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- research-articleOctober 2024
A complementary fused method using GRU and XGBoost models for long-term solar energy hourly forecasting
Expert Systems with Applications: An International Journal (EXWA), Volume 254, Issue Chttps://doi.org/10.1016/j.eswa.2024.124286AbstractSolar photovoltaic (PV) energy plays a vital role in global renewable energy generation. Accurate and reliable solar energy forecasting is the key to improving energy scheduling, planning, and intelligent decision-making. However, existing ...
Highlights- Ensembled XGBoost and GRU models for long-term solar energy hourly forecasting.
- A well-designed feature engineering is introduced in this study.
- Achieved first place in the UNiLAB Algorithm Competition Track 3 competition.
- research-articleOctober 2024
RCFI-Net: A reliable correspondences evaluation and feature interaction network for fast and accurate point cloud registration
Abstract3D point cloud registration is a crucial task in computer vision, robotics, and safeguarding cultural artifacts in the digital realm. However, maintaining a balance between efficiency and accuracy during the 3D point cloud registration process ...
Highlights- A fast and accurate point cloud registration method is proposed without iteration or an initial transformation.
- A transformer with position encoding is proposed to learn the contextual information and enhance cross-feature fusion.
- ...
- ArticleAugust 2024
Federated Knowledge Graph Embedding Unlearning via Diffusion Model
AbstractKnowledge graph (KG) embedding representation provides a foundation for knowledge reasoning and applications by mapping entities and relations into vector space. Federated learning (FL) promotes the development and application of artificial ...
- research-articleAugust 2024
Open-world knowledge embedding in a low-text resource environment
Applied Intelligence (KLU-APIN), Volume 54, Issue 22Pages 11564–11576https://doi.org/10.1007/s10489-024-05744-zAbstractThe objective of knowledge embedding (KE) is to represent entities and relations in a knowledge graph (KG) in a continuous low-dimensional vector space, thereby facilitating the integration of the KG into various downstream applications. Existing ...
- research-articleAugust 2024
STONE: A Spatio-temporal OOD Learning Framework Kills Both Spatial and Temporal Shifts
KDD '24: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 2948–2959https://doi.org/10.1145/3637528.3671680Traffic prediction is a crucial task in the Intelligent Transportation System (ITS), receiving significant attention from both industry and academia. Numerous spatio-temporal graph convolutional networks have emerged for traffic prediction and achieved ...
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- research-articleSeptember 2024
Research on the discourse power evaluation of academic journals from the perspective of multiple fusion: Taking Medicine, General and Internal journals as an example
Journal of Information Science (JIPP), Volume 50, Issue 4Pages 811–830https://doi.org/10.1177/01655515221107334In the open science environment, this article evaluates the discourse power of academic journals from the perspective of multiple integration. It is conducive to scientific research management and provides a reference for enriching and perfecting the ...
- research-articleJuly 2024
SEBGM: Sentence Embedding Based on Generation Model with multi-task learning
AbstractSentence embedding, which aims to learn an effective representation of a sentence, is a significant part for downstream tasks. Recently, using contrastive learning and pre-trained model, most methods of sentence embedding achieve encouraging ...
Highlights- We design a contrastive framework based on generation model with multi-task learning.
- We propose a continuous prompt template to help model digest sentence embedding task.
- State-of-the-art results are achieved on the semantic ...
- short-paperJuly 2024
Learnersourcing: Student-generated Content @ Scale: 2nd Annual Workshop
- Steven Moore,
- Anjali Singh,
- Xinyi Lu,
- Hyoungwook Jin,
- Hassan Khosravi,
- Paul Denny,
- Christopher Brooks,
- Xu Wang,
- Juho Kim,
- John Stamper
L@S '24: Proceedings of the Eleventh ACM Conference on Learning @ ScalePages 559–562https://doi.org/10.1145/3657604.3664643We propose the second annual workshop on Learnersourcing: Student-generated Content @ Scale. This full-day workshop is designed to explore the vast potential of learnersourcing, which combines the efforts of humans, AI, and other data sources to create ...
- research-articleJuly 2024
CodeTailor: LLM-Powered Personalized Parsons Puzzles for Engaging Support While Learning Programming
L@S '24: Proceedings of the Eleventh ACM Conference on Learning @ ScalePages 51–62https://doi.org/10.1145/3657604.3662032Learning to program can be challenging, and providing high-quality and timely support at scale is hard. Generative AI and its products, like ChatGPT, can create a solution for most intro-level programming problems. However, students might use these tools ...
- research-articleJuly 2024
Generative Students: Using LLM-Simulated Student Profiles to Support Question Item Evaluation
L@S '24: Proceedings of the Eleventh ACM Conference on Learning @ ScalePages 16–27https://doi.org/10.1145/3657604.3662031Evaluating the quality of automatically generated question items has been a long standing challenge. In this paper, we leverage LLMs to simulate student profiles and generate responses to multiple-choice questions (MCQs). The generative students' ...
