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Articles 991 - 1020 of 4266
Full-Text Articles in Entire DC Network
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, …
Reference-Based Screentone Transfer Via Pattern Correspondence And Regularization, Zhansheng Li, Nanxuan Zhao, Zongwei Wu, Yihua Dai, Junle Wang, Yanqing Jing, Shengfeng He
Reference-Based Screentone Transfer Via Pattern Correspondence And Regularization, Zhansheng Li, Nanxuan Zhao, Zongwei Wu, Yihua Dai, Junle Wang, Yanqing Jing, Shengfeng He
Research Collection School Of Computing and Information Systems
Adding screentone to initial line drawings is a crucial step for manga generation, but is a tedious and human-laborious task. In this work, we propose a novel data-driven method aiming to transfer the screentone pattern from a reference manga image. This not only ensures the quality, but also adds controllability to the generated manga results. The reference-based screentone translation task imposes several unique challenges. Since manga image often contains multiple screentone patterns interweaved with line drawing, as an abstract art, this makes it even more difficult to extract disentangled style code from the reference. Also, finding correspondence for mapping between …
Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu
Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu
Research Collection School Of Computing and Information Systems
Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, ., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text ., …
Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia
Design, Development, And Evaluation Of An Interactive Personalized Social Robot To Monitor And Coach Post-Stroke Rehabilitation Exercises, Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermudez I Badia
Research Collection School Of Computing and Information Systems
Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these systems still remains a challenge. In this paper, we present our work of iteratively engaging therapists and post-stroke survivors to design, develop, and evaluate a social robot exercise coaching system for personalized rehabilitation. Through interviews with therapists, we designed how this system interacts with the user and then developed an interactive …
Rntrajrec: Road Network Enhanced Trajectory Recovery With Spatial-Temporal Trans-Former, Yuqi Chen, Hanyuan Zhang, Weiwei Sun, Baihua Zheng
Rntrajrec: Road Network Enhanced Trajectory Recovery With Spatial-Temporal Trans-Former, Yuqi Chen, Hanyuan Zhang, Weiwei Sun, Baihua Zheng
Research Collection School Of Computing and Information Systems
GPS trajectories are the essential foundations for many trajectory-based applications. Most applications require a large number of high sample rate trajectories to achieve a good performance. However, many real-life trajectories are collected with low sample rate due to energy concern or other constraints. We study the task of trajectory recovery in this paper as a means to increase the sample rate of low sample trajectories. Most existing works on trajectory recovery follow a sequence-to-sequence diagram, with an encoder to encode a trajectory and a decoder to recover real GPS points in the trajectory. However, these works ignore the topology of …
Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee
Bubbleu: Exploring Augmented Reality Game Design With Uncertain Ai-Based Interaction, Minji Kim, Kyungjin Lee, Rajesh Krishna Balan, Youngki Lee
Research Collection School Of Computing and Information Systems
Object detection, while being an attractive interaction method for Augmented Reality (AR), is fundamentally error-prone due to the probabilistic nature of the underlying AI models, resulting in sub-optimal user experiences. In this paper, we explore the effect of three game design concepts, Ambiguity, Transparency, and Controllability, to provide better gameplay experiences in AR games that use error-prone object detection-based interaction modalities. First, we developed a base AR pet breeding game, called Bubbleu that uses object detection as a key interaction method. We then implemented three different variants, each according to the three concepts, to investigate the impact of each design …
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 …
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 …
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.
