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Full-Text Articles in Entire DC Network
Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?, Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li
Distance Based Image Classification: A Solution To Generative Classification’S Conundrum?, Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li
Research Collection School Of Computing and Information Systems
Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory’s [25] hierarchical generative process and non-semantic factors by …
Contextual Path Retrieval: A Contextual Entity Relation Embedding-Based Approach, Pei-Chi Lo, Ee-Peng Lim
Contextual Path Retrieval: A Contextual Entity Relation Embedding-Based Approach, Pei-Chi Lo, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Contextual path retrieval (CPR) refers to the task of finding contextual path(s) between a pair of entities in a knowledge graph that explains the connection between them in a given context. For this novel retrieval task, we propose the Embedding-based Contextual Path Retrieval (ECPR) framework. ECPR is based on a three-component structure that includes a context encoder and path encoder that encode query context and path, respectively, and a path ranker that assigns a ranking score to each candidate path to determine the one that should be the contextual path. For context encoding, we propose two novel context encoding methods, …
Relation Preserving Triplet Mining For Stabilising The Triplet Loss In Re-Identification Systems, Adhiraj Ghosh, Kuruparan Shanmugalingam, Wen-Yan Lin
Relation Preserving Triplet Mining For Stabilising The Triplet Loss In Re-Identification Systems, Adhiraj Ghosh, Kuruparan Shanmugalingam, Wen-Yan Lin
Research Collection School Of Computing and Information Systems
Object appearances change dramatically with pose variations. This creates a challenge for embedding schemes that seek to map instances with the same object ID to locations that are as close as possible. This issue becomes significantly heightened in complex computer vision tasks such as re-identification(reID). In this paper, we suggest that these dramatic appearance changes are indications that an object ID is composed of multiple natural groups, and it is counterproductive to forcefully map instances from different groups to a common location. This leads us to introduce Relation Preserving Triplet Mining (RPTM), a feature matching guided triplet mining scheme, that …
Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel
Human-Centred Artificial Intelligence In The Banking Sector, Krishnaraj Arul Obuchettiar, Alan @ Ali Madjelisi Megargel
Research Collection School Of Computing and Information Systems
Changes in technology have shaped how corporate and retail businesses have evolved, alongside the customers’ preferences. The advent of smart digital devices and social media has shaped how consumers interact and transact with their financial institutions over the past two decades. With the rapid evolution of new technologies and customers' growing preference for digital engagement with financial institutions, organizations need to adopt and align with emerging technologies that support speed, accuracy, efficiency, and security in a user-friendly manner. Today, consumers want hyper-personalized interactions that are more frequent and proactive. Moreover, financial institutions have a growing need to cater to consumers' …
Ranked Keyword Search Over Encrypted Cloud Data Through Machine Learning Method, Yinbin Miao, Wei Zheng, Xiaohua Jia, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng
Ranked Keyword Search Over Encrypted Cloud Data Through Machine Learning Method, Yinbin Miao, Wei Zheng, Xiaohua Jia, Ximeng Liu, Kim-Kwang Raymond Choo, Robert H. Deng
Research Collection School Of Computing and Information Systems
Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, …
T-Counter: Trustworthy And Efficient Cpu Resource Measurement Using Sgx In The Cloud, Chuntao Dong, Qingni Shen, Xuhua Ding, Daoqing Yu, Wu Luo, Pengfei Wu, Zhonghai Wu
T-Counter: Trustworthy And Efficient Cpu Resource Measurement Using Sgx In The Cloud, Chuntao Dong, Qingni Shen, Xuhua Ding, Daoqing Yu, Wu Luo, Pengfei Wu, Zhonghai Wu
Research Collection School Of Computing and Information Systems
As cloud services have become popular, and their adoption is growing, consumers are becoming more concerned about the cost of cloud services. Cloud Service Providers (CSPs) generally use a pay-per-use billing scheme in the cloud services model: consumers use resources as they needed and are billed for their resource usage. However, CSPs are untrusted and privileged; they have full control of the entire operating system (OS) and may tamper with bills to cheat consumers. So, how to provide a trusted solution that can keep track of and verify the consumers’ resource usage has been a challenging problem. In this paper, …
Appearance-Preserved Portrait-To-Anime Translation Via Proxy-Guided Domain Adaptation, Wenpeng Xiao, Cheng Xu, Jiajie Mai, Xuemiao Xu, Yue Li, Chengze Li, Xueting Liu, Shengfeng He
Appearance-Preserved Portrait-To-Anime Translation Via Proxy-Guided Domain Adaptation, Wenpeng Xiao, Cheng Xu, Jiajie Mai, Xuemiao Xu, Yue Li, Chengze Li, Xueting Liu, Shengfeng He
Research Collection School Of Computing and Information Systems
