Zhiling Guo, Jin Li, Ram Ramesh
Green Data Analytics Of Supercomputing From Massive Sensor Networks: Does Workload Distribution Matter?, Zhiling Guo, Jin Li, Ram Ramesh
Research Collection School Of Computing and Information Systems
Energy costs represent a significant share of the total cost of ownership in high performance computing (HPC) systems. Using a unique data set collected by massive sensor networks in a peta scale national supercomputing center, we first present an explanatory model to identify key factors that affect energy consumption in supercomputing. Our analytic results show that, not only does computing node utilization significantly affect energy consumption, workload distribution among the nodes also has significant effects and could effectively be leveraged to improve energy efficiency. Next, we establish the high model performance using in-sample and out-of-sample analyses. We then develop prescriptive …
Blockscope: Detecting And Investigating Propagated Vulnerabilities In Forked Blockchain Projects, Xiao Yi, Yuzhou Fang, Daoyuan Wu, Lingxiao Jiang
Blockscope: Detecting And Investigating Propagated Vulnerabilities In Forked Blockchain Projects, Xiao Yi, Yuzhou Fang, Daoyuan Wu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Due to the open-source nature of the blockchain ecosystem, it is common for new blockchains to fork or partially reuse the code of classic blockchains. For example, the popular Dogecoin, Litecoin, Binance BSC, and Polygon are all variants of Bitcoin/Ethereum. These “forked” blockchains thus could encounter similar vulnerabilities that are propagated from Bitcoin/Ethereum during forking or subsequently commit fetching. In this paper, we conduct a systematic study of detecting and investigating the propagated vulnerabilities in forked blockchain projects. To facilitate this study, we propose BlockScope, a novel tool that can effectively and efficiently detect multiple types of cloned vulnerabilities given …
Volere: Leakage Resilient User Authentication Based On Personal Voice Challenges, Rui Zhang, Zheng Yan, Xuerui Wang, Robert H. Deng
Volere: Leakage Resilient User Authentication Based On Personal Voice Challenges, Rui Zhang, Zheng Yan, Xuerui Wang, Robert H. Deng
Research Collection School Of Computing and Information Systems
Voiceprint Authentication as a Service (VAaS) offers great convenience due to ubiquity, generality, and usability. Despite its attractiveness, it suffers from user voiceprint leakage over the air or at the cloud, which intrudes user voice privacy and retards its wide adoption. The literature still lacks an effective solution on this issue. Traditional methods based on cryptography are too complex to be practically deployed while other approaches distort user voiceprints, which hinders accurate user identification. In this article, we propose a leakage resilient user authentication cloud service with privacy preservation based on random personal voice challenges, named VOLERE (VOice LEakage REsilient). …
Investigating Noticeable Hand Redirection In Virtual Reality Using Physiological And Interaction Data, Martin Feick, Kora Regitz, Anthony Tang, Jungbluth
Investigating Noticeable Hand Redirection In Virtual Reality Using Physiological And Interaction Data, Martin Feick, Kora Regitz, Anthony Tang, Jungbluth
Research Collection School Of Computing and Information Systems
Hand redirection is effective so long as the introduced offsets are not noticeably disruptive to users. In this work we investigate the use of physiological and interaction data to detect movement discrepancies between a user's real and virtual hand, pushing towards a novel approach to identify discrepancies which are too large and therefore can be noticed. We ran a study with 22 participants, collecting EEG, ECG, EDA, RSP, and interaction data. Our results suggest that EEG and interaction data can be reliably used to detect visuo-motor discrepancies, whereas ECG and RSP seem to suffer from inconsistencies. Our findings also show …
Spatio-Temporal Heterogeneity In The International Trade Resilience During Covid-19, Wei Luo, Lingfeng He, Zihui Yang, Shirui Zhang, Yong Wang, Dianbo Liu, Sheng Hu, Li He, Jizhe Xia, Min Chen
Spatio-Temporal Heterogeneity In The International Trade Resilience During Covid-19, Wei Luo, Lingfeng He, Zihui Yang, Shirui Zhang, Yong Wang, Dianbo Liu, Sheng Hu, Li He, Jizhe Xia, Min Chen
Research Collection School Of Computing and Information Systems
The COVID-19 pandemic and subsequent lockdowns have created immeasurable health and economic crises, leading to unprecedented disruptions to world trade. The COVID-19 pandemic shows diverse impacts on different economies that suffer and recover at different rates and degrees. This research aims to evaluate the spatio-temporal heterogeneity of international trade network vulnerabilities in the current crisis to understand the global production resilience and prepare for the future crisis. We applied a series of complex network analysis approaches to the monthly international trade networks at the world, regional, and country scales for the pre- and post- COVID-19 outbreak period. The spatio-temporal patterns …
Investigating Guardian Awareness Techniques To Promote Safety In Virtual Reality, Sixuan Wu, Jiannan Li, Maurício Sousa, Tovi Grossman
Investigating Guardian Awareness Techniques To Promote Safety In Virtual Reality, Sixuan Wu, Jiannan Li, Maurício Sousa, Tovi Grossman
Research Collection School Of Computing and Information Systems
Virtual Reality (VR) can completely immerse users in a virtual world and provide little awareness of bystanders in the surrounding physical environment. Current technologies use predefined guardian area visualizations to set safety boundaries for VR interactions. However, bystanders cannot perceive these boundaries and may collide with VR users if they accidentally enter guardian areas. In this paper, we investigate four awareness techniques on mobile phones and smartwatches to help bystanders avoid invading guardian areas. These techniques include augmented reality boundary overlays and visual, auditory, and haptic alerts indicating bystanders' distance from guardians. Our findings suggest that the proposed techniques effectively …
Learning And Understanding User Interface Semantics From Heterogeneous Networks With Multimodal And Positional Attributes, Meng Kiat Gary Ang, Ee-Peng Lim
Learning And Understanding User Interface Semantics From Heterogeneous Networks With Multimodal And Positional Attributes, Meng Kiat Gary Ang, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
User interfaces (UI) of desktop, web, and mobile applications involve a hierarchy of objects (e.g., applications, screens, view class, and other types of design objects) with multimodal (e.g., textual and visual) and positional (e.g., spatial location, sequence order, and hierarchy level) attributes. We can therefore represent a set of application UIs as a heterogeneous network with multimodal and positional attributes. Such a network not only represents how users understand the visual layout of UIs but also influences how users would interact with applications through these UIs. To model the UI semantics well for different UI annotation, search, and evaluation tasks, …
Generalizing Graph Neural Network Across Graphs And Time, Zhihao Wen
Generalizing Graph Neural Network Across Graphs And Time, Zhihao Wen
Research Collection School Of Computing and Information Systems
Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. …
Introduction To The Special Issue On Innovation In Transportation-Enabled Urban Services, Part 1, Niels Agatz, Soo-Haeng Cho, Hai Wang, Saif Benjaafar
Introduction To The Special Issue On Innovation In Transportation-Enabled Urban Services, Part 1, Niels Agatz, Soo-Haeng Cho, Hai Wang, Saif Benjaafar
Research Collection School Of Computing and Information Systems
Rapid developments in city infrastructure and technol-ogies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/ econometric analysis, and decision-making tools based …