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 …
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 …
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 …
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 …
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 …
Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meeralakshmi Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra
Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meeralakshmi Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra
Research Collection School Of Computing and Information Systems
We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve …
Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo
Exploring And Repairing Gender Fairness Violations In Word Embedding-Based Sentiment Analysis Model Through Adversarial Patches, Lin Sze Khoo, Jia Qi Bay, Ming Lee Kimberly Yap, Mei Kuan Lim, Chun Yong Chong, Zhou Yang, David Lo
Research Collection School Of Computing and Information Systems
With the advancement of sentiment analysis (SA) models and their incorporation into our daily lives, fairness testing on these models is crucial, since unfair decisions can cause discrimination to a large population. Nevertheless, some challenges in fairness testing include the unknown oracle, the difficulty in generating suitable test inputs, and the lack of a reliable way of fixing the issues. To fill in these gaps, BiasRV, a tool based on metamorphic testing (MT), was introduced and succeeded in uncovering fairness issues in a transformer-based model. However, the extent of unfairness in other SA models has not been thoroughly investigated. Our …
Automatic Identification Of Crash-Inducing Smart Contracts, Chao Ni, Cong Tian, Kaiwen Yang, David Lo, Jiachi Chen, Xiaohu Yang
Automatic Identification Of Crash-Inducing Smart Contracts, Chao Ni, Cong Tian, Kaiwen Yang, David Lo, Jiachi Chen, Xiaohu Yang
Research Collection School Of Computing and Information Systems
Smart contract, a special software code running on and resided in the blockchain, enlarges the general application of blockchain and exchanges assets without dependence of external parties. With blockchain's characteristic of immutability, they cannot be modified once deployed. Thus, the contract and the records are persisted on the blockchain forever, including failed transactions that are caused by runtime errors and result in the waste of computation, storage, and fees. In this paper, we refer to smart contracts which will cause runtime errors as crash-inducing smart contracts. However, automatic identification of crash-inducing smart contracts is limited investigated in the literature. The …
Demystifying Performance Regressions In String Solvers, Yao Zhang, Xiaofei Xie, Yi Li, Yi Lin, Sen Chen, Yang Liu, Xiaohong Li
Demystifying Performance Regressions In String Solvers, Yao Zhang, Xiaofei Xie, Yi Li, Yi Lin, Sen Chen, Yang Liu, Xiaohong Li
Research Collection School Of Computing and Information Systems
Over the past few years, SMT string solvers have found their applications in an increasing number of domains, such as program analyses in mobile and Web applications, which require the ability to reason about string values. A series of research has been carried out to find quality issues of string solvers in terms of its correctness and performance. Yet, none of them has considered the performance regressions happening across multiple versions of a string solver. To fill this gap, in this paper, we focus on solver performance regressions (SPRs), i.e., unintended slowdowns introduced during the evolution of string solvers. To …
Specification-Based Autonomous Driving System Testing, Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M. Poskitt, Yang Liu, Zijiang Yang
Specification-Based Autonomous Driving System Testing, Yuan Zhou, Yang Sun, Yun Tang, Yuqi Chen, Jun Sun, Christopher M. Poskitt, Yang Liu, Zijiang Yang
Research Collection School Of Computing and Information Systems
Autonomous vehicle (AV) systems must be comprehensively tested and evaluated before they can be deployed. High-fidelity simulators such as CARLA or LGSVL allow this to be done safely in very realistic and highly customizable environments. Existing testing approaches, however, fail to test simulated AVs systematically, as they focus on specific scenarios and oracles (e.g., lane following scenario with the "no collision" requirement) and lack any coverage criteria measures. In this paper, we propose AVUnit, a framework for systematically testing AV systems against customizable correctness specifications. Designed modularly to support different simulators, AVUnit consists of two new languages for specifying dynamic …
Concept-Oriented Transformers For Visual Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw
Concept-Oriented Transformers For Visual Sentiment Analysis, Quoc Tuan Truong, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
In the richly multimedia Web, detecting sentiment signals expressed in images would support multiple applications, e.g., measuring customer satisfaction from online reviews, analyzing trends and opinions from social media. Given an image, visual sentiment analysis aims at recognizing positive or negative sentiment, and occasionally neutral sentiment as well. A nascent yet promising direction is Transformer-based models applied to image data, whereby Vision Transformer (ViT) establishes remarkable performance on largescale vision benchmarks. In addition to investigating the fitness of ViT for visual sentiment analysis, we further incorporate concept orientation into the self-attention mechanism, which is the core component of Transformer. The …
Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meera Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra
Wearables For In-Situ Monitoring Of Cognitive States: Challenges And Opportunities, Meera Radhakrishnan, Thivya Kandappu, Manoj Gulati, Archan Misra
Research Collection School Of Computing and Information Systems
We propose using wrist and ear-based sensing, via multiple novel and complementary modalities, to unobtrusively infer activity-aware, complex cognitive and affective states (such as confusion, boredom, and recall failure) of individuals. While state-of-the-art wearable devices are predominantly used (a) independently, with limited coordination among multiple devices, and (b) to capture macro-level physical activity and physiological state, we seek to expand the ambit of unobtrusive wearable sensing to capture the cognitive states while performing commonplace physical activities. Such states typically manifest via fine-grained, almost unobservable, microscopic head, face, and eye movements. We identify some of these fine-grained physical markers that serve …
Multi-Modal Api Recommendation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo
Multi-Modal Api Recommendation, Ivana Clairine Irsan, Ting Zhang, Ferdian Thung, Kisub Kim, David Lo
Research Collection School Of Computing and Information Systems
Too many options can be a problem, which is the case for Application Programming Interfaces (APIs). As there are many such APIs, with many more being introduced periodically, it raises the problem of choosing which API to be recommended. Furthermore, numerous APIs are commonly used together with other complementary third-party APIs. It can be challenging for developers to understand how to use each API and to remember all the complementary APIs for the API they want to use. Therefore, an accurate API recommendation approach can improve developers' efficiency in implementing certain functionality. Several approaches have been developed to automatically recommend …