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Articles 1351 - 1380 of 8458
Full-Text Articles in Computer Sciences
Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen
Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen
Research Collection School Of Computing and Information Systems
Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this …
Remote Multi-Person Heart Rate Monitoring With Smart Speakers: Overcoming Separation Constraint, Ngoc Doan Thu Tran, Dong Ma, Rajesh Krishna Balan
Remote Multi-Person Heart Rate Monitoring With Smart Speakers: Overcoming Separation Constraint, Ngoc Doan Thu Tran, Dong Ma, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Heart rate is a key vital sign that can be used to understand an individual’s health condition. Recently, remote sensing techniques, especially acoustic-based sensing, have received increasing attention for their ability to non-invasively detect heart rate via commercial mobile devices such as smartphones and smart speakers. However, due to signal interference, existing methods have primarily focused on monitoring a single user and required a large separation between them when monitoring multiple people. These limitations hinder many common use cases such as couples sharing the same bed or two or more people located in close proximity. In this paper, we present …
Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang
Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang
Research Collection School Of Computing and Information Systems
Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static …
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Predicting Viral Rumors And Vulnerable Users With Graph-Based Neural Multi-Task Learning For Infodemic Surveillance, Xuan Zhang, Wei Gao
Research Collection School Of Computing and Information Systems
In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) …
Hardware-Assisted Live Kernel Function Updating On Intel Platforms, Lei Zhou, Fengwei Zhang, Kevin Leach, Xuhua Ding, Zhenyu Ning, Guojun Wang, Jidong Xiao
Hardware-Assisted Live Kernel Function Updating On Intel Platforms, Lei Zhou, Fengwei Zhang, Kevin Leach, Xuhua Ding, Zhenyu Ning, Guojun Wang, Jidong Xiao
Research Collection School Of Computing and Information Systems
Traditional kernel updates such as perfective maintenance and vulnerability patching requires shutting the system down, disrupting continuous execution of applications. Enterprises and researchers have proposed various live updating techniques to patch the kernel with lower downtime to reduce the loss of useful uptime. However, existing kernel live update techniques either rely on specific support from the target OS, or are deployed in virtualized environments (i.e., systems running in virtual machines). In this article we present KShot , a hardware-assisted live and secure kernel function update mechanism for native operating systems. By leveraging x86 SMM and Intel SGX, KShot runs in …
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Tracking People Across Ultra Populated Indoor Spaces By Matching Unreliable Wi-Fi Signals With Disconnected Video Feeds, Quang Hai Truong, Dheryta Jaisinghani, Shubham Jain, Arunesh Sinha, Jeong Gil Ko, Rajesh Krishna Balan
Research Collection School Of Computing and Information Systems
Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — DenseTrack for tracking people in such environments. DenseTrack leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to …
Privobfnet: A Weakly Supervised Semantic Segmentation Model For Data Protection, Chiat Pin Tay, Vigneshwaran Subbaraju, Thivya Kandappu
Privobfnet: A Weakly Supervised Semantic Segmentation Model For Data Protection, Chiat Pin Tay, Vigneshwaran Subbaraju, Thivya Kandappu
Research Collection School Of Computing and Information Systems
The use of social media has made it easy to communicate and share information over the internet. However, it also brings issues such as data privacy leakage, which can be exploited by recipients with malicious intentions to harm the sender. In this paper, we propose a deep neural network that analyzes user’s image for privacy sensitive content and automatically locates sensitive regions for obfuscation. Our approach relies solely on image level annotations and learns to (a) predict an overall privacy score, (b) detect sensitive attributes and (c) demarcate the sensitive regions for obfuscation, in a given input image. We validated …
Causal Disentangled Recommendation Against User Preference Shifts, Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat‑Seng Chua
Causal Disentangled Recommendation Against User Preference Shifts, Wenjie Wang, Xinyu Lin, Liuhui Wang, Fuli Feng, Yunshan Ma, Tat‑Seng Chua
Research Collection School Of Computing and Information Systems
Recommender systems easily face the issue of user preference shifts. User representations will become outof-date and lead to inappropriate recommendations if user preference has shifted over time. To solve theissue, existing work focuses on learning robust representations or predicting the shifting pattern. Therelacks a comprehensive view to discover the underlying reasons for user preference shifts. To understand thepreference shift, we abstract a causal graph to describe the generation procedure of user interaction sequences.Assuming user preference is stable within a short period, we abstract the interaction sequence as a set ofchronological environments. From the causal graph, we find that the changes …
Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang
Quantifying The Competitiveness Of A Dataset In Relation To General Preferences, Kyriakos Mouratidis, Keming Li, Bo Tang
Research Collection School Of Computing and Information Systems
