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Mitigating Industry Contagion Effects From Financial Reporting Fraud: A Competitive Dynamics Perspective Of Non-Errant Rival Firms Exploiting Product-Market Opportunities, Eugene Kang, Nongnapat Thosuwanchot, David Gomulya Nov 2023

Mitigating Industry Contagion Effects From Financial Reporting Fraud: A Competitive Dynamics Perspective Of Non-Errant Rival Firms Exploiting Product-Market Opportunities, Eugene Kang, Nongnapat Thosuwanchot, David Gomulya

Research Collection Lee Kong Chian School Of Business

Existing studies show that financial reporting frauds by errant firms cause declines in stock market valuations for non-errant rival firms (i.e. industry contagion effects). We posit that contagion effects may be mitigated by investors’ expectations of non-errant rivals exploiting product-market opportunities at the expense of errant firms. We apply the competitive dynamics literature to argue that non-errant rivals experience lower contagion effects when they have more available slack to engage in competitive actions. This effect is expected to strengthen when rival firms have previously deployed more resources for research and development and advertising investments or have higher prior market share …

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Going Beyond The Call Of Duty Under Conditions Of Economic Threat: Integrating Life History And Temporal Dilemma Perspectives, Nina Sirola Nov 2023

Going Beyond The Call Of Duty Under Conditions Of Economic Threat: Integrating Life History And Temporal Dilemma Perspectives, Nina Sirola

Research Collection Lee Kong Chian School Of Business

Under conditions of economic threat, such as during economic downturns, organizations can benefit from employees’ willingness to go beyond the call of duty and engage in organization-directed citizenship behavior (OCBO). Yet, such behavior is discretionary and competes for time with employees’ other interests and priorities. I integrate life history theory with the temporal dilemma perspective on organizational citizenship behavior to propose that childhood environments sensitize individuals to prioritize different goals in response to economic threat later in life. Consistent with strategies for responding to threat that are functional in their childhood environments, employees from wealthier backgrounds respond to economic threat …

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On The Trajectory Of Discrimination: A Meta-Analysis And Forecasting Survey Capturing 44 Years Of Field Experiments On Gender And Hiring Decisions, Michael Schaerer, Christilene Du Plessis, My Hoang Nguyen, Robbie C. M. Van Aert, Leo Tiokkin, Daniel Lakens, Elena G. Clemente, Thomas Pfeiffer, Anna Dreber, Magnus Johannesson, Cory J. Clark Nov 2023

On The Trajectory Of Discrimination: A Meta-Analysis And Forecasting Survey Capturing 44 Years Of Field Experiments On Gender And Hiring Decisions, Michael Schaerer, Christilene Du Plessis, My Hoang Nguyen, Robbie C. M. Van Aert, Leo Tiokkin, Daniel Lakens, Elena G. Clemente, Thomas Pfeiffer, Anna Dreber, Magnus Johannesson, Cory J. Clark

Research Collection Lee Kong Chian School Of Business

A preregistered meta-analysis, including 244 effect sizes from 85 field audits and 361,645 individual job applications, tested for gender bias in hiring practices in female-stereotypical and gender-balanced as well as male-stereotypical jobs from 1976 to 2020. A “red team” of independent experts was recruited to increase the rigor and robustness of our meta-analytic approach. A forecasting survey further examined whether laypeople (n = 499 nationally representative adults) and scientists (n = 312) could predict the results. Forecasters correctly anticipated reductions in discrimination against female candidates over time. However, both scientists and laypeople overestimated the continuation of bias against female candidates. …

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Revisiting Disentanglement And Fusion On Modality And Context In Conversational Multimodal Emotion Recognition, Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li Nov 2023

Revisiting Disentanglement And Fusion On Modality And Context In Conversational Multimodal Emotion Recognition, Bobo Li, Hao Fei, Lizi Liao, Yu Zhao, Chong Teng, Tat-Seng Chua, Donghong Ji, Fei Li

Research Collection School Of Computing and Information Systems

It has been a hot research topic to enable machines to understand human emotions in multimodal contexts under dialogue scenarios, which is tasked with multimodal emotion analysis in conversation (MM-ERC). MM-ERC has received consistent attention in recent years, where a diverse range of methods has been proposed for securing better task performance. Most existing works treat MM-ERC as a standard multimodal classification problem and perform multimodal feature disentanglement and fusion for maximizing feature utility. Yet after revisiting the characteristic of MM-ERC, we argue that both the feature multimodality and conversational contextualization should be properly modeled simultaneously during the feature disentanglement …