Converting a human portrait to anime style is a desirable but challenging problem. Existing methods fail to resolve this problem due to the large inherent gap between two domains that cannot be overcome by a simple direct mapping. For this reason, these methods struggle to preserve the appearance features in the original photo. In this paper, we discover an intermediate domain, the coser portrait (portraits of humans costuming as anime characters), that helps bridge this gap. It alleviates the learning ambiguity and loosens the mapping difficulty in a progressive manner. Specifically, we start from learning the mapping between coser and …
Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng
Segment-Wise Time-Varying Dynamic Bayesian Network With Graph Regularization, Xing Yang, Chen Zhang, Baihua Zheng
Research Collection School Of Computing and Information Systems
Time-varying dynamic Bayesian network (TVDBN) is essential for describing time-evolving directed conditional dependence structures in complex multivariate systems. In this article, we construct a TVDBN model, together with a score-based method for its structure learning. The model adopts a vector autoregressive (VAR) model to describe inter-slice and intra-slice relations between variables. By allowing VAR parameters to change segment-wisely over time, the time-varying dynamics of the network structure can be described. Furthermore, considering some external information can provide additional similarity information of variables. Graph Laplacian is further imposed to regularize similar nodes to have similar network structures. The regularized maximum a …
R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao
R2f: A General Retrieval, Reading And Fusion Framework For Document-Level Natural Language Inference, Hao Wang, Yixin Cao, Yangguang Li, Zhen Huang, Kun Wang, Jing Shao
Research Collection School Of Computing and Information Systems
Document-level natural language inference (DocNLI) is a new challenging task in natural language processing, aiming at judging the entailment relationship between a pair of hypothesis and premise documents. Current datasets and baselines largely follow sentence-level settings, but fail to address the issues raised by longer documents. In this paper, we establish a general solution, named Retrieval, Reading and Fusion (R2F) framework, and a new setting, by analyzing the main challenges of DocNLI: interpretability, long-range dependency, and cross-sentence inference. The basic idea of the framework is to simplify document-level task into a set of sentence-level tasks, and improve both performance and …
Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam
Differentiated Security Architecture For Secure And Efficient Infotainment Data Communication In Iov Networks, Jiani Fan, Lwin Khin Shar, Jiale Guo, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam
Research Collection School Of Computing and Information Systems
This paper aims to provide differentiated security protection for infotainment data commu- nication in Internet-of-Vehicle (IoV) networks. The IoV is a network of vehicles that uses various sensors, software, built-in hardware, and communication technologies to enable information exchange between pedestrians, cars, and urban infrastructure. Negligence on the security of infotainment data commu- nication in IoV networks can unintentionally open an easy access point for social engineering attacks. The attacker can spread false information about traffic conditions, mislead drivers in their directions, and interfere with traffic management. Such attacks can also cause distractions to the driver, which has a potential implication …
Pickup And Multi-Delivery Problem With Time Windows, Pham Tuan Anh, Aldy Gunawan, Vincent F. Yu, Tuan C. Chau
Pickup And Multi-Delivery Problem With Time Windows, Pham Tuan Anh, Aldy Gunawan, Vincent F. Yu, Tuan C. Chau
Research Collection School Of Computing and Information Systems
This paper addresses a new variant of Pickup and Delivery Problem with Time Windows (PDPTW) for enhancing customer satisfaction. In particular, a huge number of requests is served in the system, where each request includes a pickup node and several delivery nodes instead of a pair of pickup and delivery nodes. It is named Pickup and Multi-Delivery Problem with Time Windows (PMDPTW). A mixed-integer programming model is formulated with the objective of minimizing total travel costs. Computational experiments are conducted to test the correctness of the model with a newly generated benchmark based on the PDPTW benchmark instances. Results show …
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Biasfinder: Metamorphic Test Generation To Uncover Bias For Sentiment Analysis Systems, Muhammad Hilmi Asyrofi, Zhou Yang, Imam Nur Bani Yusuf, Hong Jin Kang, Thung Ferdian, David Lo
Research Collection School Of Computing and Information Systems
Artificial intelligence systems, such as Sentiment Analysis (SA) systems, typically learn from large amounts of data that may reflect human bias. Consequently, such systems may exhibit unintended demographic bias against specific characteristics (e.g., gender, occupation, country-of-origin, etc.). Such bias manifests in an SA system when it predicts different sentiments for similar texts that differ only in the characteristic of individuals described. To automatically uncover bias in SA systems, this paper presents BiasFinder, an approach that can discover biased predictions in SA systems via metamorphic testing. A key feature of BiasFinder is the automatic curation of suitable templates from any given …
