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arXiv 2023年

作者： Wang, Xiangmeng Li, Qian Yu, Dianer Huang, Wei Li, Qing Xu, Guandong School of Computer Science University of Technology Sydney Sydney Australia School of Elec Eng Comp and Math Sci Curtin University Perth Australia Deep Learning Theory Team RIKEN Center for AIP Tokyo Japan Department of Computing Hong Kong Polytechnic University Hong Kong

Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF). One classical approach in GCF is to learn user and item embeddings with Graph Convolutional Network (GCN) and utilize these embeddings for CF models. However, existing GCN-based methods are insufficient in generating satisfactory embeddings for CF models. This is because they fail to model complex node dependencies and variable relation dependencies from a given graph, making the learned embeddings fragile to uncover the root causes of user interests. In this work, we propose to integrate causal modeling with the learning process of GCN-based GCF models, leveraging causality-aware graph embeddings to capture complex causal relations in recommendations. We complete the task by 1) Causal Graph conceptualization, 2) Neural Causal Model parameterization and 3) Variational inference for Neural Causal Model. Our Neural Causal Model, called Neural Causal Graph Collaborative Filtering (NCGCF), enables causal modeling for GCN-based GCF to facilitate accurate recommendations. Extensive experiments show that NCGCF provides precise recommendations that align with user preferences. We release our code and processed datasets at https://***/Chrystalii/CNGCF. © 2023, CC BY.

关键词： Recommender systems

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Addressing the Gap Between How Students and Professionals Read Code

Journal of Computing Sciences in Colleges

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Journal of computing sciences in Colleges 2024年第3期40卷

作者： Matthew Woerner David Socha Mark Kochanski Computing & Software Systems School of Science Technology Engineering & Math University of Washington Bothell Bothell WA

This paper reports on the design and evaluation of a Canvas module to help students learn how to do what professionals commonly have to do: manually read and understand code by tracing calls into and through multiple files in novel codebases in order to understand how the code actually works before modifying the code. This work was motivated by our prior research that showed that students, but not professionals, exhibited a common set of mistakes when reading novel codebases. Our goal in this current study was to see if we could create a module that would help students move toward professional practice in this respect. We based the module design upon a set of lessons derived from our analysis of the videos of ten students doing code reading exercises. Our evaluation of the results of the nine students who went through the Canvas module suggest that the module indeed improved their ability to read novel codebases. The module is available online for anyone to use.

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Usability Evaluation of Low-Cost Smart Pill Dispenser by Health Care Practitioners

Usability Evaluation of Low-Cost Smart Pill Dispenser by Hea...

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Future Technologies Conference, FTC 2020

作者： Mugisha, Gift Arnold Muhumuza, Christine Uzoka, Faith-Michael Nwafor-Okoli, Chinyere Nabunje, Joletta Arindagye, Melody Bukenya, Justine N. Shenyang Aerospace University Shenyang China School of Public Health College of Health Science Makerere University Kampala Uganda Department of Math and Computing Mount Royal University Calgary Canada Canadian Institute for Innovation and Development Calgary Canada School of Medicine College of Health Science Makerere University Kampala Uganda Faculty of Medicine Mbarara University of Science and Technology Mbarara Uganda

ISBN: (纸本)9783030630911

Several health complications arise from non-adherence to medications. Most existing tools lack the ability to facilitate communication between the patient and their healthcare worker, especially in critical situations. We developed a smart pill dispenser/medical assistant to help address some adherence changes. This innovation is equipped with intelligent remote communication capabilities between the patient and a medical professional, and can be used in many settings such as hospital and home facilities to assist in dispensing of medications. The purpose of this study was to explore the user perception and attitudes towards the smart pill dispenser in addressing drug dispensing challenges in hospitals in Kampala, Uganda. This was a pilot study that employed qualitative data collection methods among health providers in four hospitals, involving 17 key informant interviews. The findings revealed that the prototype was easy to use and very acceptable to many health workers. In particular, the respondents rated the prototype highly based on supporting features such camera, video enabled two-way communication and water dispenser. However, some modifications were proposed such as decreasing the size, and increasing the number of dispensing channels. This study provides the impetus for an agile improvement strategy that includes prototype refinement and further acceptance study among users in more health facilities. © 2021, Springer Nature Switzerland AG.

