Solar 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 ...

• 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.

3D 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 ...

• 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.
• ...

Knowledge 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 ...

The 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 ...

Traffic 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 ...

In 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 ...

Sentence 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 ...

• 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 ...

We 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 ...

Learning 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 ...

Evaluating 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' ...

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 ...

Face 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 ...

Fault 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. ...

The 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 ...

Fuzzy 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 ...

This 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 ...

The 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 ...

Connected 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 ...

Neural 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 ...

Graph 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 ...