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LECF: recommendation via learnable edge collaborative filtering

science China(Information sciences) 2022年第1期65卷 116-130页

作者： Shitao XIAO Yingxia SHAO Yawen LI Hongzhi YIN Yanyan SHEN Bin CUI School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing Key Lab of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications School of Information Technology and Electrical Engineering The University of Queensland Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Computer Science and Technology Peking University National Engineering Laboratory for Big Data Analysis and Applications Peking University

The core of recommendation models is estimating the probability that a user will like an item based on historical interactions. Existing collaborative filtering(CF) algorithms compute the likelihood by utilizing simple relationships between objects, e.g., user-item, item-item, or user-user. They always rely on a single type of object-object relationship, ignoring other useful relationship information in data. In this paper, we model an interaction between user and item as an edge and propose a novel CF framework, called learnable edge collaborative filtering(LECF). LECF predicts the existence probability of an edge based on the connections among edges and is able to capture the complex relationship in data. Specifically, we first adopt the concept of line graph where each node represents an interaction edge; then calculate a weighted sum of similarity between the query edge and the observed edges(i.e., historical interactions) that are selected from the neighborhood of query edge in the line graph for a recommendation. In addition, we design an efficient propagation algorithm to speed up the training and inference of LECF. Extensive experiments on four public datasets demonstrate LECF can achieve better performance than the state-of-the-art methods.

关键词： collaborative filtering recommender system implicit feedback edge-edge similarity line graph

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AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle Designs 37

AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Ve...

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Cobb, Adam D. Roy, Anirban Elenius, Daniel Heim, F. Michael Swenson, Brian Whittington, Sydney Walker, James D. Bapty, Theodore Hite, Joseph Ramani, Karthik McComb, Christopher Jha, Susmit Computer Science Laboratory SRI International United States Mechanical Engineering Division Southwest Research Institute United States Intelligent Systems Division Southwest Research Institute United States Institute of Software Integrated Systems Vanderbilt University United States School of Mechanical Engineering Purdue University United States Department of Mechanical Engineering Carnegie Mellon University United States

ISBN: (纸本)9781713899921

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer-aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27, 714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details;a STandard for the Exchange of Product (STEP) model data;a 3D CAD design using a stereolithography (STL) file format;a 3D point cloud for the shape of the design;and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446, baseline models and code at https://***/SRI-CSL/AircraftVerse, and the dataset description at https://***/. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： computer aided logic design

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DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis

arXiv

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arXiv 2024年

作者： Deng, Kaijun Zheng, Dezhi Xie, Jindong Wang, Jinbao Xie, Weicheng Shen, Linlin Song, Siyang Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China Department of Computer Science University of Exeter United Kingdom

Accurately synthesizing talking face videos and capturing fine facial features for individuals with long hair presents a significant challenge. To tackle these challenges in existing methods, we propose a decomposed per-embedding Gaussian fields (DEGSTalk), a 3D Gaussian Splatting (3DGS)-based talking face synthesis method for generating realistic talking faces with long hairs. Our DEGSTalk employs Deformable Pre-Embedding Gaussian Fields, which dynamically adjust pre-embedding Gaussian primitives using implicit expression coefficients. This enables precise capture of dynamic facial regions and subtle expressions. Additionally, we propose a Dynamic Hair-Preserving Portrait Rendering technique to enhance the realism of long hair motions in the synthesized videos. Results show that DEGSTalk achieves improved realism and synthesis quality compared to existing approaches, particularly in handling complex facial dynamics and hair preservation. Our code will be publicly available at https://***/CVI-SZU/DEGSTalk. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

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RAT: A Refactoring-Aware Tool for Tracking Code History

RAT: A Refactoring-Aware Tool for Tracking Code History

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International Conference on software engineering - Companion, ICSE, Companion

作者： Feifei Niu Junqian Shao Chaofan Xu Christoph Mayr-Dorn Wesley K. G. Assunção Liguo Huang Chuanyi Li Jidong Ge Bin Luo Alexander Egyed State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Department of Computer Science North Carolina State University North Carolina USA Department of Computer Science and Engineering Southern Methodist University Dallas Texash USA

