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 2911 - 2940 of 8597
Full-Text Articles in Entire DC Network
Contextual Transformation Networks For Online Continual Learning, Quang Pham, Chenghao Liu, Doyen Sahoo, Steve C. H. Hoi
Contextual Transformation Networks For Online Continual Learning, Quang Pham, Chenghao Liu, Doyen Sahoo, Steve C. H. Hoi
Research Collection School Of Computing and Information Systems
Continual learning methods with fixed architectures rely on a single network to learn models that can perform well on all tasks. As a result, they often only accommodate common features of those tasks but neglect each task's specific features. On the other hand, dynamic architecture methods can have a separate network for each task, but they are too expensive to train and not scalable in practice, especially in online settings. To address this problem, we propose a novel online continual learning method named ``Contextual Transformation Networks” (CTN) to efficiently model the \emph{task-specific features} while enjoying neglectable complexity overhead compared to …
Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua
Dialogue State Tracking With Incremental Reasoning, Lizi Liao, Le Hong Long, Yunshan Ma, Wenqiang Lei, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Tracking dialogue states to better interpret user goals and feed downstream policy learning is a bottleneck in dialogue management. Common practice has been to treat it as a problem of classifying dialogue content into a set of pre-defined slot-value pairs, or generating values for different slots given the dialogue history. Both have limitations on considering dependencies that occur on dialogues, and are lacking of reasoning capabilities. This paper proposes to track dialogue states gradually with reasoning over dialogue turns with the help of the back-end data. Empirical results demonstrate that our method outperforms the state-of-theart methods in terms of joint …
Low-Power Downlink For The Internet Of Things Using Ieee 802.11-Compliant Wake-Up Receivers, Johannes Blobel, Vu Huy Tran, Archan Misra, Falko Dressler
Low-Power Downlink For The Internet Of Things Using Ieee 802.11-Compliant Wake-Up Receivers, Johannes Blobel, Vu Huy Tran, Archan Misra, Falko Dressler
Research Collection School Of Computing and Information Systems
Ultra-low power communication is critical for supporting the next generation of battery-operated or energy harvesting battery-less Internet of Things (IoT) devices. Duty cycling protocols and wake-up receiver (WuRx) technologies, and their combinations, have been investigated as energy-efficient mechanisms to support selective, event-driven activation of devices. In this paper, we go one step further and show how WuRx can be used for an efficient and multi-purpose low power downlink (LPD) communication channel. We demonstrate how to (a) extend the wake-up signal to support low-power flexible and extensible unicast, multicast, and broadcast downlink communication and (b) utilize the WuRx-based LPD to also …
Androevolve: Automated Update For Android Deprecated-Api Usages, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller
Androevolve: Automated Update For Android Deprecated-Api Usages, Stefanus A. Haryono, Ferdian Thung, David Lo, Lingxiao Jiang, Julia Lawall, Hong Jin Kang, Lucas Serrano, Gilles Muller
Research Collection School Of Computing and Information Systems
The Android operating system (OS) is often updated, where each new version may involve API deprecation. Usages of deprecated APIs in Android apps need to be updated to ensure the apps' compatibility with the old and new versions of the Android OS. In this work, we propose AndroEvolve, an automated tool to update usages of deprecated Android APIs, that addresses the limitations of the state-of-the-art tool, CocciEvolve. AndroEvolve utilizes data flow analysis to solve the problem of out-of-method-boundary variables, and variable denormalization to remove the temporary variables introduced by CocciEvolve. We evaluated the accuracy of AndroEvolve using a dataset of …
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
A Matheuristic Algorithm For The Vehicle Routing Problem With Cross-Docking, Aldy Gunawan, Audrey Tedja Widjaja, Pieter Vansteenwegen, Vincent F. Yu
Research Collection School Of Computing and Information Systems
This paper studies the integration of the vehicle routing problem with cross-docking (VRPCD). The aim is to find a set of routes to deliver products from a set of suppliers to a set of customers through a cross-dock facility, such that the operational and transportation costs are minimized, without violating the vehicle capacity and time horizon constraints. A two-phase matheuristic based on column generation is proposed. The first phase focuses on generating a set of feasible candidate routes in both pickup and delivery processes by implementing an adaptive large neighborhood search algorithm. A set of destroy and repair operators are …
Characterization And Prediction Of Questions Without Accepted Answers On Stack Overflow, Mohamad Yazdaninia, David Lo, Ashkan Sami
Characterization And Prediction Of Questions Without Accepted Answers On Stack Overflow, Mohamad Yazdaninia, David Lo, Ashkan Sami
Research Collection School Of Computing and Information Systems
A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is …
Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng
Robot: Robustness-Oriented Testing For Deep Learning Systems, Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng
Research Collection School Of Computing and Information Systems