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Public Communication Of Audit Risks And Related-Party Transactions: Evidence From China, Ole-Kristian Hope, Heng Yue, Qinlin Zhong Nov 2023

Public Communication Of Audit Risks And Related-Party Transactions: Evidence From China, Ole-Kristian Hope, Heng Yue, Qinlin Zhong

Research Collection School Of Accountancy

This paper examines whether and how firms' engagement in related-party transactions (RPTs) is shaped by public communication of audit risks as required by the expanded audit report. Using the phased regulatory changes in China and a difference-in-differences design with firm fixed effects and matching, we find that firms significantly reduce their RPTs after the adoption of expanded audit reports (EARs). To investigate potential mechanisms, we find that (1) investor scrutiny increases after the adoption of EARs, (2) the reduction of RPTs is more pronounced when EARs are more likely to attract investor attention, and (3) the reduction of RPTs is …

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After Great Pain: The Uses Of Religious Folklore In Kenji Mizoguchi’S Sansho The Bailiff (Jp 1954) And Kaneto Shindo’S Onibaba (Jp 1964), Teng-Kuan Ng Nov 2023

After Great Pain: The Uses Of Religious Folklore In Kenji Mizoguchi’S Sansho The Bailiff (Jp 1954) And Kaneto Shindo’S Onibaba (Jp 1964), Teng-Kuan Ng

Research Collection College of Integrative Studies

This article studies the adaptations and applications of religious folklore in two mas-terworks of Japanese cinema: Kenji Mizoguchi’s Sansho Dayu (Sansho the Bailiff, JP 1954) and Kaneto Shindo’s Onibaba (JP 1964). While academic approaches will often draw a strict line between narrative genres and discursive forms, these films, I argue, draw creatively from Japanese tradition for both critical and constructive purposes in the postwar context. Besides mounting trenchant criticisms of Japan’s erstwhile militaristic violence and imperial ambitions, both filmmakers present their respective female protagonists as models for spiritual and sociocultural transformation in the face of anomie. Embodying humanistic compassion on …

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Enhancing Classification Of Energy Meters With Limited Labels Using A Semi-Supervised Generative Model, Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller Nov 2023

Enhancing Classification Of Energy Meters With Limited Labels Using A Semi-Supervised Generative Model, Chun Fu, Hussain Kazmi, Matias Quintana, Clayton Miller

Research Collection College of Integrative Studies

In the energy domain, the classification of power meters has become an increasingly significant area of interest, such as appliance identification and characteristics prediction, enabling targeted and efficient energy management. However, the limited availability of labeled data for power meters and the inconsistencies in labeling and naming conventions have constrained the potential of metadata for further application. This study aims to bridge the gap by employing semi-supervised Generative Adversarial Networks (SGAN) to classify 1805 power meters distributed globally. This approach explores and assesses the advantages of incorporating unlabeled power meter data into the learning process. A comparative analysis is performed …

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Financial Crisis And Female Entrepreneurship: Evidence From South Korea, Jungho Lee, Sunha Myong Nov 2023

Financial Crisis And Female Entrepreneurship: Evidence From South Korea, Jungho Lee, Sunha Myong

Research Collection School Of Economics

We document a drastic increase in female-owned manufacturing firms in South Korea after the 1997 financial crisis. During the crisis, a major banking sector reform was conducted, and many underperforming bank branches were forced to close down. Using a geographical variation of bank branch closures during the reform, we show that the banking sector reform resulted in a rise in female entrepreneurship. We present evidence that male-owned firms were preferred by the closeddown bank branches, despite female-owned firms exhibiting lower risks and higher returns. The banking sector reform, although not explicitly aimed at addressing gender disparities, substantially benefited female entrepreneurs …

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Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua Nov 2023

Constructing Holistic Spatio-Temporal Scene Graph For Video Semantic Role Labeling, Yu Zhao, Hao Fei, Yixin Cao, Bobo Li, Meishan Zhang, Jianguo Wei, Min Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

As one of the core video semantic understanding tasks, Video Semantic Role Labeling (VidSRL) aims to detect the salient events from given videos, by recognizing the predict-argument event structures and the interrelationships between events. While recent endeavors have put forth methods for VidSRL, they can be mostly subject to two key drawbacks, including the lack of fine-grained spatial scene perception and the insufficiently modeling of video temporality. Towards this end, this work explores a novel holistic spatio-temporal scene graph (namely HostSG) representation based on the existing dynamic scene graph structures, which well model both the fine-grained spatial semantics and temporal …

