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Probablistic Verification Of Neural Networks Against Group Fairness, Bing Sun, Jun Sun, Ting Dai, Lijun Zhang Nov 2021

Probablistic Verification Of Neural Networks Against Group Fairness, Bing Sun, Jun Sun, Ting Dai, Lijun Zhang

Research Collection School Of Computing and Information Systems

Fairness is crucial for neural networks which are used in applications with important societal implication. Recently, there have been multiple attempts on improving fairness of neural networks, with a focus on fairness testing (e.g., generating individual discriminatory instances) and fairness training (e.g., enhancing fairness through augmented training). In this work, we propose an approach to formally verify neural networks against fairness, with a focus on independence-based fairness such as group fairness. Our method is built upon an approach for learning Markov Chains from a user-provided neural network (i.e., a feed-forward neural network or a recurrent neural network) which is guaranteed …

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Figcps: Effective Failure-Inducing Input Generation For Cyber-Physical Systems With Deep Reinforcement Learning, Shaohua Zhang, Shuang Liu, Jun Sun, Yuqi Chen, Wenzhi Huang, Jinyi Liu, Jian Liu, Jianye Hao Nov 2021

Figcps: Effective Failure-Inducing Input Generation For Cyber-Physical Systems With Deep Reinforcement Learning, Shaohua Zhang, Shuang Liu, Jun Sun, Yuqi Chen, Wenzhi Huang, Jinyi Liu, Jian Liu, Jianye Hao

Research Collection School Of Computing and Information Systems

Cyber-Physical Systems (CPSs) are composed of computational control logic and physical processes, that intertwine with each other. CPSs are widely used in various domains of daily life, including those safety-critical systems and infrastructures, such as medical monitoring, autonomous vehicles, and water treatment systems. It is thus critical to effectively test them. However, it is not easy to obtain test cases which can fail the CPS. In this work, we propose a failure-inducing input generation approach FIGCPS for CPS, which requires no knowledge of the CPS under test or any history logs of the CPS which are usually hard to obtain. …

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Using Role Play To Develop An Empathetic Mindset In Executive Education, Anuradha Ravi, Kasun Gamlath, Siyan Hu, Archan Misra Nov 2021

Using Role Play To Develop An Empathetic Mindset In Executive Education, Anuradha Ravi, Kasun Gamlath, Siyan Hu, Archan Misra

Research Collection School Of Computing and Information Systems

We describe the practical development of a smart lighting control system, CS-Light, that uses a preexisting surveillance camera infrastructure as the sole sensing substrate. At a high level, the camera feeds are used to both (a) estimate the illuminance of individual, fine-grained (roughly 12m2) sub-regions, and (b) identify sub-regions that have non-transient human occupancy. Subsequently, these estimates are used to perform fine-grained (non-binary) power optimization of a set of LED luminaires, collectively minimizing energy consumption while assuring comfort to human occupants. The key to our approach is the ability to tackle the challenging problem of translating the luminance (pixel intensity) …

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Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin Nov 2021

Expediting The Accuracy-Improving Process Of Svms For Class Imbalance Learning, Bin Cao, Yuqi Liu, Chenyu Hou, Jing Fan, Baihua Zheng, Jianwei Jin

Research Collection School Of Computing and Information Systems

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all …

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Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding Nov 2021

Factual Consistency Evaluation For Text Summarization Via Counterfactual Estimation, Yuexiang Xie, Fei Sun, Yang Deng, Yaliang Li, Bolin Ding

Research Collection School Of Computing and Information Systems

Despite significant progress has been achieved in text summarization, factual inconsistency in generated summaries still severely limits its practical applications. Among the key factors to ensure factual consistency, a reliable automatic evaluation metric is the first and the most crucial one. However, existing metrics either neglect the intrinsic cause of the factual inconsistency or rely on auxiliary tasks, leading to an unsatisfied correlation with human judgments or increasing the inconvenience of usage in practice. In light of these challenges, we propose a novel metric to evaluate the factual consistency in text summarization via counterfactual estimation, which formulates the causal relationship …