- proceedingJuly 2024
L@S '24: Proceedings of the Eleventh ACM Conference on Learning @ Scale
It is our great pleasure to present the Proceedings of the Eleventh Annual ACM Conference on Learning at Scale, L@S 2024, held July 18-20, 2024 at Georgia Tech in Atlanta, Georgia, USA.
The Learning at Scale conference was created by the Association for ...
- research-articleSeptember 2024
Face Anti-Spoofing with Unknown Attacks: A Comprehensive Feature Extraction and Representation Perspective
Journal of Computer Science and Technology (JCST), Volume 39, Issue 4Pages 827–840https://doi.org/10.1007/s11390-024-4164-7AbstractFace anti-spoofing aims at detecting whether the input is a real photo of a user (living) or a fake (spoofing) image. As new types of attacks keep emerging, the detection of unknown attacks, known as Zero-Shot Face Anti-Spoofing (ZSFA), has become ...
- research-articleJune 2024
MTL-TRANSFER: Leveraging Multi-task Learning and Transferred Knowledge for Improving Fault Localization and Program Repair
ACM Transactions on Software Engineering and Methodology (TOSEM), Volume 33, Issue 6Article No.: 148, Pages 1–31https://doi.org/10.1145/3654441Fault localization (FL) and automated program repair (APR) are two main tasks of automatic software debugging. Compared with traditional methods, deep learning-based approaches have been demonstrated to achieve better performance in FL and APR tasks. ...
- surveyJune 2024
Blockchained Federated Learning for Internet of Things: A Comprehensive Survey
ACM Computing Surveys (CSUR), Volume 56, Issue 10Article No.: 258, Pages 1–37https://doi.org/10.1145/3659099The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins ...
- research-articleJune 2024
A New Neurodynamics-Based Model for Fuzzy Convex Optimization Problems With Fuzzy Coefficients and General Constraints
IEEE Transactions on Fuzzy Systems (TOFS), Volume 32, Issue 9Pages 5073–5085https://doi.org/10.1109/TFUZZ.2024.3411049Fuzzy convex optimization problems with fuzzy coefficients (FCOPFCs) arise in many applications. Although many neurodynamics-based models have been proposed for solving FCOPFCs, most of them are designed for FCOPFCs with equality or inequality constraints ...
- research-articleJune 2024
Enhancing Physical-Layer Key Generation Accuracy through Deep Learning-Based Hardware Calibration
AdaAIoTSys '24: Proceedings of the 2024 Workshop on Adaptive AIoT SystemsPages 13–18https://doi.org/10.1145/3662007.3663883This paper introduces a deep learning-based approach for calibrating hardware defects in physical-layer key generation (PKG) tasks, focusing on directional-of-arrival (DoA) based key generation in wireless communication systems. The proposed scheme ...
- research-articleAugust 2024
Improved YOLOv3 Subway Platform Pedestrian Density Algorithm
MIDA '24: Proceedings of the 2024 International Conference on Machine Intelligence and Digital ApplicationsPages 457–461https://doi.org/10.1145/3662739.3670227The rapid growth of subway passenger flow has brought enormous challenges to the operation and management of the subway system. The foundation for stable and efficient management of subway passenger flow is a deep understanding of subway passenger travel ...
- research-articleMay 2024
Stability and Safety Analysis of Connected and Automated Vehicle Platoon Considering Dynamic Communication Topology
IEEE Transactions on Intelligent Transportation Systems (ITS-TRANSACTIONS), Volume 25, Issue 10Pages 13442–13452https://doi.org/10.1109/TITS.2024.3398111Connected and automated vehicles (CAVs) can communicate with other CAVs through vehicle-to-vehicle (V2V) communication technology which can improve the stability and safety performance of the platoon. However, the V2V communication could be unreliable as ...
- research-articleMay 2024
Rankitect: Ranking Architecture Search Battling World-class Engineers at Meta Scale
- Wei Wen,
- Kuang-Hung Liu,
- Igor Fedorov,
- Xin Zhang,
- Hang Yin,
- Weiwei Chu,
- Kaveh Hassani,
- Mengying Sun,
- Jiang Liu,
- Xu Wang,
- Lin Jiang,
- Yuxin Chen,
- Buyun Zhang,
- Xi Liu,
- Dehua Cheng,
- Zhengxing Chen,
- Guang Zhao,
- Fangqiu Han,
- Jiyan Yang,
- Yuchen Hao,
- Liang Xiong,
- Wen-Yen Chen
WWW '24: Companion Proceedings of the ACM Web Conference 2024Pages 73–82https://doi.org/10.1145/3589335.3648304Neural Architecture Search (NAS) has demonstrated its efficacy in computer vision and potential for ranking systems. However, prior work focused on academic problems, which are evaluated at small scale under well-controlled fixed baselines. In industry ...
- research-articleMay 2024
When Imbalance Meets Imbalance: Structure-driven Learning for Imbalanced Graph Classification
WWW '24: Proceedings of the ACM Web Conference 2024Pages 905–913https://doi.org/10.1145/3589334.3645629Graph Neural Networks (GNNs) can learn representative graph-level features to achieve efficient graph classification. But GNNs usually assume an environment where both class and structure distribution are balanced. Although previous works have considered ...