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, where adversarial examples (a.k.a. bugs) of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the commonly used neuron coverage metrics by existing DL testing approaches are not correlated to model robustness. It is also not an effective measurement on the confidence of the model robustness after testing. In this work, we address this gap by …
Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Parachuri, Akshat Kumar
Action Selection For Composable Modular Deep Reinforcement Learning, Vaibhav Gupta, Daksh Anand, Praveen Parachuri, Akshat Kumar
Research Collection School Of Computing and Information Systems
In modular reinforcement learning (MRL), a complex decision making problem is decomposed into multiple simpler subproblems each solved by a separate module. Often, these subproblems have conflicting goals, and incomparable reward scales. A composable decision making architecture requires that even the modules authored separately with possibly misaligned reward scales can be combined coherently. An arbitrator should consider different module’s action preferences to learn effective global action selection. We present a novel framework called GRACIAS that assigns fine-grained importance to the different modules based on their relevance in a given state, and enables composable decision making based on modern deep RL …
A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding
A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding
Research Collection School Of Computing and Information Systems
Code instrumentation and hardware based event trapping are two primary approaches used in dynamic malware analysis systems. In this paper, we propose a new approach called Execution Flow Instrumentation (EFI) where the analyzer execution flow is interleaved with the target flow in user- and kernel-mode, at junctures flexibly chosen by the analyzer at runtime. We also propose OASIS as the system infrastructure to realize EFI with virtues of the current two approaches, however without their drawbacks. Despite being securely and transparently isolated from the target, the analyzer introspects and controls it in the same native way as instrumentation code. We …
Automated Query Reformulation For Efficient Search Based On Query Logs From Stack Overflow, Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen
Automated Query Reformulation For Efficient Search Based On Query Logs From Stack Overflow, Kaibo Cao, Chunyang Chen, Sebastian Baltes, Christoph Treude, Xiang Chen
Research Collection School Of Computing and Information Systems
As a popular Q&A site for programming, Stack Overflow is a treasure for developers. However, the amount of questions and answers on Stack Overflow make it difficult for developers to efficiently locate the information they are looking for. There are two gaps leading to poor search results: the gap between the user's intention and the textual query, and the semantic gap between the query and the post content. Therefore, developers have to constantly reformulate their queries by correcting misspelled words, adding limitations to certain programming languages or platforms, etc. As query reformulation is tedious for developers, especially for novices, we …
How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude
How Do Software Developers Use Github Actions To Automate Their Workflows?, Timothy Kinsman, Mairieli Wessel, Marco Gerosa, Christoph Treude
Research Collection School Of Computing and Information Systems
Automated tools are frequently used in social coding repositories to perform repetitive activities that are part of the distributed software development process. Recently, GitHub introduced GitHub Actions, a feature providing automated work-flows for repository maintainers. Although several Actions have been built and used by practitioners, relatively little has been done to evaluate them. Understanding and anticipating the effects of adopting such kind of technology is important for planning and management. Our research is the first to investigate how developers use Actions and how several activity indicators change after their adoption. Our results indicate that, although only a small subset of …
Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude
Characterising The Knowledge About Primitive Variables In Java Code Comments, Mahfouth Alghamdi, Shinpei Hayashi, Takashi Kobayashi, Christoph Treude
Research Collection School Of Computing and Information Systems
Primitive types are fundamental components available in any programming language, which serve as the building blocks of data manipulation. Understanding the role of these types in source code is essential to write software. Little work has been conducted on how often these variables are documented in code comments and what types of knowledge the comments provide about variables of primitive types. In this paper, we present an approach for detecting primitive variables and their description in comments using lexical matching and advanced matching. We evaluate our approaches by comparing the lexical and advanced matching performance in terms of recall, precision, …
Solving 3d Bin Packing Problem Via Multimodal Deep Reinforcement Learning, Yuan Jiang, Zhiguang Cao, Jie Zhang
Solving 3d Bin Packing Problem Via Multimodal Deep Reinforcement Learning, Yuan Jiang, Zhiguang Cao, Jie Zhang
Research Collection School Of Computing and Information Systems