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Privacy-Preserving Bloom Filter-Based Keyword Search Over Large Encrypted Cloud Data, Yanrong Liang, Jianfeng Ma, Yinbin Miao, Da Kuang, Xiangdong Meng, Robert H. Deng Nov 2023

Privacy-Preserving Bloom Filter-Based Keyword Search Over Large Encrypted Cloud Data, Yanrong Liang, Jianfeng Ma, Yinbin Miao, Da Kuang, Xiangdong Meng, Robert H. Deng

Research Collection School Of Computing and Information Systems

To achieve the search over encrypted data in cloud server, Searchable Encryption (SE) has attracted extensive attention from both academic and industrial fields. The existing Bloom filter-based SE schemes can achieve similarity search, but will generally incur high false positive rates, and even leak the privacy of values in Bloom filters (BF). To solve the above problems, we first propose a basic Privacy-preserving Bloom filter-based Keyword Search scheme using the Circular Shift and Coalesce-Bloom Filter (CSC-BF) and Symmetric-key Hidden Vector Encryption (SHVE) technology (namely PBKS), which can achieve effective search while protecting the values in BFs. Then, we design a …

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Quantumeyes: Towards Better Interpretability Of Quantum Circuits, Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang Nov 2023

Quantumeyes: Towards Better Interpretability Of Quantum Circuits, Shaolun Ruan, Qiang Guan, Paul Griffin, Ying Mao, Yong Wang

Research Collection School Of Computing and Information Systems

Quantum computing offers significant speedup compared to classical computing, which has led to a growing interest among users in learning and applying quantum computing across various applications. However, quantum circuits, which are fundamental for implementing quantum algorithms, can be challenging for users to understand due to their underlying logic, such as the temporal evolution of quantum states and the effect of quantum amplitudes on the probability of basis quantum states. To fill this research gap, we propose QuantumEyes, an interactive visual analytics system to enhance the interpretability of quantum circuits through both global and local levels. For the global-level analysis, …

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An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le Nov 2023

An Idealist’S Approach For Smart Contract Correctness, Duy Tai Nguyen, Hong Long Pham, Jun Sun, Quang Loc Le

Research Collection School Of Computing and Information Systems

In this work, we experiment an idealistic approach for smart contract correctness verification and enforcement, based on the assumption that developers are either desired or required to provide a correctness specification due to the importance of smart contracts and the fact that they are immutable after deployment. We design a static verification system with a specification language which supports fully compositional verification (with the help of function specifications, contract invariants, loop invariants and call invariants). Our approach has been implemented in a tool named iContract which automatically proves the correctness of a smart contract statically or checks the unverified part …

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Npf-200: A Multi-Modal Eye Fixation Dataset And Method For Non-Photorealistic Videos, Ziyu Yang, Sucheng Ren, Zongwei Wu, Nanxuan Zhao, Junle Wang, Jing Qin, Shengfeng He Nov 2023

Npf-200: A Multi-Modal Eye Fixation Dataset And Method For Non-Photorealistic Videos, Ziyu Yang, Sucheng Ren, Zongwei Wu, Nanxuan Zhao, Junle Wang, Jing Qin, Shengfeng He

Research Collection School Of Computing and Information Systems

Non-photorealistic videos are in demand with the wave of the metaverse, but lack of sufficient research studies. This work aims to take a step forward to understand how humans perceive nonphotorealistic videos with eye fixation (i.e., saliency detection), which is critical for enhancing media production, artistic design, and game user experience. To fill in the gap of missing a suitable dataset for this research line, we present NPF-200, the first largescale multi-modal dataset of purely non-photorealistic videos with eye fixations. Our dataset has three characteristics: 1) it contains soundtracks that are essential according to vision and psychological studies; 2) it …

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Followupqg: Towards Information-Seeking Follow-Up Question Generation, Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan Nov 2023

Followupqg: Towards Information-Seeking Follow-Up Question Generation, Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan

Research Collection School Of Computing and Information Systems

Humans ask follow-up questions driven by curiosity, which reflects a creative human cognitive process. We introduce the task of realworld information-seeking follow-up question generation (FQG), which aims to generate follow-up questions seeking a more in-depth understanding of an initial question and answer. We construct FOLLOWUPQG, a dataset1 of over 3K real-world (initial question, answer, follow-up question) tuples collected from a Reddit forum providing layman-friendly explanations for open-ended questions. In contrast to existing datasets, questions in FOLLOWUPQG use more diverse pragmatic strategies to seek information, and they also show higher-order cognitive skills (such as applying and relating). We evaluate current question …

