Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Active learning (1)
- Blockchain networks (1)
- Bugs (1)
- COVID-19 (1)
- Community Search (1)
-
- Community Understanding (1)
- Data Visualization (1)
- Descriptive meta-information (1)
- Dialogue generation (1)
- Emotional chatbot (1)
- Emotional conversation (1)
- Experiential learning (1)
- Group-based learning (1)
- Hidden Markov Models (1)
- Hypernetworks (1)
- Learning Behavior Analysis (1)
- Multimodal Search (1)
- Pre-trained language mode (1)
- Problem Solving (1)
- Program logics (1)
- Programming (1)
- Runtime verification (1)
- Smart contracts (1)
- Task Analysis (1)
- Text Generation (1)
- Time Series Data (1)
- Under-approximate reasoning (1)
- Visual Analytics (1)
- Vulnerabilities (1)
Articles 1 - 10 of 10
Full-Text Articles in Physical Sciences and Mathematics
From Community Search To Community Understanding: A Multimodal Community Query Engine, Zhao Li, Pengcheng Zou, Xia Chen, Shichang Hu, Peng Zhang, Yumou Zhou, Bingsheng He, Yuchen Li, Xing Tang
From Community Search To Community Understanding: A Multimodal Community Query Engine, Zhao Li, Pengcheng Zou, Xia Chen, Shichang Hu, Peng Zhang, Yumou Zhou, Bingsheng He, Yuchen Li, Xing Tang
Research Collection School Of Computing and Information Systems
In this demo, we present an online multi-modal community query engine (MQE1 ) on Alibaba’s billion-scale heterogeneous network. MQE has two distinct features in comparison with existing community query engines. Firstly, MQE supports multimodal community search on heterogeneous graphs with keyword and image queries. Secondly, to facilitate community understanding in real business scenarios, MQE generates natural language descriptions for the retrieved community in combination with other useful demographic information. The distinct features of MQE benefit many downstream applications in Alibaba’s e-commerce platform like recommendation. Our experiments confirm the effectiveness and efficiency of MQE on graphs with billions of edges.
Target-Guided Emotion-Aware Chat Machine, Wei Wei, Xianling Mao, Guibing Guo, Feida Zhu, Feida Zhu, Yuchong Hu, Shanshan Feng
Target-Guided Emotion-Aware Chat Machine, Wei Wei, Xianling Mao, Guibing Guo, Feida Zhu, Feida Zhu, Yuchong Hu, Shanshan Feng
Research Collection School Of Computing and Information Systems
The consistency of a response to a given post at the semantic level and emotional level is essential for a dialogue system to deliver humanlike interactions. However, this challenge is not well addressed in the literature, since most of the approaches neglect the emotional information conveyed by a post while generating responses. This article addresses this problem and proposes a unified end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post and leveraging target information to generate more intelligent responses with appropriately expressed emotions. Extensive experiments on real-world data demonstrate that the proposed …
Design And Supervision Model Of Group Projects For Active Learning, Yi Meng Lau, Kyong Jin Shim, Swapna Gottipati
Design And Supervision Model Of Group Projects For Active Learning, Yi Meng Lau, Kyong Jin Shim, Swapna Gottipati
Research Collection School Of Computing and Information Systems
This research paper presents a group project framework for a second-year programming course, which was conducted during the COVID-19 pandemic. The framework offers well defined stages of the group project which allow students to work on their choice of a real-world problem, integrate their learnings from previous courses, and present a working solution. In the group project, students actively participate, reflect, and contribute to achieving the goals set in the learning objectives of the course. Our framework incorporates key features from Kolb’s Experiential Learning Theory (1984) and principles of active learning from Barnes (1989) to achieve active and experiential learning …
Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang
Does Bert Understand Idioms? A Probing-Based Empirical Study Of Bert Encodings Of Idioms, Minghuan Tan, Jing Jiang
Research Collection School Of Computing and Information Systems
Understanding idioms is important in NLP. In this paper, we study to what extent pre-trained BERT model can encode the meaning of a potentially idiomatic expression (PIE) in a certain context. We make use of a few existing datasets and perform two probing tasks: PIE usage classification and idiom paraphrase identification. Our experiment results suggest that BERT indeed can separate the literal and idiomatic usages of a PIE with high accuracy. It is also able to encode the idiomatic meaning of a PIE to some extent.
Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang
Learning And Evaluating Chinese Idiom Embeddings, Minghuan Tan, Jing Jiang
Research Collection School Of Computing and Information Systems
We study the task of learning and evaluating Chinese idiom embeddings. We first construct a new evaluation dataset that contains idiom synonyms and antonyms. Observing that existing Chinese word embedding methods may not be suitable for learning idiom embeddings, we further present a BERT-based method that directly learns embedding vectors for individual idioms. We empirically compare representative existing methods and our method. We find that our method substantially outperforms existing methods on the evaluation dataset we have constructed.
Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele
Injecting Descriptive Meta-Information Into Pre-Trained Language Models With Hypernetworks, Wenying Duan, Xiaoxi He, Zimu Zhou, Hong Rao, Lothar Thiele
Research Collection School Of Computing and Information Systems
Pre-trained language models have been widely adopted as backbones in various natural language processing tasks. However, existing pre-trained language models ignore the descriptive meta-information in the text such as the distinction between the title and the mainbody, leading to over-weighted attention to insignificant text. In this paper, we propose a hypernetwork-based architecture to model the descriptive meta-information and integrate it into pre-trained language models. Evaluations on three natural language processing tasks show that our method notably improves the performance of pre-trained language models and achieves the state-of-the-art results on keyphrase extraction.
Incorrectness Logic For Graph Programs, Christopher M. Poskitt
Incorrectness Logic For Graph Programs, Christopher M. Poskitt
Research Collection School Of Computing and Information Systems
Program logics typically reason about an over-approximation of program behaviour to prove the absence of bugs. Recently, program logics have been proposed that instead prove the presence of bugs by means of under-approximate reasoning, which has the promise of better scalability. In this paper, we present an under-approximate program logic for a nondeterministic graph programming language, and show how it can be used to reason deductively about program incorrectness, whether defined by the presence of forbidden graph structure or by finitely failing executions. We prove this 'incorrectness logic' to be sound and complete, and speculate on some possible future applications …
Sguard: Towards Fixing Vulnerable Smart Contracts Automatically, Tai D. Nguyen, Long H. Pham, Jun Sun
Sguard: Towards Fixing Vulnerable Smart Contracts Automatically, Tai D. Nguyen, Long H. Pham, Jun Sun
Research Collection School Of Computing and Information Systems
Smart contracts are distributed, self-enforcing programs executing on top of blockchain networks. They have the potential to revolutionize many industries such as financial institutes and supply chains. However, smart contracts are subject to code-based vulnerabilities, which casts a shadow on its applications. As smart contracts are unpatchable (due to the immutability of blockchain), it is essential that smart contracts are guaranteed to be free of vulnerabilities. Unfortunately, smart contract languages such as Solidity are Turing-complete, which implies that verifying them statically is infeasible. Thus, alternative approaches must be developed to provide the guarantee. In this work, we develop an approach …
Retrieval-Augmented Generation For Code Summarization Via Hybrid Gnn, Shangqing Liu, Yu Chen, Xiaofei Xie, Jingkai Siow, Yang Liu
Retrieval-Augmented Generation For Code Summarization Via Hybrid Gnn, Shangqing Liu, Yu Chen, Xiaofei Xie, Jingkai Siow, Yang Liu
Research Collection School Of Computing and Information Systems
Source code summarization aims to generate natural language summaries from structured code snippets for better understanding code functionalities. However, automatic code summarization is challenging due to the complexity of the source code and the language gap between the source code and natural language summaries. Most previous approaches either rely on retrieval-based (which can take advantage of similar examples seen from the retrieval database, but have low generalization performance) or generation-based methods (which have better generalization performance, but cannot take advantage of similar examples). This paper proposes a novel retrieval-augmented mechanism to combine the benefits of both worlds. Furthermore, to mitigate …
Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma
Qlens: Visual Analytics Of Multi-Step Problem-Solving Behaviors For Improving Question Design, Meng Xia, Reshika P. Velumani, Yong Wang, Huamin Qu, Xiaojuan Ma
Research Collection School Of Computing and Information Systems
With the rapid development of online education in recent years, there has been an increasing number of learning platforms that provide students with multi-step questions to cultivate their problem-solving skills. To guarantee the high quality of such learning materials, question designers need to inspect how students’ problem-solving processes unfold step by step to infer whether students’ problem-solving logic matches their design intent. They also need to compare the behaviors of different groups (e.g., students from different grades) to distribute questions to students with the right level of knowledge. The availability of fine-grained interaction data, such as mouse movement trajectories from …