ISBN: (数字)9798400705021

ISBN: (纸本)9798350351781

History of code elements is essential for software maintenance tasks. However, code refactoring is one of the main causes that makes obtaining a consistent view on code evolution difficult as renaming or moving source code elements break such history. To this end, this paper presents RAT, a refactoring-aware tool for keeping track of code elements evolution across time, not just in terms of revisions but also in terms of refactoring. This is the first tool that enables fine-grained code element traceability of the whole repository. empirical evaluation of leveraging our tool in three bug localization techniques relying on code history shows significant improvement in localization accuracy. Based on our findings, we believe that many of the state-of-the-art approaches using past source code data would benefit from our tool. Demo Tool: https://***/feifeiniu-se/RAT_Demo Demo Video: https://***/VI_xwUaIPp4

关键词： Location awareness software maintenance Codes Source coding computer bugs Maintenance engineering History

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Amplified locality-sensitive hashing-based recommender systems with privacy protection

Amplified locality-sensitive hashing-based recommender syste...

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作者： Chi, Xiaoxiao Yan, Chao Wang, Hao Rafique, Wajid Qi, Lianyong School of Information Science and Engineering Qufu Normal University Rizhao China Department of Computer Science Norwegian University of Science and Technology Gjøvik Norway State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China

With the advent of Internet of Things (IoT) age, the variety and volume of web services have been increasing at a fast speed. This often leads to users' selections for web services more complicated. Under the circumstance, a variety of methods such as collaborative filtering are adopted to deal with this challenging situation. While traditional collaborative filtering method has some shortcomings, one of which is that only centralized user-service data are considered while distributed quality data from multiple platform are ignored. Generally, service recommendation across different platforms often involves data communication among multiple platforms, during which user privacy may be disclosed and much computational time is required. Considering these challenges, a unique amplified locality-sensitive hashing (LSH)-based service recommendation method, that is, SRAmplified-LSH, is proposed in the article. SRAmplified-LSH can guarantee a good balance between accuracy and efficiency of recommendation and user privacy information. Finally, extensive experiments deployed on WS-DREAM dataset validate the feasibility of our proposed method. © 2020 John Wiley & Sons, Ltd.

关键词： Efficiency

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A robust TDoA-based localization and tracking method designed for intelligent recommender systems

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Information sciences 2025年 717卷

作者： Mei, Hong-Yuan Zhou, Zhi Shen, Fu-Rao Zhao, Jian State Key Laboratory for Novel Software Technology Nanjing China Department of Computer Science and Technology Nanjing University China School of Artificial Intelligence Nanjing University China School of Electronic Science and Engineering Nanjing University China

In today's fast-paced society, the demand for intelligent Recommender Systems(RS) is increasing day by day. Users may hope that RSs can take their real-time situation into consideration to help them make timely decisions. To fulfill this goal, RSs need to accurately understand users' information, especially their real-time location. Therefore, localization of the users is very helpful in building an intelligent RS. Meanwhile, great efforts have been made to deal with localization based on time-difference-of-arrival (TDoA) measurements in the past decades. It is widely used in applications such as radar, sonar, sensor networks, and mobile communications. However, such a task is still challenging in complex real-world scenarios where large-scale noise and none-light-of-sight (NLoS) occur. To address this challenge, we propose a noise-tolerant method consisting of two stages: the Robust Multilateration Solver (RMS) and the Restricted Particle Filter (RPF). In the first stage, anomaly detection is adopted to eliminate outliers, and NLoS effects are mitigated by a data augmentation strategy. Then a rough estimate of the current position is obtained by combining two modified multilateration algorithms. In the second stage, the rough estimate is passed through an enhanced particle filter in order to reduce its bias caused by heavy Gaussian noise and smooth the motion trajectory. Meanwhile, localization rationality is guaranteed by confining the position to a predefined area. Sufficient testing in real indoor environments and simulation results demonstrate the effectiveness of our method and reveal its fitness for various industrial fields. © 2025