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Opportunities For Spatial Database Research In The Context Of Preference Queries, Kyriakos Mouratidis Nov 2023

Opportunities For Spatial Database Research In The Context Of Preference Queries, Kyriakos Mouratidis

Research Collection School Of Computing and Information Systems

This is the outline of the keynote speech at LocalRec@ACM SIGSPATIAL 2023. The main objective of the talk is to point out opportunities for spatial database researchers in the area of preference-based querying. We will commence with an overview of the standard queries for multi-objective decision making, and demonstrate their direct connection to recommendations and to market analysis. In this context, there is a number of specific decision criteria, and user preferences are represented as vectors with as many dimensions. We will demonstrate how and why this type of preferences are natural to actual applications and practical for the support …

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Krover: A Symbolic Execution Engine For Dynamic Kernel Analysis, Pansilu Madhura Bhashana Pitigalaarachchi Pitigala Arachchillage, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang Nov 2023

Krover: A Symbolic Execution Engine For Dynamic Kernel Analysis, Pansilu Madhura Bhashana Pitigalaarachchi Pitigala Arachchillage, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

We present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, …

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Faire: Repairing Fairness Of Neural Networks Via Neuron Condition Synthesis, Tianlin Li, Xiaofei Xie, Jian Wang, Qing Guo, Aishan Liu, Lei Ma, Yang Liu Nov 2023

Faire: Repairing Fairness Of Neural Networks Via Neuron Condition Synthesis, Tianlin Li, Xiaofei Xie, Jian Wang, Qing Guo, Aishan Liu, Lei Ma, Yang Liu

Research Collection School Of Computing and Information Systems

Deep Neural Networks (DNNs) have achieved tremendous success in many applications, while it has been demonstrated that DNNs can exhibit some undesirable behaviors on concerns such as robustness, privacy, and other trustworthiness issues. Among them, fairness (i.e., non-discrimination) is one important property, especially when they are applied to some sensitive applications (e.g., finance and employment). However, DNNs easily learn spurious correlations between protected attributes (e.g., age, gender, race) and the classification task and develop discriminatory behaviors if the training data is imbalanced. Such discriminatory decisions in sensitive applications would introduce severe social impacts. To expose potential discrimination problems in DNNs …

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Laf: Labeling-Free Model Selection For Automated Deep Neural Network Reusing, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon Nov 2023

Laf: Labeling-Free Model Selection For Automated Deep Neural Network Reusing, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Mike Papadakis, Yves Le Traon

Research Collection School Of Computing and Information Systems

pplying deep learning (DL) to science is a new trend in recent years, which leads DL engineering to become an important problem. Although training data preparation, model architecture design, and model training are the normal processes to build DL models, all of them are complex and costly. Therefore, reusing the open-sourced pre-trained model is a practical way to bypass this hurdle for developers. Given a specific task, developers can collect massive pre-trained deep neural networks from public sources for reusing. However, testing the performance (e.g., accuracy and robustness) of multiple deep neural networks (DNNs) and recommending which model should be …

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Verifytl: Secure And Verifiable Collaborative Transfer Learning, Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Wei Zheng, Kim-Kwang Raymond Choo, Robert H. Deng Nov 2023

Verifytl: Secure And Verifiable Collaborative Transfer Learning, Zhuoran Ma, Jianfeng Ma, Yinbin Miao, Ximeng Liu, Wei Zheng, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

Getting access to labeled datasets in certain sensitive application domains can be challenging. Hence, one may resort to transfer learning to transfer knowledge learned from a source domain with sufficient labeled data to a target domain with limited labeled data. However, most existing transfer learning techniques only focus on one-way transfer which may not benefit the source domain. In addition, there is the risk of a malicious adversary corrupting a number of domains, which can consequently result in inaccurate prediction or privacy leakage. In this paper, we construct a secure and Verif iable collaborative T ransfer L earning scheme, VerifyTL, …

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Autolog: A Log Sequence Synthesis Framework For Anomaly Detection, Yintong Huo, Yichen Li, Yuxin Su, Pinjia He, Zifan Xie, R. Michael Lyu Nov 2023

Autolog: A Log Sequence Synthesis Framework For Anomaly Detection, Yintong Huo, Yichen Li, Yuxin Su, Pinjia He, Zifan Xie, R. Michael Lyu