Recently, there is growing attention on applying deep reinforcement learning (DRL) to solve the 3D bin packing problem (3D BPP), given its favorable generalization and independence of ground-truth label. However, due to the relatively less informative yet computationally heavy encoder, and considerably large action space inherent to the 3D BPP, existing methods are only able to handle up to 50 boxes. In this paper, we propose to alleviate this issue via an end-to-end multimodal DRL agent, which sequentially addresses three sub-tasks of sequence, orientation and position, respectively. The resulting architecture enables the agent to solve large-scale instances of 100 boxes …
Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang
Automatic Solution Summarization For Crash Bugs, Haoye Wang, Xin Xia, David Lo, John C. Grundy, Xinyu Wang
Research Collection School Of Computing and Information Systems
The causes of software crashes can be hidden anywhere in the source code and development environment. When encountering software crashes, recurring bugs that are discussed on Q&A sites could provide developers with solutions to their crashing problems. However, it is difficult for developers to accurately search for relevant content on search engines, and developers have to spend a lot of manual effort to find the right solution from the returned results. In this paper, we present CRASOLVER, an approach that takes into account both the structural information of crash traces and the knowledge of crash-causing bugs to automatically summarize solutions …
Infercode: Self-Supervised Learning Of Code Representations By Predicting Subtrees, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Infercode: Self-Supervised Learning Of Code Representations By Predicting Subtrees, Duy Quoc Nghi Bui, Yijun Yu, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Learning code representations has found many uses in software engineering, such as code classification, code search, code comment generation, and bug prediction. Although representations of code in tokens, syntax trees, dependency graphs, paths in trees, or the combinations of their variants have been proposed, existing learning techniques have a major limitation that these models are often trained on datasets labeled for specific downstream tasks, and the code representations may not be suitable for other tasks. Even though some techniques generate representations from unlabeled code, they are far from satisfactory when applied to downstream tasks. To overcome the limitation, this paper …
On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik
On The Root Of Trust Identification Problem, Ivan De Oliveira Nunes, Xuhua Ding, Gene Tsudik
Research Collection School Of Computing and Information Systems
Trusted Execution Environments (TEEs) are becoming ubiquitous and are currently used in many security applications: from personal IoT gadgets to banking and databases. Prominent examples of such architectures are Intel SGX, ARM TrustZone, and Trusted Platform Modules (TPMs). A typical TEE relies on a dynamic Root of Trust (RoT) to provide security services such as code/data confidentiality and integrity, isolated secure software execution, remote attestation, and sensor auditing. Despite their usefulness, there is currently no secure means to determine whether a given security service or task is being performed by the particular RoT within a specific physical device. We refer …
Learning Index Policies For Restless Bandits With Application To Maternal Healthcare, Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe
Learning Index Policies For Restless Bandits With Application To Maternal Healthcare, Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe
Research Collection School Of Computing and Information Systems
In many community health settings, it is crucial to have a systematic monitoring and intervention process to ensure that the patients adhere to healthcare programs, such as periodic health checks or taking medications. When these interventions are expensive, they can be provided to only a fixed small fraction of the patients at any period of time. Hence, it is important to carefully choose the beneficiaries who should be provided with interventions and when. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention …
Ship-Gan: Generative Modeling Based Maritime Traffic Simulator, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar
Ship-Gan: Generative Modeling Based Maritime Traffic Simulator, Chaithanya Basrur, Arambam James Singh, Arunesh Sinha, Akshat Kumar
Research Collection School Of Computing and Information Systems
Modeling vessel movement in a maritime environment is an extremely challenging task given the complex nature of vessel behavior. Several existing multiagent maritime decision making frameworks require access to an accurate traffic simulator. We develop a system using electronic navigation charts to generate realistic and high fidelity vessel traffic data using Generative Adversarial Networks (GANs). Our proposed Ship-GAN uses a conditional Wasserstein GAN to model a vessel's behavior. The generator can simulate the travel time of vessels across different maritime zones conditioned on vessels' speeds and traffic intensity. Furthermore, it can be used as an accurate simulator for prior decision …
Automatic Web Testing Using Curiosity-Driven Reinforcement Learning, Yan Zheng, Yi Liu, Xiaofei Xie, Yepang Liu, Lei Ma, Jianye Hao, Yang Liu
Automatic Web Testing Using Curiosity-Driven Reinforcement Learning, Yan Zheng, Yi Liu, Xiaofei Xie, Yepang Liu, Lei Ma, Jianye Hao, Yang Liu
Research Collection School Of Computing and Information Systems