Deep Just-In-Time Defect Localization, Fangcheng Qiu, Zhipeng Gao, Xin Xia, David Lo, John Grundy, Xinyu Wang
Deep Just-In-Time Defect Localization, Fangcheng Qiu, Zhipeng Gao, Xin Xia, David Lo, John Grundy, Xinyu Wang
Research Collection School Of Computing and Information Systems
During software development and maintenance, defect localization is an essential part of software quality assurance. Even though different techniques have been proposed for defect localization, i.e., information retrieval (IR)-based techniques and spectrum-based techniques, they can only work after the defect has been exposed, which can be too late and costly to adapt to the newly introduced bugs in the daily development. There are also many JIT defect prediction tools that have been proposed to predict the buggy commit. But these tools do not locate the suspicious buggy positions in the buggy commit. To assist developers to detect bugs in time …
A Recommendation On How To Teach K-Means In Introductory Analytics Courses, Manoj Thulasidas
A Recommendation On How To Teach K-Means In Introductory Analytics Courses, Manoj Thulasidas
Research Collection School Of Computing and Information Systems
We teach K-Means clustering in introductory data analytics courses because it is one of the simplest and most widely used unsupervised machine learning algorithms. However, one drawback of this algorithm is that it does not offer a clear method to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. What is usually taught as the solution for the K Selection problem is the so-called elbow method, where we look at the incremental changes in some quality metric (usually, the sum of squared errors, SSE), trying to find a sudden change. In addition to …
A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang
A Unified Dialogue User Simulator For Few-Shot Data Augmentation, Dazhen Wan, Zheng Zhang, Qi Zhu, Lizi Liao, Minlie Huang
Research Collection School Of Computing and Information Systems
Pre-trained language models have shown superior performance in task-oriented dialogues. However, existing datasets are on limited scales, which cannot support large-scale pre-training. Fortunately, various data augmentation methods have been developed to augment largescale task-oriented dialogue corpora. However, they heavily rely on annotated data in the target domain, which require a tremendous amount of data collection and human labeling work. In this paper, we build a unified dialogue user simulation model by pre-training on several publicly available datasets. The model can then be tuned on a target domain with fewshot data. The experiments on a target dataset across multiple domains show …
Quote: Quality-Oriented Testing For Deep Learning Systems, Jialuo Chen, Jingyi Wang, Xingjun Ma, Youcheng Sun, Jun Sun, Peixin Zhang, Peng Cheng
Quote: Quality-Oriented Testing For Deep Learning Systems, Jialuo Chen, Jingyi Wang, Xingjun Ma, Youcheng Sun, Jun Sun, Peixin Zhang, Peng Cheng
Research Collection School Of Computing and Information Systems
Recently, there has been a significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is deep learning testing, i.e., given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model …
Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam
Conreader: Exploring Implicit Relations In Contracts For Contract Clause Extraction, Weiwen Xu, Yang Deng, Wenqiang Lei, Wenlong Zhao, Tat-Seng Chua, Wai Lam
Research Collection School Of Computing and Information Systems
We study automatic Contract Clause Extraction (CCE) by modeling implicit relations in legal contracts. Existing CCE methods mostly treat contracts as plain text, creating a substantial barrier to understanding contracts of high complexity. In this work, we first comprehensively analyze the complexity issues of contracts and distill out three implicit relations commonly found in contracts, namely, 1) Long-range Context Relation that captures the correlations of distant clauses; 2) Term-Definition Relation that captures the relation between important terms with their corresponding definitions; and 3) Similar Clause Relation that captures the similarities between clauses of the same type. Then we propose a …
Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua
Pacific: Towards Proactive Conversational Question Answering Over Tabular And Textual Data In Finance, Yang Deng, Wenqiang Lei, Wenxuan Zhang, Wai Lam, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
To facilitate conversational question answering (CQA) over hybrid contexts in finance, we present a new dataset, named PACIFIC. Compared with existing CQA datasets, PACIFIC exhibits three key features: (i) proactivity, (ii) numerical reasoning, and (iii) hybrid context of tables and text. A new task is defined accordingly to study Proactive Conversational Question Answering (PCQA), which combines clarification question generation and CQA. In addition, we propose a novel method, namely UniPCQA, to adapt a hybrid format of input and output content in PCQA into the Seq2Seq problem, including the reformulation of the numerical reasoning process as code generation. UniPCQA performs multi-task …
End-To-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
End-To-End Hierarchical Reinforcement Learning With Integrated Subgoal Discovery, Shubham Pateria, Budhitama Subagdja, Ah-Hwee Tan, Chai Quek