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Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam Nov 2021

Aspect-Based Sentiment Analysis In Question Answering Forums, Wenxuan Zhang, Yang Deng, Xin Li, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) typically focuses on extracting aspects and predicting their sentiments on individual sentences such as customer reviews. Recently, another kind of opinion sharing platform, namely question answering (QA) forum, has received increasing popularity, which accumulates a large number of user opinions towards various aspects. This motivates us to investigate the task of ABSA on QA forums (ABSA-QA), aiming to jointly detect the discussed aspects and their sentiment polarities for a given QA pair. Unlike review sentences, a QA pair is composed of two parallel sentences, which requires interaction modeling to align the aspect mentioned in the question …

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Aspect Sentiment Quad Prediction As Paraphrase Generation, Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam Nov 2021

Aspect Sentiment Quad Prediction As Paraphrase Generation, Wenxuan Zhang, Yang Deng, Xin Li, Yifei Yuan, Lidong Bing, Wai Lam

Research Collection School Of Computing and Information Systems

Aspect-based sentiment analysis (ABSA) has been extensively studied in recent years, which typically involves four fundamental sentiment elements, including the aspect category, aspect term, opinion term, and sentiment polarity. Existing studies usually consider the detection of partial sentiment elements, instead of predicting the four elements in one shot. In this work, we introduce the Aspect Sentiment Quad Prediction (ASQP) task, aiming to jointly detect all sentiment elements in quads for a given opinionated sentence, which can reveal a more comprehensive and complete aspect-level sentiment structure. We further propose a novel Paraphrase modeling paradigm to cast the ASQP task to a …

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Efficient Online-Friendly Two-Party Ecdsa Signature, Haiyang Xue, Ho Man Au, Xiang Xie, Hon Tsz Yuen, Handong Cui Nov 2021

Efficient Online-Friendly Two-Party Ecdsa Signature, Haiyang Xue, Ho Man Au, Xiang Xie, Hon Tsz Yuen, Handong Cui

Research Collection School Of Computing and Information Systems

Two-party ECDSA signatures have received much attention due to their widespread deployment in cryptocurrencies. Depending on whether or not the message is required, we could divide two-party signing into two different phases, namely, offline and online. Ideally, the online phase should be made as lightweight as possible. At the same time, the cost of the offline phase should remain similar to that of a normal signature generation. However, the existing two-party protocols of ECDSA are not optimal: either their online phase requires decryption of a ciphertext, or their offline phase needs at least two executions of multiplicative-to-additive conversion which dominates …

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Unlocking The Creativity Potential Of Dialectical Thinking: Field Investigations Of The Comparative Effects Of Transformational And Transactional Leadership Styles, Roy Y. J. Chua, Jia Hui Lim, Wannwiruch (Fon) Wiruchnipawan Nov 2021

Unlocking The Creativity Potential Of Dialectical Thinking: Field Investigations Of The Comparative Effects Of Transformational And Transactional Leadership Styles, Roy Y. J. Chua, Jia Hui Lim, Wannwiruch (Fon) Wiruchnipawan

Research Collection Lee Kong Chian School Of Business

In a digital economy characterized by high volumes of information and ideas, many of which could be contradictory to one another, employees high in dialectical thinking should be well poised to connect disparate ideas to generate creative solutions for business problems. Yet, it is unclear whether dialectical thinking as a creativity-relevant skill can be realized in naturalistic workplace settings, given past mixed findings and the lack of field studies. We propose that supervisors’ leadership styles are important moderators that can unlock employees’ creativity potential in dialectical thinking. Additionally, we compare the activating effect of transformational leadership and the inhibiting effect …

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Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall Nov 2021

Artificial Intelligence As Augmenting Automation: Implications For Employment, F. Ted Tschang, Esteve Almirall