Web testing has long been recognized as a notoriously difficult task. Even nowadays, web testing still heavily relies on manual efforts while automated web testing is far from achieving human-level performance. Key challenges in web testing include dynamic content update and deep bugs hiding under complicated user interactions and specific input values, which can only be triggered by certain action sequences in the huge search space. In this paper, we propose WebExplor, an automatic end-to-end web testing framework, to achieve an adaptive exploration of web applications. WebExplor adopts curiosity-driven reinforcement learning to generate high-quality action sequences (test cases) satisfying temporal …
A Visual Analytics Approach To Facilitate The Proctoring Of Online Exams, Haotian Li, Min Xu, Yong Wang, Huan Wei, Huamin Qu
A Visual Analytics Approach To Facilitate The Proctoring Of Online Exams, Haotian Li, Min Xu, Yong Wang, Huan Wei, Huamin Qu
Research Collection School Of Computing and Information Systems
Online exams have become widely used in recent years to evaluate students’ performance in mastering the knowledge, especially during the pandemic of COVID-19. However, it is challenging to conduct proctoring for online exams due to the lack of face-to-face interactions. Also, prior research has shown that online exams are more vulnerable to various cheating behaviors, which can damage the credibility of online exams. In this paper, we present a novel vi- sual analytics approach to facilitate the proctoring of online exams by analyzing the exam video records and mouse movement data of each student. Specifically, we detect and visualize suspected …
Leveraging Multiple Relations For Fashion Trend Forecasting Based On Social Media, Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, Tat-Seng Chua
Leveraging Multiple Relations For Fashion Trend Forecasting Based On Social Media, Yujuan Ding, Yunshan Ma, Lizi Liao, Wai Keung Wong, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
—Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion …
Angrybert: Joint Learning Target And Emotion For Hate Speech Detection, Md Rabiul Awal, Rui Cao, Roy Ka-Wei Lee, Sandra Mitrović
Angrybert: Joint Learning Target And Emotion For Hate Speech Detection, Md Rabiul Awal, Rui Cao, Roy Ka-Wei Lee, Sandra Mitrović
Research Collection School Of Computing and Information Systems
Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. However, most of the existing methods adopt a supervised approach that depended heavily on the annotated hate speech datasets, which are imbalanced and often lack training samples for hateful content. This paper addresses the research gaps by proposing a novel multitask learning-based model, AngryBERT, which jointly learns hate speech detection with sentiment classification and target identification as secondary relevant tasks. We conduct extensive experiments to augment three commonly-used hate speech detection datasets. Our experiment …
Effect Of Augmented Reality On Consumer Behavior In E-Commerce, Chibuke Uzoechina, Fiona Fui-Hoon Nah
Effect Of Augmented Reality On Consumer Behavior In E-Commerce, Chibuke Uzoechina, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
AR technology has been referred to as the future of e-commerce. In this paper, we propose to investigate the effect of AR on consumer behavior in e-commerce. Based on the theory of telepresence, we generated a set of hypotheses on their relationships. An experimental study is proposed to test the hypotheses. Our findings will be of interest to e-commerce companies that are looking at incorporating AR into their e-commerce platforms.
Adaptive Operating Hours For Improved Performance Of Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng
Adaptive Operating Hours For Improved Performance Of Taxi Fleets, Rajiv Ranjan Kumar, Pradeep Varakantham, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Taxi fleets and car aggregation systems are an important component of the urban public transportation system. Taxis and cars in taxi fleets and car aggregation systems (e.g., Uber) are dependent on a large number of self-controlled and profit-driven taxi drivers, which introduces inefficiencies in the system. There are two ways in which taxi fleet performance can be optimized: (i) Operational decision making: improve assignment of taxis/cars to customers, while accounting for future demand; (ii) strategic decision making: optimize operating hours of (taxi and car) drivers. Existing research has primarily focused on the operational decisions in (i) and we focus on …
Fine-Grained And Controllably Redactable Blockchain With Harmful Data Forced Removal, Huiying Hou, Shidi Hao, Jiaming Yuan, Shengmin Xu, Yunlei Zhao
Fine-Grained And Controllably Redactable Blockchain With Harmful Data Forced Removal, Huiying Hou, Shidi Hao, Jiaming Yuan, Shengmin Xu, Yunlei Zhao
Research Collection School Of Computing and Information Systems
Notoriously, immutability is one of the most striking properties of blockchains. As the data contained in blockchains may be compelled to redact for personal and legal reasons, immutability needs to be skillfully broken. In most existing redactable blockchains, fine-grained redaction and effective deletion of harmful data are mutually exclusive. To close the gap, we propose a fine-grained and controllably redactable blockchain with harmful data forced removal. In the scheme, the originator of the transaction has fine-grained control over who can perform the redaction and which portions of the transaction can be redacted. The redaction transaction is performed after collecting enough …