Research Collection School Of Computing and Information Systems

The rapid progress of modern computing systems has led to a growing interest in informative run-time logs. Various log-based anomaly detection techniques have been proposed to ensure software reliability. However, their implementation in the industry has been limited due to the lack of high-quality public log resources as training datasets.While some log datasets are available for anomaly detection, they suffer from limitations in (1) comprehensiveness of log events; (2) scalability over diverse systems; and (3) flexibility of log utility. To address these limitations, we propose AutoLog, the first automated log generation methodology for anomaly detection. AutoLog uses program analysis to …

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Cgt-Gan: Clip-Guided Text Gan For Image Captioning, Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He Nov 2023

Cgt-Gan: Clip-Guided Text Gan For Image Captioning, Jiarui Yu, Haoran Li, Yanbin Hao, Bin Zhu, Tong Xu, Xiangnan He

Research Collection School Of Computing and Information Systems

The large-scale visual-language pre-trained model, Contrastive Language-Image Pre-training (CLIP), has significantly improved image captioning for scenarios without human-annotated image-caption pairs. Recent advanced CLIP-based image captioning without human annotations follows a text-only training paradigm, i.e., reconstructing text from shared embedding space. Nevertheless, these approaches are limited by the training/inference gap or huge storage requirements for text embeddings. Given that it is trivial to obtain images in the real world, we propose CLIP-guided text GAN (CgT-GAN), which incorporates images into the training process to enable the model to "see" real visual modality. Particularly, we use adversarial training to teach CgT-GAN to mimic …

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Multimodal Fashion Knowledge Extraction As Captioning, Yifei Yuan, Wenxuan Zhang, Yang Deng, Wai Lam Nov 2023

Multimodal Fashion Knowledge Extraction As Captioning, Yifei Yuan, Wenxuan Zhang, Yang Deng, Wai Lam

Research Collection School Of Computing and Information Systems

Social media plays a significant role in boosting the fashion industry, where a massive amount of fashion-related posts are generated every day. In order to obtain the rich fashion information from the posts, we study the task of social media fashion knowledge extraction. Fashion knowledge, which typically consists of the occasion, person attributes, and fashion item information, can be effectively represented as a set of tuples. Most previous studies on fashion knowledge extraction are based on the fashion product images without considering the rich text information in social media posts. Existing work on fashion knowledge extraction in social media is …

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Impartiality And The Construction Of Trust In Investor-State Dispute Settlement, Stavros Brekoulakis, Anna Howard Oct 2023

Impartiality And The Construction Of Trust In Investor-State Dispute Settlement, Stavros Brekoulakis, Anna Howard

Singapore International Dispute Resolution Academy

This article analyses impartiality in investor-State dispute settlement (ISDS) by identifying the way that the parties’ trust in arbitrators is constructed. Drawing on the findings of a large-scale empirical project, it questions the applicability of an orthodox judicial doctrine of impartiality to ISDS on the grounds that trust in arbitrators is constructed on a fundamentally different basis from that of trust in judges. The primary feature of a judicial doctrine of impartiality is that trust is founded on an absolutist approach to impartiality which is intended to ensure that judges have no predispositions to parties. In contrast, trust in ISDS …

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Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu Oct 2023

Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency’s pseudo-anonymous nature makes it vulnerable to malicious activities. However, existing deep learning solutions lack interpretability and only support retrospective analysis of specific malice types. To address these challenges, we propose Intention-Monitor for early malice detection in Bitcoin. Our model, utilizing Decision-Tree based feature Selection and Complement (DT-SC), builds different feature sets for different malice types. The Status Proposal Module (SPM) and hierarchical self-attention predictor provide real-time global status and address label predictions. A survival module determines the stopping point and proposes the status sequence (intention). Our model detects various malicious activities with strong interpretability, outperforming state-of-the-art methods in extensive …

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The Future Can't Help Fix The Past: Assessing Program Repair In The Wild, Vinay Kabadi, Dezhen Kong, Siyu Xie, Lingfeng Bao, Gede Artha Azriadi Prana, Tien Duy B. Le, Xuan Bach D. Le, David Lo Oct 2023

The Future Can't Help Fix The Past: Assessing Program Repair In The Wild, Vinay Kabadi, Dezhen Kong, Siyu Xie, Lingfeng Bao, Gede Artha Azriadi Prana, Tien Duy B. Le, Xuan Bach D. Le, David Lo