Research Collection Lee Kong Chian School Of Business

There has been great concern in recent years that artificial intelligence (AI) may cause widespread unemployment, but proponents say that AI augments existing jobs. Both of these positions have substance, but there is a need is to articulate the mechanisms by which AI may actually do both, and in the process, transform work and business organizations alike. We use economic studies showing past transformations automation wrought on the structure of employment and skills (such as the favouring of nonroutine skills) to articulate a ground for discussion. We then use case evidence of AI and automation to show how AI is …

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Going Far Together By Being Here Now: Mindfulness Increases Cooperation In Negotiations, Theodore Charles Masters-Waage, Jared Nai, Jochen Reb, Samantha Sim, Jayanth Narayanan, Noriko Tan Nov 2021

Going Far Together By Being Here Now: Mindfulness Increases Cooperation In Negotiations, Theodore Charles Masters-Waage, Jared Nai, Jochen Reb, Samantha Sim, Jayanth Narayanan, Noriko Tan

Research Collection Lee Kong Chian School Of Business

Integrating theorizing across the mindfulness and negotiation literatures, we hypothesize that mindfulness increases cooperation in negotiations. We further propose that processes of self-transcendence, self-regulation, and self-awareness mediate this effect. We test these hypotheses in five studies across different forms of cooperation, in both distributive and integrative negotiation contexts, and for both measured and experimentally induced mindfulness. In Study 1a, individuals higher on measured state mindfulness displayed greater cooperative orientation measured as preference for pareto-optimal agreements. In Study 1b, experimentally induced mindfulness led to greater cooperative orientation measured as the recall of cooperative heuristics. In Study 2, a distributive (fixed-sum) negotiation, …

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Stock Return Prediction Using Financial News: A Unified Sequence Model Based On Hierarchical Attention And Long-Short Term Memory Networks, Haoling Chen, Peng Liu Nov 2021

Stock Return Prediction Using Financial News: A Unified Sequence Model Based On Hierarchical Attention And Long-Short Term Memory Networks, Haoling Chen, Peng Liu

Research Collection Lee Kong Chian School Of Business

Stock return prediction has been a hot topic in both research and industry given its potential for large financial gain. The return signal, apart from its inherent volatility and complexity, is often accompanied by a multitude of noises, such as other stocks’ performance, macroeconomic factors and financial news, etc. To better characterize these factors, we propose a new model that consists of two levels of sequence: an NLP-based module to capture the sequential nature of words and sentences in the financial news, and a time-series-based module to exploit the sequential nature of adjacent observations in the stock price. In this …

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Towards Open Science In India: An Update, Sridhar Gutam Oct 2021

Towards Open Science In India: An Update, Sridhar Gutam

COAR Asia OA Meeting 2021

This COVID19 pandemic has made us understand the importance of Open Science. The science developed through the knowledge contributed should be open so for innovations to happen for the public good. The goods and services which we envisage for the public good can only happen when the data and information are freely available as soon as possible. During the pandemic, India had brought out a draft policy on ‘Science, Technology, and Innovation Policy (STIP) 2020’ which is aimed at making Science more Open. During the presentation, a brief on the STIP 2020 and other developments towards Open Scholarship in India …

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Cashing The Cheque Of Open Access Movement: Emerging Tools Built On Open Access Data, Aaron Tay Oct 2021

Cashing The Cheque Of Open Access Movement: Emerging Tools Built On Open Access Data, Aaron Tay

COAR Asia OA Meeting 2021

The presentation showed that the rising amount of OA full text, coupled with increased availability of open scholarly metadata due to the rise of persistent Identifiers (PID) infrastructure like Crossref, ORCID, Datacite, and Research Organization Registry (ROR) has led to the emergence of new innovative tools and features that leverage these sources of open data. He provided an overview of the range of new tools available based on open access data, which includes discovery indexes like Lens.org, literature mapping tools like Citation Gecko, Litmaps, Inciteful, VOSviewer or new features such as contextual citations and more. These tools are evidence that …

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Open Access And Open Science In Singapore, Lian-Ping Ooi Oct 2021

Open Access And Open Science In Singapore, Lian-Ping Ooi

COAR Asia OA Meeting 2021

Developments and update of Open Access and Open Science in Singapore.