Typically, a specific market (e.g., of hotels, restaurants, laptops, etc.) is represented as a multi-attribute dataset of the available products. The topic of identifying and shortlisting the products of most interest to a user has been well-explored. In contrast, in this work we focus on the dataset, and aim to assess its competitiveness with regard to different possible preferences. We define measures of competitiveness, and represent them in the form of a heat-map in the domain of preferences. Our work finds application in market analysis and in business development. These applications are further enhanced when the competitiveness heat-map is used …
Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le
Big Code Search: A Bibliography, Kisub Kim, Sankalp Ghatpande, Dongsun Kim, Xin Zhou, Kui Liu, Tegawende F. Bissyande, Jacques Klein, Traon Yves Le
Research Collection School Of Computing and Information Systems
Code search is an essential task in software development. Developers often search the internet and other code databases for necessary source code snippets to ease the development efforts. Code search techniques also help learn programming as novice programmers or students can quickly retrieve (hopefully good) examples already used in actual software projects. Given the recurrence of the code search activity in software development, there is an increasing interest in the research community. To improve the code search experience, the research community suggests many code search tools and techniques. These tools and techniques leverage several different ideas and claim a better …
Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He
Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He
Research Collection School Of Computing and Information Systems
Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao
Research Collection School Of Computing and Information Systems
The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …
Enabling Ai And Robotic Coaches For Physical Rehabilitation Therapy: Iterative Design And Evaluation With Therapists And Post-Stroke Survivors, Min Hun Lee, Daniel Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia
Enabling Ai And Robotic Coaches For Physical Rehabilitation Therapy: Iterative Design And Evaluation With Therapists And Post-Stroke Survivors, Min Hun Lee, Daniel Siewiorek, Asim Smailagic, Alexandre Bernardino, Sergi Bermúdez I Badia
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, the deployment of these systems remains a challenge. Previous work described the lack of involving stakeholders to design such functionalities as one of the major causes. In this paper, we present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient’s exercises in an effective and acceptable way with four therapists and five post-stroke survivors. Through iterative …
Efficient Privacy-Preserving Spatial Data Query In Cloud Computing, Yinbin Miao, Yutao Yang, Xinghua Li, Linfeng Wei, Zhiquan Liu, Robert H. Deng
Efficient Privacy-Preserving Spatial Data Query In Cloud Computing, Yinbin Miao, Yutao Yang, Xinghua Li, Linfeng Wei, Zhiquan Liu, Robert H. Deng
Research Collection School Of Computing and Information Systems
With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes …
Continual Learning, Fast And Slow, Quang Anh Pham, Chenghao Liu, Steven C. H. Hoi
Continual Learning, Fast And Slow, Quang Anh Pham, Chenghao Liu, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
According to the Complementary Learning Systems (CLS) theory (McClelland et al. 1995) in neuroscience, humans do effective continual learning through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics, individual experiences; and a slow learning system located in the neocortex for the gradual acquisition of structured knowledge about the environment. Motivated by this theory, we propose DualNets (for Dual Networks), a general continual learning framework comprising a fast learning system for supervised learning of pattern-separated representation from specific tasks and a slow learning system for representation learning of task-agnostic general representation via …
Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara
Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara
Research Collection School Of Computing and Information Systems
We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …
Conceptthread: Visualizing Threaded Concepts In Mooc Videos, Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang
Conceptthread: Visualizing Threaded Concepts In Mooc Videos, Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang
Research Collection School Of Computing and Information Systems
Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread , a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, …
Active Code Learning: Benchmarking Sample-Efficient Training Of Code Models, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Active Code Learning: Benchmarking Sample-Efficient Training Of Code Models, Qiang Hu, Yuejun Guo, Xiaofei Xie, Maxime Cordy, Lei Ma, Mike Papadakis, Yves Le Traon
Research Collection School Of Computing and Information Systems
The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), especially for those with limited budgets. Therefore, efficiently training models of code with less human effort has become an emergent problem. Active learning is such a technique to address this issue that allows developers to train a model with reduced data while producing models with desired performance, which has been well studied in computer vision and natural language processing domains. Unfortunately, there is no such work that explores the effectiveness of active learning for code …
Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng
Soci+: An Enhanced Toolkit For Secure Outsourced Computation On Integers, Bowen Zhao, Weiquan Deng, Xiaoguo Li, Ximeng Liu, Qingqi Pei, Robert H. Deng
Research Collection School Of Computing and Information Systems