Research Collection School Of Computing and Information Systems

Automated program repair (APR) has been gaining ground with substantial effort devoted to the area, opening up many challenges and opportunities. One such challenge is that the state-of-the-art repair techniques often resort to incomplete specifications, e.g., test cases that witness buggy behavior, to generate repairs. In practice, bug-exposing test cases are often available when: (1) developers, at the same time of (or after) submitting bug fixes, create the tests to assure the correctness of the fixes, or (2) regression errors occur. The former case – a scenario commonly used for creating popular bug datasets – however, may not be suitable …

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Wait, Wasn't That Code Here Before? Detecting Outdated Software Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude Oct 2023

Wait, Wasn't That Code Here Before? Detecting Outdated Software Documentation, Wen Siang Tan, Markus Wagner, Christoph Treude

Research Collection School Of Computing and Information Systems

Encountering outdated documentation is not a rare occurrence for developers and users in the software engineering community. To ensure that software documentation is up-to-date, developers often have to manually check whether the documentation needs to be updated whenever changes are made to the source code. In our previous work, we proposed an approach to automatically detect outdated code element references in software repositories and found that more than a quarter of the 1000 most popular projects on GitHub contained at least one outdated reference. In this paper, we present a GitHub Actions tool that builds on our previous work’s approach …

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Underwater Image Translation Via Multi-Scale Generative Adversarial Network, Dongmei Yang, Tianzi Zhang, Boquan Li, Menghao Li, Weijing Chen, Xiaoqing Li, Xingmei Wang Oct 2023

Underwater Image Translation Via Multi-Scale Generative Adversarial Network, Dongmei Yang, Tianzi Zhang, Boquan Li, Menghao Li, Weijing Chen, Xiaoqing Li, Xingmei Wang

Research Collection School Of Computing and Information Systems

The role that underwater image translation plays assists in generating rare images for marine applications. However, such translation tasks are still challenging due to data lacking, insufficient feature extraction ability, and the loss of content details. To address these issues, we propose a novel multi-scale image translation model based on style-independent discriminators and attention modules (SID-AM-MSITM), which learns the mapping relationship between two unpaired images for translation. We introduce Convolution Block Attention Modules (CBAM) to the generators and discriminators of SID-AM-MSITM to improve its feature extraction ability. Moreover, we construct style-independent discriminators that enable the discriminant results of SID-AM-MSITM to …

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Masked Diffusion Transformer Is A Strong Image Synthesizer, Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan Oct 2023

Masked Diffusion Transformer Is A Strong Image Synthesizer, Shanghua Gao, Pan Zhou, Ming-Ming Cheng, Shuicheng Yan

Research Collection School Of Computing and Information Systems

Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process. To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs’ ability to contextual relation learning among object semantic parts in an image. During training, MDT operates in the latent space to mask certain tokens. Then, an asymmetric masking diffusion transformer is designed to predict masked tokens from unmasked ones while maintaining the diffusion …

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Problems In Microservice Development: Supporting Visualisation, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner Oct 2023

Problems In Microservice Development: Supporting Visualisation, Oscar Manglaras, Alex Farkas, Peter Fule, Christoph Treude, Markus Wagner

Research Collection School Of Computing and Information Systems

In microservice architectures, developers can face significant problems understanding the structure of the system and how the different microservices interact. This difficulty results from the distributed nature of the system, and the abundance of inter-service communication within the architecture. We want to determine if network visualisations can address these problems given their ability to convey complex topologies. However, to identify what architectural characteristics should be visualised, and how this should be done, we must first determine the needs of microservice developers. This paper identifies and presents the impact and frequency of problems faced by a cohort of microservice developers using …

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Prirpt: Practical Blockchain-Based Privacy-Preserving Reporting System With Rewards, Rui. Shi, Yang Yang, Huamin. Feng, Feng. Yuan, Huiqin. Xie, Jianyi. Zhang Oct 2023

Prirpt: Practical Blockchain-Based Privacy-Preserving Reporting System With Rewards, Rui. Shi, Yang Yang, Huamin. Feng, Feng. Yuan, Huiqin. Xie, Jianyi. Zhang

Research Collection School Of Computing and Information Systems

In order to obtain evidence of a crime timely, most authorities encourage whistleblowers to provide valuable reports by rewarding them with prizes. However, criminals will try their best to delete or tamper with the reports and even threaten and revenge the whistleblowers to escape punishment. Hence, to make the reporting system work, it is essential to ensure the integrity of reported messages and the anonymity of the reporting and rewarding procedures in the reporting system. Most existing schemes for this problem are generally based on ring signatures, which incur high computational overhead and imperfect anonymity. In this paper, we introduce …

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