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The Unknown Benefits Of Black Markets, Singapore Management University Oct 2021

The Unknown Benefits Of Black Markets, Singapore Management University

Perspectives@SMU

Research on the license plate market in China’s capital finds that black markets can potentially serve as an auction mechanism to redistribute limited resources to those in need

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Transporting Causal Mechanisms For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang Oct 2021

Transporting Causal Mechanisms For Unsupervised Domain Adaptation, Zhongqi Yue, Qianru Sun, Xian-Sheng Hua, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Existing Unsupervised Domain Adaptation (UDA) literature adopts the covariate shift and conditional shift assumptions, which essentially encourage models to learn common features across domains. However, due to the lack of supervision in the target domain, they suffer from the semantic loss: the feature will inevitably lose nondiscriminative semantics in source domain, which is however discriminative in target domain. We use a causal view—transportability theory [41]—to identify that such loss is in fact a confounding effect, which can only be removed by causal intervention. However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the …

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A Large-Scale Benchmark For Food Image Segmentation, Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C. H. Hoi, Qianru Sun Oct 2021

A Large-Scale Benchmark For Food Image Segmentation, Xiongwei Wu, Xin Fu, Ying Liu, Ee-Peng Lim, Steven C. H. Hoi, Qianru Sun

Research Collection School Of Computing and Information Systems

Food image segmentation is a critical and indispensible task for developing health-related applications such as estimating food calories and nutrients. Existing food image segmentation models are underperforming due to two reasons: (1) there is a lack of high quality food image datasets with fine-grained ingredient labels and pixel-wise location masks—the existing datasets either carry coarse ingredient labels or are small in size; and (2) the complex appearance of food makes it difficult to localize and recognize ingredients in food images, e.g., the ingredients may overlap one another in the same image, and the identical ingredient may appear distinctly in different …

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An Exploratory Study Of Social Support Systems To Help Older Adults In Managing Mobile Safety, Tamir Mendel, Debin Gao, David Lo, Eran Toch Oct 2021

An Exploratory Study Of Social Support Systems To Help Older Adults In Managing Mobile Safety, Tamir Mendel, Debin Gao, David Lo, Eran Toch

Research Collection School Of Computing and Information Systems

Older adults face increased safety challenges, such as targeted online fraud and phishing, contributing to the growing technological divide between them and younger adults. Social support from family and friends is often the primary way older adults receive help, but it may also lead to reliance on others. We have conducted an exploratory study to investigate older adults' attitudes and experiences related to mobile social support technologies for mobile safety. We interviewed 18 older adults about their existing support and used the think-aloud method to gather data about a prototype for providing social support during mobile safety challenges. Our findings …

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Solarslam: Battery-Free Loop Closure For Indoor Localisation, Bo Wei, Weitao Xu, Chengwen Luo, Guillaume Zoppi, Dong Ma, Sen Wang Oct 2021

Solarslam: Battery-Free Loop Closure For Indoor Localisation, Bo Wei, Weitao Xu, Chengwen Luo, Guillaume Zoppi, Dong Ma, Sen Wang

Research Collection School Of Computing and Information Systems

In this paper, we propose SolarSLAM, a batteryfree loop closure method for indoor localisation. Inertial Measurement Unit (IMU) based indoor localisation method has been widely used due to its ubiquity in mobile devices, such as mobile phones, smartwatches and wearable bands. However, it suffers from the unavoidable long term drift. To mitigate the localisation error, many loop closure solutions have been proposed using sophisticated sensors, such as cameras, laser, etc. Despite achieving high-precision localisation performance, these sensors consume a huge amount of energy. Different from those solutions, the proposed SolarSLAM takes advantage of an energy harvesting solar cell as a …

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Noahqa: Numerical Reasoning With Interpretable Graph Question Answering Dataset, Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim Oct 2021

Noahqa: Numerical Reasoning With Interpretable Graph Question Answering Dataset, Qiyuan Zhang, Lei Wang, Sicheng Yu, Shuohang Wang, Yang Wang, Jing Jiang, Ee-Peng Lim