Secure outsourced computation is critical for cloud computing to safeguard data confidentiality and ensure data usability. Recently, secure outsourced computation schemes following a twin-server architecture based on partially homomorphic cryptosystems have received increasing attention. The Secure Outsourced Computation on Integers (SOCI) [1] toolkit is the state-of-the-art among these schemes which can perform secure computation on integers without requiring the costly bootstrapping operation as in fully homomorphic encryption; however, SOCI suffers from relatively large computation and communication overhead. In this paper, we propose SOCI+ which significantly improves the performance of SOCI. Specifically, SOCI+ employs a novel (2,2)-threshold Paillier cryptosystem with fast …
A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng
A Unified Framework For Contextual And Factoid Question Generation, Chenhe Dong, Ying Shen, Shiyang Lin, Zhenzhou Lin, Yang Deng
Research Collection School Of Computing and Information Systems
Question generation (QG) aims to automatically generate fluent and relevant questions, where the two most mainstream directions are generating questions from unstructured contextual texts (CQG), such as news articles, and generating questions from structured factoid texts (FQG), such as knowledge graphs or tables. Existing methods for these two tasks mainly face challenges of limited internal structural information as well as scarce background information, while these two tasks can benefit each other for alleviating these issues. For example, when meeting the entity mention “United Kingdom” in CQG, it can be inferred that it is a country in European continent based on …
Demonstrating Canvas-Based Processing Of Multiple Camera Streams At The Edge, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra
Demonstrating Canvas-Based Processing Of Multiple Camera Streams At The Edge, Ila Gokarn, Hemanth Sabbella, Yigong Hu, Tarek Abdelzaher, Archan Misra
Research Collection School Of Computing and Information Systems
We demonstrate criticality-aware canvas-based processing of multiple concurrent camera streams at the resource constrained edge to show substantial improvement in the accuracy-throughput trade-off. The proposed system focuses the available computation resources on select Regions of Interest (RoI) across all the camera streams by (i) extracting RoI from the input camera stream (ii) 2D bin packing the RoI on a canvas frame and (iii) batching and inferring upon these constructed composite canvas frames with a YOLOv5 object detection model. Our experiments show that such canvas-based processing can (i) sustain real-time processing throughput of 23 FPS per camera across 6 concurrent input …
Societal Impacts Of Artificial Intelligence: Ethics, Legal, And Governance Issues, Yuzhou Qian, Keng Siau, Fiona Fui-Hoon Nah
Societal Impacts Of Artificial Intelligence: Ethics, Legal, And Governance Issues, Yuzhou Qian, Keng Siau, Fiona Fui-Hoon Nah
Research Collection School Of Computing and Information Systems
Artificial intelligence (AI) is quickly changing the way we work and the way we live. The emergence of ChatGPT has thrust AI, especially Generative AI, into the spotlight. The societal impact of AI is on most people's minds. This article presents several research projects on how AI impacts work and society. Three research works are discussed in this article. The first study develops a theoretical framework structuring the legal and ethical objectives that are needed and the means to achieve them. The second study concentrates on bias and discrimination issues embedded in AI applications. It focuses on enhancing the collaboration …
Deepaco: Neural-Enhanced Ant Systems For Combinatorial Optimization, Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li
Deepaco: Neural-Enhanced Ant Systems For Combinatorial Optimization, Haoran Ye, Jiarui Wang, Zhiguang Cao, Helan Liang, Yong Li
Research Collection School Of Computing and Information Systems
Ant Colony Optimization (ACO) is a meta-heuristic algorithm that has been successfully applied to various Combinatorial Optimization Problems (COPs). Traditionally, customizing ACO for a specific problem requires the expert design of knowledge-driven heuristics. In this paper, we propose DeepACO, a generic framework leveraging deep reinforcement learning to automate heuristic designs. DeepACO serves to strengthen the heuristic measures of existing ACO algorithms and dispense with laborious manual design in future ACO applications. As a neural-enhanced meta-heuristic, DeepACO consistently outperforms its ACO counterparts on eight COPs using a single neural model and a single set of hyperparameters. As a Neural Combinatorial Optimization …
Explorelah: Personalised And Smart Trip Planner For Mobile Tourism, Aldy Gunawan, Siu Loon Hoe, Xun Yi Lim, Linh Chi Tran, Dang Viet Anh Nguyen
Explorelah: Personalised And Smart Trip Planner For Mobile Tourism, Aldy Gunawan, Siu Loon Hoe, Xun Yi Lim, Linh Chi Tran, Dang Viet Anh Nguyen
Research Collection School Of Computing and Information Systems
Various recommender systems for mobile tourism have been developed over the years. However, most of these recommender systems tend to overwhelm users with too much information and may not be personalised to user preferences. In this paper, we introduce ExploreLah, a personalised and smart trip planner for exploring Point of Interests (POIs) in Singapore. The user preferences are categorised into five groups: shopping, art & culture, outdoor activity, adventure, and nightlife. The problem is considered as the Team Orienteering Problem with Time Windows. The algorithm is developed to generate itineraries. Simulated experiments using test cases were performed to evaluate and …
Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee
