Open Access. Powered by Scholars. Published by Universities.®
Numerical Analysis and Computation Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
- Keyword
-
- 3D articulated motion (1)
- Adaptive Resonance Theory (1)
- Adaptive sampling (1)
- Assertions (1)
- Auto face annotation (1)
-
- Autonomous Vehicle (1)
- Bilevel program (1)
- Category Node (1)
- Choice Function (1)
- Classification (1)
- Code Search (1)
- Codes (1)
- Conformal geometry videos (1)
- Conformal parameterization (1)
- Congestion control (1)
- Content-aware recommender (1)
- Deformable objects (1)
- Domestication (1)
- Ethereum (1)
- Ethereum Attack Detection (1)
- Feature selection (1)
- Geometry videos (1)
- GitHub (1)
- Globalization (1)
- Graph Neural Networks (1)
- H.264/AVC (1)
- Internet pricing (1)
- Interpreting user preferences (1)
- Isometric transformation (1)
- Media attention (1)
Articles 1 - 16 of 16
Full-Text Articles in Numerical Analysis and Computation
Cami: A Counselor Agent Supporting Motivational Interviewing Through State Inference And Topic Exploration, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Ee-Peng Lim
Cami: A Counselor Agent Supporting Motivational Interviewing Through State Inference And Topic Exploration, Yizhe Yang, Palakorn Achananuparp, Heyan Huang, Jing Jiang, Phey Ling Kit, Nicholas Gabriel Lim, Cameron Shi Ern Tan, Ee-Peng Lim
Research Collection School Of Computing and Information Systems
Conversational counselor agents have become essential tools for addressing the rising demand for scalable and accessible mental health support. This paper introduces CAMI, a novel automated counselor agent grounded in Motivational Interviewing (MI) – a client-centered counseling approach designed to address ambivalence and facilitate behavior change. CAMI employs a novel STAR framework, consisting of client’s state inference, motivation topic exploration, and response generation modules, leveraging large language models (LLMs). These components work together to evoke change talk, aligning with MI principles and improving counseling outcomes for diverse clients. We evaluate CAMI’s performance through both automated and expert evaluations, utilizing simulated …
Quantitative Runtime Monitoring Of Ethereum Transaction Attacks, Xinyao Xu, Ziyu Mao, Jianzhong Su, Xingwei Lin, David Basin, Jun Sun, Jingyi Wang
Quantitative Runtime Monitoring Of Ethereum Transaction Attacks, Xinyao Xu, Ziyu Mao, Jianzhong Su, Xingwei Lin, David Basin, Jun Sun, Jingyi Wang
Research Collection School Of Computing and Information Systems
The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks. Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types. To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum. MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks. We integrate …
Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw
Question-Attentive Review-Level Explanation For Neural Rating Regression, Trung Hoang Le, Hady Wirawan Lauw
Research Collection School Of Computing and Information Systems
Recommendation explanations help to improve their acceptance by end users. Explanations come in many different forms. One that is of interest here is presenting an existing review of the recommended item as the explanation. The challenge is in selecting a suitable review, which is customarily addressed by assessing the relative importance or “attention” of each review to the recommendation objective. Our focus is improving review-level explanation by leveraging additional information in the form of questions and answers (QA). The proposed framework employs QA in an attention mechanism that aligns reviews to various QAs of an item and assesses their contribution …
Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw
Memory Network-Based Interpreter Of User Preferences In Content-Aware Recommender Systems, Nhu Thuat Tran, Hady W. Lauw
Research Collection School Of Computing and Information Systems
This article introduces a novel architecture for two objectives recommendation and interpretability in a unified model. We leverage textual content as a source of interpretability in content-aware recommender systems. The goal is to characterize user preferences with a set of human-understandable attributes, each is described by a single word, enabling comprehension of user interests behind item adoptions. This is achieved via a dedicated architecture, which is interpretable by design, involving two components for recommendation and interpretation. In particular, we seek an interpreter, which accepts holistic user’s representation from a recommender to output a set of activated attributes describing user preferences. …
Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu
Graphsearchnet: Enhancing Gnns Via Capturing Global Dependencies For Semantic Code Search, Shangqing Liu, Xiaofei Xie, Jjingkai Siow, Lei Ma, Guozhu Meng, Yang Liu
Research Collection School Of Computing and Information Systems
Code search aims to retrieve accurate code snippets based on a natural language query to improve software productivity and quality. With the massive amount of available programs such as (on GitHub or Stack Overflow), identifying and localizing the precise code is critical for the software developers. In addition, Deep learning has recently been widely applied to different code-related scenarios, ., vulnerability detection, source code summarization. However, automated deep code search is still challenging since it requires a high-level semantic mapping between code and natural language queries. Most existing deep learning-based approaches for code search rely on the sequential text ., …