关键词： Indoor localization Recommender systems Source tracking TDoA

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($k,\alpha$k,α)-Coverage for RIS-Aided mmWave Directional Communication

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IEEE Transactions on Mobile Computing 2022年第12期22卷 7482-7497页

作者： Xueyang Hu Tian Liu Tao Shu Computer Science and Software Engineering Department Auburn University Auburn AL USA Zhejiang Laboratory Hangzhou China

Reconfigurable intelligent surface (RIS) offers a new way to provide controllable non line-of-sight (NLoS) propagation paths for millimeter-wave (mmWave) directional communication to overcome the performance degradation caused by line-of-sight blockage. However, current coverage models do not consider the impact of path direction difference on path's availability, which is a crucial property of mmWave directional communication network. In the article, we propose a new coverage model called $(k,\alpha)$ -coverage. A receiver is $(k,\alpha)$ -covered if it is covered by at least $k$ RISs to have $k$ different NLoS path directions and the angular separation between any two adjacent path directions is at least $\alpha$ . In this case, when the current communication direction is blocked by an obstacle, other RIS created paths are still likely to be available for transmission, which increases the robustness of mmWave directional communication. To tackle the problem of using the least number of RISs to achieve the $(k,\alpha)$ -coverage, we formally define the $(k,\alpha)$ -coverage models and propose methods to verify if the target area is $(k,\alpha)$ -covered by the given set of RISs. Then, we solve the problem under both deterministic and random RIS deployment schemes. For the deterministic deployment scheme, we derive the optimal $k$ -sided regular polygon deployment patterns and use it to achieve area $(k,\alpha)$ -coverage. An analytical performance bound on the number of RISs needed is also derived. For the random RIS deployment scheme, we derive the $(k,\alpha)$ -coverage probability under uniform and spatial-Poisson RIS distributions. Finally, extensive simulation results are provided to validate our analyses.

关键词： Millimeter wave communication Receivers Wireless sensor networks Robustness Line-of-sight propagation Mobile computing Wireless communication

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DisCOV: Distributed COVID-19 Detection on X-Ray Images with Edge-Cloud Collaboration

DisCOV: Distributed COVID-19 Detection on X-Ray Images with ...

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2022 IEEE World Congress on Services, SERVICES 2022

作者： Xu, Xiaolong Tian, Hao Zhang, Xuyun Qi, Lianyong He, Qiang Dou, Wanchun Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Macquarie University Department of Computing Sydney Australia Qufu Normal University School of Information Science and Engineering Rizhao China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Nanjing University State Key Laboratory for Novel Software Technology Nanjing China

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]

ISBN: (数字)9781665481311

ISBN: (纸本)9781665481311

[J1C2 Presentation Abstract at IEEE SERVICES 2022 for IEEE Transactions on Services Computing DOI 10.1109/TSC.2022.3142265]

关键词： COVID-19 Image edge detection Collaboration X-ray imaging

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Fraction Arithmetic Operations Performed by $\mathbf{P}$ Systems

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Chinese Journal of Electronics 2023年第4期22卷 689-694页

作者： GUO Ping ZHANG Hong CHEN Haizhu CHEN Jingxin College of Computer Science Chongqing University Chongqing China Chongqing Key Laboratory of Software Theory and Technology Chongqing China Department of Software Engineering Chongqing College of Electronic Engineering Chongqing China Chongqing Branch Agricultural Bank of China Chongqing China

Arithmetical operations are fundamental in computing models. But arithmetic operations in membrane computing are restricted in integer field. In this paper, we present fraction arithmetic $\mathbf{P}$ systems for performing addition, subtraction, multiplication, division on fractions through designing the rules with priority. Some examples are given to illustrate how to compute the arithmetical fractions in these systems and show that the designed rules can carry out correct arithmetic computations of fractions.

关键词： Computational modeling Arithmetic

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