Mermaid: A Dataset And Framework For Multimodal Meme Semantic Understanding, Shaun Toh, Adriel Kuek, Wen Haw Chong, Roy Ka Wei Lee
Research Collection School Of Computing and Information Systems
Memes are widely used to convey cultural and societal issues and have a significant impact on public opinion. However, little work has been done on understanding and explaining the semantics expressed in multimodal memes. To fill this research gap, we introduce MERMAID, a dataset consisting of 3,633 memes annotated with their entities and relations, and propose a novel MERF pipeline that extracts entities and their relationships in memes. Our framework combines state-of-the-art techniques from natural language processing and computer vision to extract text and image features and infer relationships between entities in memes. We evaluate the proposed framework on a …
Llm4vis: Explainable Visualization Recommendation Using Chatgpt, Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, Yong Wang
Llm4vis: Explainable Visualization Recommendation Using Chatgpt, Lei Wang, Songheng Zhang, Yun Wang, Ee-Peng Lim, Yong Wang
Research Collection School Of Computing and Information Systems
Data visualization is a powerful tool for exploring and communicating insights in various domains. To automate visualization choice for datasets, a task known as visualization recommendation has been proposed. Various machine-learning-based approaches have been developed for this purpose, but they often require a large corpus of dataset-visualization pairs for training and lack natural explanations for their results. To address this research gap, we propose LLM4Vis, a novel ChatGPT-based prompting approach to perform visualization recommendation and return human-like explanations using very few demonstration examples. Our approach involves feature description, demonstration example selection, explanation generation, demonstration example construction, and inference steps. To …
Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun
Refinement-Based Specification And Analysis Of Multi-Core Arinc 653 Using Event-B, Feng Zhang, Leping Zhang, Yongwang Zhao, Yang Liu, Jun Sun
Research Collection School Of Computing and Information Systems
ARINC 653 as the de facto standard of partitioning operating systems has been applied in many safety-critical domains. The multi-core version of ARINC 653, ARINC 653 Part 1-4 (Version 4), provides support for services to be utilized with a module that contains multiple processor cores. Formal specification and analysis of this standard document could provide a rigorous specification and uncover concealed errors in the textual description of service requirements. This article proposes a specification method for concurrency on a multi-core platform using Event-B, and a refinement structure for the complicated ARINC 653 Part 1-4 provides a comprehensive, stepwise refinement-based Event-B …
Do Contributing Files Provide Information About Oss Newcomers' Onboarding Barriers?, Felipe Fronchetti, David Shepherd, Igor Wiese, Christoph Treude, Marco Gerosa, Igor Steinmacher
Do Contributing Files Provide Information About Oss Newcomers' Onboarding Barriers?, Felipe Fronchetti, David Shepherd, Igor Wiese, Christoph Treude, Marco Gerosa, Igor Steinmacher
Research Collection School Of Computing and Information Systems
Effectively onboarding newcomers is essential for the success of open source projects. These projects often provide onboarding guidelines in their ‘CONTRIBUTING’ files (e.g., CONTRIBUTING.md on GitHub). These files explain, for example, how to find open tasks, implement solutions, and submit code for review. However, these files often do not follow a standard structure, can be too large, and miss barriers commonly found by newcomers. In this paper, we propose an automated approach to parse these CONTRIBUTING files and assess how they address onboarding barriers. We manually classified a sample of files according to a model of onboarding barriers from the …
Lessons From The Long Tail: Analysing Unsafe Dependency Updates Across Software Ecosystems, Supatsara Wattanakriengkrai, Raula Kula, Christoph Treude, Kenichi Matsumoto
Lessons From The Long Tail: Analysing Unsafe Dependency Updates Across Software Ecosystems, Supatsara Wattanakriengkrai, Raula Kula, Christoph Treude, Kenichi Matsumoto
Research Collection School Of Computing and Information Systems
A risk in adopting third-party dependencies into an application is their potential to serve as a doorway for malicious code to be injected (most often unknowingly). While many initiatives from both industry and research communities focus on the most critical dependencies (i.e., those most depended upon within the ecosystem), little is known about whether the rest of the ecosystem suffers the same fate. Our vision is to promote and establish safer practises throughout the ecosystem. To motivate our vision, in this paper, we present preliminary data based on three representative samples from a population of 88,416 pull requests (PRs) and …
Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang
Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang
Research Collection School Of Computing and Information Systems
Accurate detection of semantic code clones has many applications in software engineering but is challenging because of lexical, syntactic, or structural dissimilarities in code. CodeBERT, a popular deep neural network based pre-trained code model, can detect code clones with a high accuracy. However, its performance on unseen data is reported to be lower. A challenge is to interpret CodeBERT's clone detection behavior and isolate the causes of mispredictions. In this paper, we evaluate CodeBERT and interpret its clone detection behavior on the SemanticCloneBench dataset focusing on Java and Python clone pairs. We introduce the use of a black-box model interpretation …