A Deep Ensemble Method For Multi-Agent Reinforcement Learning: A Case Study On Air Traffic Control, Supriyo Ghosh, Sean Laguna, Laura Wynter, Laura Wynter, Hasan Poonawala
A Deep Ensemble Method For Multi-Agent Reinforcement Learning: A Case Study On Air Traffic Control, Supriyo Ghosh, Sean Laguna, Laura Wynter, Laura Wynter, Hasan Poonawala
Research Collection School Of Computing and Information Systems
Reinforcement learning (RL), a promising framework for data-driven decision making in an uncertain environment, has successfully been applied in many real-world operation and control problems. However, the application of RL in a large-scale decentralized multi-agent environment remains a challenging problem due to the partial observability and limited communications between agents. In this paper, we develop a model-based kernel RL approach and a model-free deep RL approach for learning a decentralized, shared policy among homogeneous agents. By leveraging the strengths of both these methods, we further propose a novel deep ensemble multi-agent reinforcement learning (MARL) method that efficiently learns to arbitrate …
An Efficient Transformer-Based Model For Vietnamese Punctuation Prediction, Hieu Tran, Cuong V. Dinh, Hong Quang Pham, Binh T. Nguyen
An Efficient Transformer-Based Model For Vietnamese Punctuation Prediction, Hieu Tran, Cuong V. Dinh, Hong Quang Pham, Binh T. Nguyen
Research Collection School Of Computing and Information Systems
In both formal and informal texts, missing punctuation marks make the texts confusing and challenging to read. This paper aims to conduct exhaustive experiments to investigate the benefits of the pre-trained Transformer-based models on two Vietnamese punctuation datasets. The experimental results show our models can achieve encouraging results, and adding Bi-LSTM or/and CRF layers on top of the proposed models can also boost model performance. Finally, our best model can significantly bypass state-of-the-art approaches on both the novel and news datasets for the Vietnamese language. It can gain the corresponding performance up to 21.45%21.45% and 18.27%18.27% in the overall F1-scores.
Accurate And Cost-Effective Traffic Information Acquisition Using Adaptive Sampling: Centralized And V2v Schemes, Shiau Hong Lim, Yeow Khiang Chia, Laura Wynter
Accurate And Cost-Effective Traffic Information Acquisition Using Adaptive Sampling: Centralized And V2v Schemes, Shiau Hong Lim, Yeow Khiang Chia, Laura Wynter
Research Collection School Of Computing and Information Systems
The new generation of GPS-based tolling systems allow for a much higher degree of road sensing than has been available up to now. We propose an adaptive sampling scheme to collect accurate real-time traffic information from large-scale implementations of on-board GPS-based devices over a road network. The goal of the system is to minimize the transmission costs over all vehicles while satisfying requirements in the accuracy and timeliness of the traffic information obtained. The system is designed to make use of cellular communication as well as leveraging additional technologies such as roadside units equipped with WiFi and vehicle-to-vehicle (V2V) dedicated …
Robust Commuter Movement Inference From Connected Mobile Devices, Achintya Kundu, Pengqian Yu, Laura Wynter, Shiau Hong Lim
Robust Commuter Movement Inference From Connected Mobile Devices, Achintya Kundu, Pengqian Yu, Laura Wynter, Shiau Hong Lim
Research Collection School Of Computing and Information Systems
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of methods is to better accommodate the real-world characteristics found in federated training, such as the lack of IID data across parties, the need for robustness to outliers or stragglers, and the requirement to perform well on party-specific datasets. We achieve this through a problem formulation that allows the central server to employ robust ways of aggregating the local models while keeping the structure of local computation intact. Without making any statistical assumption on the …
Convergence Of Media Attention Across 129 Countries, Jisun An, Hassan Aldarbesti, Haewoon Kwak
Convergence Of Media Attention Across 129 Countries, Jisun An, Hassan Aldarbesti, Haewoon Kwak
Research Collection School Of Computing and Information Systems
The objective of this study is to assess the longitudinal trends of media similarity and dissimilarity on the international scale. As news value has well-established political, cultural, and economic consequences, the degree to which media coverage and content is converging across countries has implications for international relations. To study this convergence, we use the daily data of the 100 topics that were over-reported in each country, compared to other countries, from March 7 to October 9, 2016. The results of this analysis indicate that two complementary patterns–globalization and domestication–explain the media attention across the countries. We conclude that this attention …
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Large-Scale Online Feature Selection For Ultra-High Dimensional Sparse Data, Yue Wu, Steven C. H. Hoi, Tao Mei, Nenghai Yu
Research Collection School Of Computing and Information Systems