Research Collection School Of Computing and Information Systems

While diverse question answering (QA) datasets have been proposed and contributed significantly to the development of deep learning models for QA tasks, the existing datasets fall short in two aspects. First, we lack QA datasets covering complex questions that involve answers as well as the reasoning processes to get the answers. As a result, the state-of-the-art QA research on numerical reasoning still focuses on simple calculations and does not provide the mathematical expressions or evidences justifying the answers. Second, the QA community has contributed much effort to improving the interpretability of QA models. However, these models fail to explicitly show …

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Burst-Induced Multi-Armed Bandit For Learning Recommendation, Rodrigo Alves, Antoine Ledent, Marius Kloft Oct 2021

Burst-Induced Multi-Armed Bandit For Learning Recommendation, Rodrigo Alves, Antoine Ledent, Marius Kloft

Research Collection School Of Computing and Information Systems

In this paper, we introduce a non-stationary and context-free Multi-Armed Bandit (MAB) problem and a novel algorithm (which we refer to as BMAB) to solve it. The problem is context-free in the sense that no side information about users or items is needed. We work in a continuous-time setting where each timestamp corresponds to a visit by a user and a corresponding decision regarding recommendation. The main novelty is that we model the reward distribution as a consequence of variations in the intensity of the activity, and thereby we assist the exploration/exploitation dilemma by exploring the temporal dynamics of the …

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Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua Oct 2021

Towards Enriching Responses With Crowd-Sourced Knowledge For Task-Oriented Dialogue, Yingxu He, Lizi Liao, Zheng Zhang, Tat-Seng Chua

Research Collection School Of Computing and Information Systems

Task-oriented dialogue agents are built to assist users in completing various tasks. Generating appropriate responses for satisfactory task completion is the ultimate goal. Hence, as a convenient and straightforward way, metrics such as success rate, inform rate etc., have been widely leveraged to evaluate the generated responses. However, beyond task completion, there are several other factors that largely affect user satisfaction, which remain under-explored. In this work, we focus on analyzing different agent behavior patterns that lead to higher user satisfaction scores. Based on the findings, we design a neural response generation model EnRG. It naturally combines the power of …

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Non-Gaap Earnings And Stock Price Crash Risk, Charles Hsu, Rencheng Wang, Benjamin C. Whipple Oct 2021

Non-Gaap Earnings And Stock Price Crash Risk, Charles Hsu, Rencheng Wang, Benjamin C. Whipple

Research Collection School Of Accountancy

We investigate whether non-GAAP earnings disclosures increase stock price crash risk. Consistent with non-GAAP disclosures allowing managers to inflate investors’ perceptions about firm performance, our results indicate that income increasing non-GAAP reporting increases crash risk. We also find that managers can use non-GAAP reporting as a substitute for earnings management to withhold bad news from investors (the traditional explanation for crashes). Finally, we find a positive association between non-GAAP reporting and the likelihood of subsequent events that can trigger a crash. Overall, our evidence is consistent with some non-GAAP disclosures exposing investors to risks of large and sudden price declines.

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Occluded Person Re-Identification With Single-Scale Global Representations, Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen Oct 2021

Occluded Person Re-Identification With Single-Scale Global Representations, Cheng Yan, Guansong Pang, Jile Jiao, Xiao Bai, Xuetao Feng, Chunhua Shen

Research Collection School Of Computing and Information Systems

Occluded person re-identification (ReID) aims at re-identifying occluded pedestrians from occluded or holistic images taken across multiple cameras. Current state-of-the-art (SOTA) occluded ReID models rely on some auxiliary modules, including pose estimation, feature pyramid and graph matching modules, to learn multi-scale and/or part-level features to tackle the occlusion challenges. This unfortunately leads to complex ReID models that (i) fail to generalize to challenging occlusions of diverse appearance, shape or size, and (ii) become ineffective in handling non-occluded pedestrians. However, real-world ReID applications typically have highly diverse occlusions and involve a hybrid of occluded and non-occluded pedestrians. To address these two …