Research Collection School Of Computing and Information Systems
Hierarchical reinforcement learning (HRL) is a promising approach to perform long-horizon goal-reaching tasks by decomposing the goals into subgoals. In a holistic HRL paradigm, an agent must autonomously discover such subgoals and also learn a hierarchy of policies that uses them to reach the goals. Recently introduced end-to-end HRL methods accomplish this by using the higher-level policy in the hierarchy to directly search the useful subgoals in a continuous subgoal space. However, learning such a policy may be challenging when the subgoal space is large. We propose integrated discovery of salient subgoals (LIDOSS), an end-to-end HRL method with an integrated …
Make Your Own Sprites: Aliasing-Aware And Cell-Controllable Pixelization, Zongwei Wu, Liangyu Chai, Nanxuan Zhao, Bailin Deng, Yongtuo Liu, Qiang Wen, Junle Wang, Shengfeng He
Make Your Own Sprites: Aliasing-Aware And Cell-Controllable Pixelization, Zongwei Wu, Liangyu Chai, Nanxuan Zhao, Bailin Deng, Yongtuo Liu, Qiang Wen, Junle Wang, Shengfeng He
Research Collection School Of Computing and Information Systems
Pixel art is a unique art style with the appearance of low resolution images. In this paper, we propose a data-driven pixelization method that can produce sharp and crisp cell effects with controllable cell sizes. Our approach overcomes the limitation of existing learning-based methods in cell size control by introducing a reference pixel art to explicitly regularize the cell structure. In particular, the cell structure features of the reference pixel art are used as an auxiliary input for the pixelization process, and for measuring the style similarity between the generated result and the reference pixel art. Furthermore, we disentangle the …
Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati
Vr Computing Lab: An Immersive Classroom For Computing Learning, Shawn Pang, Kyong Jin Shim, Yi Meng Lau, Swapna Gottipati
Research Collection School Of Computing and Information Systems
In recent years, virtual reality (VR) is gaining popularity amongst educators and learners. If a picture is worth a thousand words, a VR session is worth a trillion words. VR technology completely immerses users with an experience that transports them into a simulated world. Universities across the United States, United Kingdom, and other countries have already started using VR for higher education in areas such as medicine, business, architecture, vocational training, social work, virtual field trips, virtual campuses, helping students with special needs, and many more. In this paper, we propose a novel VR platform learning framework which maps elements …
Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang
Prompting For Multimodal Hateful Meme Classification, Rui Cao, Roy Ka-Wei Lee, Wen-Haw Chong, Jing Jiang
Research Collection School Of Computing and Information Systems
Hateful meme classification is a challenging multimodal task that requires complex reasoning and contextual background knowledge. Ideally, we could leverage an explicit external knowledge base to supplement contextual and cultural information in hateful memes. However, there is no known explicit external knowledge base that could provide such hate speech contextual information. To address this gap, we propose PromptHate, a simple yet effective prompt-based model that prompts pre-trained language models (PLMs) for hateful meme classification. Specifically, we construct simple prompts and provide a few in-context examples to exploit the implicit knowledge in the pretrained RoBERTa language model for hateful meme classification. …
Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Jianli Fan, Lingxiao Jiang, Daniel Wai Kiat Lim
Dronlomaly: Runtime Detection Of Anomalous Drone Behaviors Via Log Analysis And Deep Learning, Lwin Khin Shar, Wei Minn, Nguyen Binh Duong Ta, Jianli Fan, Lingxiao Jiang, Daniel Wai Kiat Lim
Research Collection School Of Computing and Information Systems
Drones are increasingly popular and getting used in a variety of missions such as area surveillance, pipeline inspection, cinematography, etc. While the drone is conducting a mission, anomalies such as sensor fault, actuator fault, configuration errors, bugs in controller program, remote cyber- attack, etc., may affect the drone’s physical stability and cause serious safety violations such as crashing into the public. During a flight mission, drones typically log flight status and state units such as GPS coordinates, actuator outputs, accelerator readings, gyroscopic readings, etc. These log data may reflect the above-mentioned anomalies. In this paper, we propose a novel, deep …
Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing
Interventional Training For Out-Of-Distribution Natural Language Understanding, Sicheng Yu, Jing Jiang, Hao Zhang, Yulei Niu, Qianru Sun, Lidong Bing
Research Collection School Of Computing and Information Systems
Out-of-distribution (OOD) settings are used to measure a model’s performance when the distribution of the test data is different from that of the training data. NLU models are known to suffer in OOD settings (Utama et al., 2020b). We study this issue from the perspective of causality, which sees confounding bias as the reason for models to learn spurious correlations. While a common solution is to perform intervention, existing methods handle only known and single confounder, but in many NLU tasks the confounders can be both unknown and multifactorial. In this paper, we propose a novel interventional training method called …
Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen
Innovative And Effective Spreadsheet Tool For Learning Sentiment Analysis And Prediction, Michelle L. F. Cheong, Jean Yun-Chen Chen
Research Collection School Of Computing and Information Systems
In this paper, we describe a spreadsheet tool which was developed and implemented to teach sentiment analysis and prediction to university students who are less technically inclined. We adopted the “Use-Modify-Create” cycle to ensure that the students learn the computing concepts and practices in a progressive and active learning manner, and we evaluated the effectiveness of the tool using the Learning Object Evaluation Scale for Students (LOES-S). The evaluation shows that our tool performed well in terms of quality, learn and engagement constructs, and our paired T-test shows strong evidence that, on average, the course module with the tool does …
Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Dialogconv: A Lightweight Fully Convolutional Network For Multi-View Response Selection, Yongkang Liu, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Research Collection School Of Computing and Information Systems
Current end-to-end retrieval-based dialogue systems are mainly based on Recurrent Neural Networks or Transformers with attention mechanisms. Although promising results have been achieved, these models often suffer from slow inference or huge number of parameters. In this paper, we propose a novel lightweight fully convolutional architecture, called DialogConv, for response selection. DialogConv is exclusively built on top of convolution to extract matching features of context and response. Dialogues are modeled in 3D views, where DialogConv performs convolution operations on embedding view, word view and utterance view to capture richer semantic information from multiple contextual views. On the four benchmark datasets, …
Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy
Opportunities And Challenges In Code Search Tools, Chao Liu, Xin Xia, David Lo, Cuiying Gao, Xiaohu Yang, John Grundy
Research Collection School Of Computing and Information Systems
Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique …
Beer: Fast O(1/T) Rate For Decentralized Nonconvex Optimization With Communication Compression, Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtarik, Yuejie Chi
Beer: Fast O(1/T) Rate For Decentralized Nonconvex Optimization With Communication Compression, Haoyu Zhao, Boyue Li, Zhize Li, Peter Richtarik, Yuejie Chi
Research Collection School Of Computing and Information Systems
Communication efficiency has been widely recognized as the bottleneck for large-scale decentralized machine learning applications in multi-agent or federated environments. To tackle the communication bottleneck, there have been many efforts to design communication-compressed algorithms for decentralized nonconvex optimization, where the clients are only allowed to communicate a small amount of quantized information (aka bits) with their neighbors over a predefined graph topology. Despite significant efforts, the state-of-the-art algorithm in the nonconvex setting still suffers from a slower rate of convergence $O((G/T)^{2/3})$ compared with their uncompressed counterpart, where $G$ measures the data heterogeneity across different clients, and $T$ is the number …
How Developers Engineer Test Cases: An Observational Study, Maurício Aniche, Christoph Treude, Andy Zaidman
How Developers Engineer Test Cases: An Observational Study, Maurício Aniche, Christoph Treude, Andy Zaidman
Research Collection School Of Computing and Information Systems
One of the main challenges that developers face when testing their systems lies in engineering test cases that are good enough to reveal bugs. And while our body of knowledge on software testing and automated test case generation is already quite significant, in practice, developers are still the ones responsible for engineering test cases manually. Therefore, understanding the developers’ thought- and decision-making processes while engineering test cases is a fundamental step in making developers better at testing software. In this paper, we observe 13 developers thinking-aloud while testing different real-world open-source methods, and use these observations to explain how developers …
A Fine-Grained Data Set And Analysis Of Tangling In Bug Fixing Commits, Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel G. Hoy, Christoph Treude, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh
A Fine-Grained Data Set And Analysis Of Tangling In Bug Fixing Commits, Steffen Herbold, Alexander Trautsch, Benjamin Ledel, Alireza Aghamohammadi, Taher Ahmed Ghaleb, Kuljit Kaur Chahal, Tim Bossenmaier, Bhaveet Nagaria, Philip Makedonski, Matin Nili Ahmadabadi, Kristóf Szabados, Helge Spieker, Matej Madeja, Nathaniel G. Hoy, Christoph Treude, Shangwen Wang, Gema Rodríguez-Pérez, Ricardo Colomo-Palacios, Roberto Verdecchia, Paramvir Singh
Research Collection School Of Computing and Information Systems
Context: Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs.Objective: We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits.Methods: We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the …