Feature selection (FS) is an important technique in machine learning and data mining, especially for large scale high-dimensional data. Most existing studies have been restricted to batch learning, which is often inefficient and poorly scalable when handling big data in real world. As real data may arrive sequentially and continuously, batch learning has to retrain the model for the new coming data, which is very computationally intensive. Online feature selection (OFS) is a promising new paradigm that is more efficient and scalable than batch learning algorithms. However, existing online algorithms usually fall short in their inferior efficacy. In this article, …
Revisiting Assert Use In Github Projects, Pavneet Singh Kochhar, David Lo
Revisiting Assert Use In Github Projects, Pavneet Singh Kochhar, David Lo
Research Collection School Of Computing and Information Systems
Assertions are often used to test the assumptions that developers have about a program. An assertion contains a boolean expression which developers believe to be true at a particular program point. It throws an error if the expression is not satisfied, which helps developers to detect and correct bugs. Since assertions make developer assumptions explicit, assertions are also believed to improve under-standability of code. Recently, Casalnuovo et al. analyse C and C++ programs to understand the relationship between assertion usage and defect occurrence. Their results show that asserts have a small effect on reducing the density of bugs and developers …
Retrieval-Based Face Annotation By Weak Label Regularized Local Coordinate Coding, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu
Retrieval-Based Face Annotation By Weak Label Regularized Local Coordinate Coding, Dayong Wang, Steven C. H. Hoi, Ying He, Jianke Zhu
Research Collection School Of Computing and Information Systems
Retrieval-based face annotation is a promising paradigm in mining massive web facial images for automated face annotation. Such an annotation paradigm usually encounters two key challenges. The first challenge is how to efficiently retrieve a short list of most similar facial images from facial image databases, and the second challenge is how to effectively perform annotation by exploiting these similar facial images and their weak labels which are often noisy and incomplete. In this paper, we mainly focus on tackling the second challenge of the retrieval-based face annotation paradigm. In particular, we propose an effective Weak Label Regularized Local Coordinate …
Modeling 3d Articulated Motions With Conformal Geometry Videos (Cgvs), Dao T. P. Quynh, Ying He, Xiaoming Chen, Jiazhi Xia, Qian Sun, Steven C. H. Hoi
Modeling 3d Articulated Motions With Conformal Geometry Videos (Cgvs), Dao T. P. Quynh, Ying He, Xiaoming Chen, Jiazhi Xia, Qian Sun, Steven C. H. Hoi
Research Collection School Of Computing and Information Systems
3D articulated motions are widely used in entertainment, sports, military, and medical applications. Among various techniques for modeling 3D motions, geometry videos (GVs) are a compact representation in that each frame is parameterized to a 2D domain, which captures the 3D geometry (x, y, z) to a pixel (r, g, b) in the image domain. As a result, the widely studied image/video processing techniques can be directly borrowed for 3D motion. This paper presents conformal geometry videos (CGVs), a novel extension of the traditional geometry videos by taking into the consideration of the isometric nature of 3D articulated motions. We …
Intelligence Through Interaction: Towards A Unified Theory For Learning, Ah-Hwee Tan, Gail A. Carpenter, Stephen Grossberg
Intelligence Through Interaction: Towards A Unified Theory For Learning, Ah-Hwee Tan, Gail A. Carpenter, Stephen Grossberg
Research Collection School Of Computing and Information Systems
Machine learning, a cornerstone of intelligent systems, has typically been studied in the context of specific tasks, including clustering (unsupervised learning), classification (supervised learning), and control (reinforcement learning). This paper presents a learning architecture within which a universal adaptation mechanism unifies a rich set of traditionally distinct learning paradigms, including learning by matching, learning by association, learning by instruction, and learning by reinforcement. In accordance with the notion of embodied intelligence, such a learning theory provides a computational account of how an autonomous agent may acquire the knowledge of its environment in a real-time, incremental, and continuous manner. Through a …
Capacitated Network Revenue Management Through Shadow Pricing, Mustapha Bouhtou, Madiagne Diallo, Laura Wynter
Capacitated Network Revenue Management Through Shadow Pricing, Mustapha Bouhtou, Madiagne Diallo, Laura Wynter
Research Collection School Of Computing and Information Systems
In this paper, we analyze a method that links Lagrange multipliers from a resource allocation problem to the problem of revenue or profit maximization. This technique, first proposed in the transportation science literature by [7] has important implications for telecommunication network pricing. Indeed, the framework provides a generalization of telecommunication resource allocation/shadow price-based schemes such as those of [6] and [9], in that it permits the optimization of the shadow prices themselves, through a computationally simple procedure. We analyze the extent to which revenue can be maximized on a network that uses shadow-price-based prices, and how to deal with cases …