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Investor-State Mediation And The Belt And Road Initiative: Examining The Conditions For Settlement, Mark Mclaughlin Oct 2021

Investor-State Mediation And The Belt And Road Initiative: Examining The Conditions For Settlement, Mark Mclaughlin

Research Collection Yong Pung How School Of Law

Despite the dominance of arbitration in the realm of investor-state disputes, the variety of proposals for reform suggest considerable stakeholder discontent with the current framework. One suggested reform is the introduction of investor–state mediation, which has been supported by the conclusion of the Singapore Convention on Mediation and the proposal by the International Centre for Settlement of Investment Disputes (ICSID) of a set of mediation rules. This article examines the respective merits of arbitration and mediation to settle investment disputes related to the Belt and Road Initiative. Many of the principles underpinning the implementation of the Belt and Road Initiative …

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Smart Contract Development: Challenges And Opportunities, Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach D. Le, Xin Xia, Yang Feng, Zhenyu Chen, Baowen Xu Oct 2021

Smart Contract Development: Challenges And Opportunities, Weiqin Zou, David Lo, Pavneet Singh Kochhar, Xuan-Bach D. Le, Xin Xia, Yang Feng, Zhenyu Chen, Baowen Xu

Research Collection School Of Computing and Information Systems

Smart contract, a term which was originally coined to refer to the automation of legal contracts in general, has recently seen much interest due to the advent of blockchain technology. Recently, the term is popularly used to refer to low-level code scripts running on a blockchain platform. Our study focuses exclusively on this subset of smart contracts. Such smart contracts have increasingly been gaining ground, finding numerous important applications (e.g., crowdfunding) in the real world. Despite the increasing popularity, smart contract development still remains somewhat a mystery to many developers largely due to its special design and applications. Are there …

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Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang Oct 2021

Causal Attention For Unbiased Visual Recognition, Tan Wang, Chang Zhou, Qianru Sun, Hanwang Zhang

Research Collection School Of Computing and Information Systems

Attention module does not always help deep models learn causal features that are robust in any confounding context, e.g., a foreground object feature is invariant to different backgrounds. This is because the confounders trick the attention to capture spurious correlations that benefit the prediction when the training and testing data are IID (identical & independent distribution); while harm the prediction when the data are OOD (out-of-distribution). The sole fundamental solution to learn causal attention is by causal intervention, which requires additional annotations of the confounders, e.g., a “dog” model is learned within “grass+dog” and “road+dog” respectively, so the “grass” and …

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Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong Oct 2021

Disentangling Hate In Online Memes, Ka Wei, Roy Lee, Rui Cao, Ziqing Fan, Jing Jiang, Wen Haw Chong

Research Collection School Of Computing and Information Systems

Hateful and offensive content detection has been extensively explored in a single modality such as text. However, such toxic information could also be communicated via multimodal content such as online memes. Therefore, detecting multimodal hateful content has recently garnered much attention in academic and industry research communities. This paper aims to contribute to this emerging research topic by proposing DisMultiHate, which is a novel framework that performed the classification of multimodal hateful content. Specifically, DisMultiHate is designed to disentangle target entities in multimodal memes to improve the hateful content classification and explainability. We conduct extensive experiments on two publicly available …

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Contrasting Third-Party Package Management User Experience, Syful Islam, Raula Kula, Christoph Treude, Takashi Ishio, Kenichi Matsumoto Oct 2021

Contrasting Third-Party Package Management User Experience, Syful Islam, Raula Kula, Christoph Treude, Takashi Ishio, Kenichi Matsumoto

Research Collection School Of Computing and Information Systems

The management of third-party package dependencies is crucial to most technology stacks, with package managers acting as brokers to ensure that a verified package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of package ecosystems with their own management features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that package ecosystems are used without any issue. In this study, we explore 13 package ecosystems to understand whether their features correlate with the experience of their users. By studying experience through the questions that …

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