关键词： Hospitals

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Joint Microstrip Selection and Beamforming Design for MmWave Systems with Dynamic Metasurface Antennas

arXiv

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arXiv 2022年

作者： Huang, Wei Zhang, Haiyang Shlezinger, Nir Eldar, Yonina C. School of Computing Science and Information Engineering Hefei University of Technology Hefei China School of Communications and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of ECE Ben-Gurion University of the Negev Beer-Sheva Israel Faculty of Math and CS Weizmann Institute of Science Rehovot Israel

Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems. In this paper, we study the downlink millimeter wave (mmWave) DMA systems with limited number of radio frequency (RF) chains. By using a specific DMA structure, an equivalent mmWave channel model is first explicitly characterized. Based on that, we propose an effective joint microstrip selection and beamforming scheme to accommodate for the limited number of RF chains. A low-complexity digital beamforming solution with channel gain-based microstrip selection is developed, while the analog beamformer is obtained via a coordinate ascent method. The proposed scheme is numerically shown to approach the performance of DMAs without RF chain reduction, verifying the effectiveness of the proposed schemes. © 2022, CC BY.

关键词： Millimeter waves

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Automatic Discovery of Heterogeneous Machine Learning Pipelines: An Application to Natural Language Processing 28

Automatic Discovery of Heterogeneous Machine Learning Pipeli...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Estevez-Velarde, Suilan Gutiérrez, Yoan Montoyo, Andrés Almeida-Cruz, Yudivián School of Math and Computer Science University of Havana Cuba University of Alicante Spain Department of Languages and Computing Systems University of Alicante Spain

ISBN: (纸本)9781952148279

This paper presents AutoGOAL, a system for automatic machine learning (AutoML) that uses heterogeneous techniques. In contrast with existing AutoML approaches, our contribution can automatically build machine learning pipelines that combine techniques and algorithms from different frameworks, including shallow classifiers, natural language processing tools, and neural networks. We define the heterogeneous AutoML optimization problem as the search for the best sequence of algorithms that transforms specific input data into the desired output. This provides a novel theoretical and practical approach to AutoML. Our proposal is experimentally evaluated in diverse machine learning problems and compared with alternative approaches, showing that it is competitive with other AutoML alternatives in standard benchmarks. Furthermore, it can be applied to novel scenarios, such as several NLP tasks, where existing alternatives cannot be directly deployed. The system is freely available and includes in-built compatibility with a large number of popular machine learning frameworks, which makes our approach useful for solving practical problems with relative ease and effort. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Machine learning

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Demo Application for the AutoGOAL Framework 28

Demo Application for the AutoGOAL Framework

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28th International Conference on Computational Linguistics, COLING 2020

作者： Estevez-Velarde, Suilan Piad-Morffis, Alejandro Gutiérrez, Yoan Montoyo, Andrés Muñoz, Rafael Almeida-Cruz, Yudivián School of Math and Computer Science University of Havana Cuba University of Alicante Spain Department of Languages and Computing Systems University of Alicante Spain

ISBN: (纸本)9781952148286

This paper introduces a web demo that showcases the main characteristics of the AutoGOAL framework. AutoGOAL is a framework in Python for automatically finding the best way to solve a given task. It has been designed mainly for automatic machine learning (AutoML) but it can be used in any scenario where several possible strategies are available to solve a given computational task. In contrast with alternative frameworks, AutoGOAL can be applied seamlessly to Natural Language Processing as well as structured classification problems. This paper presents an overview of the framework’s design and experimental evaluation in several machine learning problems, including two recent NLP challenges. The accompanying software demo is available online1 and full source code is provided under the MIT open-source license2 © COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of System Demonstrations.

关键词： Python

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GPT-Based Automated Induction: Vulnerability Detection in Medical Software

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Deng, Liangjun Lei, Hang Khan, Fazlullah Srivastava, Gautam Chen, Jingxue Haque, Mainul University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China University of Nottingham Ningbo China School of Computer Science and the Next-Generation Internet of Everything Laboratory Faculty of Science and Engineering Zhejiang Ningbo315104 China Lovely Professional University Division of Research and Development Phagwara India Chandigarh University University Centre of Research and Development Mohali India Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact & Outcome Punjab Rajpura140401 India University of Nottingham Ningbo China Department of Mathematics Faculty of Science and Engineering Zhejiang Ningbo315104 China