Edgeduet: Tiling Small Object Detection For Edge Assisted Autonomous Mobile Vision, Xu Wang, Zheng Yang, Jiahang Wu, Yi Zhao, Zimu Zhou
Edgeduet: Tiling Small Object Detection For Edge Assisted Autonomous Mobile Vision, Xu Wang, Zheng Yang, Jiahang Wu, Yi Zhao, Zimu Zhou
Research Collection School Of Computing and Information Systems
Accurate, real-time object detection on resource-constrained devices enables autonomous mobile vision applications such as traffic surveillance, situational awareness, and safety inspection, where it is crucial to detect both small and large objects in crowded scenes. Prior studies either perform object detection locally on-board or offload the task to the edge/cloud. Local object detection yields low accuracy on small objects since it operates on low-resolution videos to fit in mobile memory. Offloaded object detection incurs high latency due to uploading high-resolution videos to the edge/cloud. Rather than either pure local processing or offloading, we propose to detect large objects locally while …
Immigrant Families' Health-Related Information Behavior On Instant Messaging Platforms: Health-Related Information Exchange In Immigrant Family Groups On Instant Messaging Platforms, Lev Poretski, Taamannae Taabassum, Anthony Tang
Immigrant Families' Health-Related Information Behavior On Instant Messaging Platforms: Health-Related Information Exchange In Immigrant Family Groups On Instant Messaging Platforms, Lev Poretski, Taamannae Taabassum, Anthony Tang
Research Collection School Of Computing and Information Systems
For immigrant families, instant messaging family groups are a common platform for sharing and discussing health-related information. Immigrants often maintain contact with their family abroad and trust information in shared IM family groups more than the information from local authorities and sources. In this study, we aimed to understand health-related information behaviors of immigrant families in their IM family groups. Based on the interviews with 6 participants from immigrant families to Canada, we found that immigrant families’ discourse on IM platforms is motivated by love and care for other family members. The families used local and international sources of information, …
More Kawaii Than A Real-Person Live Streamer: Understanding How The Otaku Community Engages With And Perceives Virtual Youtubers, Zhicong Lu, Chenxinran Shen, Jiannan Li, Hong Shen, Daniel Wigdor
More Kawaii Than A Real-Person Live Streamer: Understanding How The Otaku Community Engages With And Perceives Virtual Youtubers, Zhicong Lu, Chenxinran Shen, Jiannan Li, Hong Shen, Daniel Wigdor
Research Collection School Of Computing and Information Systems
Live streaming has become increasingly popular, with most streamers presenting their real-life appearance. However, Virtual YouTubers (VTubers), virtual 2D or 3D avatars that are voiced by humans, are emerging as live streamers and attracting a growing viewership in East Asia. Although prior research has found that many viewers seek real-life interpersonal interactions with real-person streamers, it is currently unknown what makes VTuber live streams engaging or how they are perceived differently than real-person streamers. We conducted an interview study to understand how viewers engage with VTubers and perceive the identities of the voice actors behind the avatars (i.e., Nakanohito). The …
Visuo-Haptic Illusions For Linear Translation And Stretching Using Physical Proxies In Virtual Reality, Martin Feick, Niko Kleer, André Zenner, Anthony Tang, Antonio Kruger
Visuo-Haptic Illusions For Linear Translation And Stretching Using Physical Proxies In Virtual Reality, Martin Feick, Niko Kleer, André Zenner, Anthony Tang, Antonio Kruger
Research Collection School Of Computing and Information Systems
Providing haptic feedback when manipulating virtual objects is an essential part of immersive virtual reality experiences; however, it is challenging to replicate all of an object’s properties and characteristics. We propose the use of visuo-haptic illusions alongside physical proxies to enhance the scope of proxy-based interactions with virtual objects. In this work, we focus on two manipulation techniques, linear translation and stretching across different distances, and investigate how much discrepancy between the physical proxy and the virtual object may be introduced without participants noticing. In a study with 24 participants, we found that manipulation technique and travel distance significantly affect …
Business-Driven Technical Debt Prioritization: An Industrial Case Study, Rodrigo Rebouças De Almeida, Rafael Do Nascimento Ribeiro, Christoph Treude, Uirá Kulesza
Business-Driven Technical Debt Prioritization: An Industrial Case Study, Rodrigo Rebouças De Almeida, Rafael Do Nascimento Ribeiro, Christoph Treude, Uirá Kulesza
Research Collection School Of Computing and Information Systems
Incorporating the business perspective into prioritizing technical debt is essential to contribute to decision making in industry. In this paper, we evolve and evaluate a businessdriven approach for technical debt prioritization. The approach was evaluated during a five-months industrial case study with business and technical stakeholders’ active participation. The results show that the approach contributed to aligning business criteria between the business and technical stakeholders. We also observed a downward trend in the amount of technical debt that affects high-value business assets. Moreover, we identified eight business factors that affect the decision making related to the prioritization of technical debt. …