Integrating Natural Language Processing (NLP) with Generative Pre-trained Transformer (GPT) models plays a pivotal role in enhancing the accuracy and efficiency of healthcare software, which is essential for patient safety and providing high-quality care. The precision of healthcare software is fundamental to protecting the well-being of the patient. In addition, it can ensure the delivery of superior care, maintain the integrity of healthcare systems, and promote trust and cost-effectiveness. It is necessary to emphasize the importance of software reliability in its development and deployment. Symbolic execution serves as a vital technology in automated vulnerability detection. However, symbolic execution often faces problems such as path explosion, which seriously affects efficiency. Although there have been several studies to reduce the number of computational paths in symbolic execution, this problem remains a major obstacle. Therefore, more efficient solutions are urgently needed to ensure the software security. This paper proposes a large-scale language model(LLM) induction method mitigating path explosion applied to symbolic execution engines. In contrast to traditional symbolic execution engines, which often result in timeout or out-of-memory detection, our approach achieves the task of detecting vulnerabilities in seconds. Furthermore, our proposal improves the scalability of symbolic execution, allowing more extensive and complex programs to be analyzed without significant increases in computational resources or time. This scalability is crucial to tackling modern software systems and improving the efficiency and effectiveness of automated defect verification in healthcare software. © 2025 IEEE. All rights reserved,

关键词： Scalability

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Code Reading: How Students and Professionals Differ

Journal of Computing Sciences in Colleges

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Journal of computing sciences in Colleges 2023年第1期39卷

作者： Matthew Woerner David Socha Mark Kochanski Computing & Software Systems School of Science Technology Engineering & Math University of Washington Bothell Bothell WA

This paper reports on a series of semi-structured interviews and code reading exercises conducted with 10 undergraduate students and 11 professional software engineers in order to better understand how students and professionals differ in how they come to understand a codebase that is novel to them. The goal was to uncover distinctions to help us design teaching activities to help students know how to read source code whose call stack spans multiple methods and files. Students had a more difficult time correctly reading source code than professionals and tended to lack a clear process for tackling code reading exercises. Professionals tended to tackle the code reading exercises more methodically, checked their assumptions, and avoided spending time reading code irrelevant to the exercise presented to them.

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Steady-State Analysis of Modular Multi-Parallel Rectifiers (MMR) with Lossy Elements

Steady-State Analysis of Modular Multi-Parallel Rectifiers (...

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International (EPE-PEMC) Power Electronics and Motion Control Conference

作者： Ali Sunbul Firuz Zare Rahul Sharma Arindam Ghosh Power and Energy Group The University of Queensland Brisbane Australia School of Electrical Engineering Computing and Math. Science Curtin University Perth Australia

This paper presents a comprehensive steady-state analysis for active front-end Modular Multi-parallel Rectifiers (MMR) including parasitic elements. Generalised mathematical representations for the system's gain and conduction loss effect are provided. In addition, the impact of the inductor losses and the firing angle on the system's performance is investigated. A60kW Simulink model is utilised to validate the proposed mathematical model and the system characteristics and operating modes.

关键词： Total harmonic distortion Firing Software packages Conferences Rectifiers Power electronics Steady-state

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Overview of the ehealth knowledge discovery challenge at iberlef 2020

Overview of the ehealth knowledge discovery challenge at ibe...

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2020 Iberian Languages Evaluation Forum, IberLEF 2020

作者： Piad-Morffis, Alejandro Gutiérrez, Yoan Cañizares-Diaz, Hian Estévez-Velarde, Suilan Montoyo, Andrés Almeida-Cruz, Yudivian School of Math and Computer Science University of Havana La Habana10400 Cuba Department of Language and Computing Systems University of Alicante Alicante03690 Spain University Institute for Computing Research University of Alicante Alicante03690 Spain

This paper summarises the results of the third edition of the eHealth Knowledge Discovery (KD) challenge, hosted at the Iberian Language Evaluation Forum 2020. The eHealth-KD challenge proposes two computational tasks involving the identification of semantic entities and relations in natural language text, focusing on Spanish language health documents. In this edition, besides text extracted from medical sources, Wikipedia content was introduced into the corpus, and a novel transfer-learning evaluation scenario was designed that challenges participants to create systems that provide crossdomain generalisation. A total of eight teams participated with a variety of approaches including deep learning end-to-end systems as well as rule-based and knowledge-driven techniques. This paper analyses the most successful approaches and highlights the most interesting challenges for future research in this field. © 2020 Copyright for this paper by its authors. Use permitted under.

关键词： eHealth

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