Reviewerly/exHarmony huggingface dataset:
Forecasting solar energy production: A comparative study of machine learning algorithms

The use of solar energy has been rapidly expanding as a clean and renewable energy source, with the installation of photovoltaic panels on homes, businesses, and large-scale solar farms. The increasing demand for sustainable energy sources has pushed the growth of the solar industry, as well as advancements in technology, making solar panels more efficient and cost-effective. The implementation of solar energy not only reduces our reliance on non-renewable fossil fuels but also helps to mitigate the effects of climate change by reducing carbon emissions. This paper presents a complete and comparative study of solar energy production forecasting in Morocco using six machine learning (ML) algorithms : Support Vector Regression (SVR), Artificial Neural Network (ANN), Decision Tree (DT), Random Forest (RF), Generalized Additive Model (GAM) and Extreme Gradient Boosting (XGBOOST), based on Solar Power Plant daily data installed in Benguerir city of Morocco between January and December 2022. The models were trained, tested, and then evaluated. In order to assess the models performance four metrics were used in this study, namely root mean squared error (RMSE), mean absolute error (MAE), mean absolute scaled error (MASE)and R-squared (R2). The performance of the models reveals ANN to be the most effective predictive model for energy forecasting in similar cases with the lowest value of RMSE, MSAE and the highest value of R-squared, which are accepted as one of the most important performance criteria by the ANN model. The findings of this study not only validate the effectiveness of the ANN algorithm but also offer the appropriate parameters for achieving the best results in predicting solar energy production. By identifying the optimal configuration of the ANN algorithm, we provide valuable insights that can be directly applied in real-world applications, thereby enhancing the optimization of solar energy systems and contributing to a sustainable future, particularly the integration of these results in an edge device for the predictive maintenance of photovoltaic power plants.
Evaluating the Effectiveness of Learning Commons as Third Spaces With the 5Es Usability Model

Gen-Z students' learning styles have recently changed significantly due to inquiry-based learning and rapid information technology development. Physical libraries have to surrender bookshelves to learning commons to attract student visits. This study analyses the effectiveness of the learning commons at the Hong Kong University of Science and Technology (HKUSTLC) as a case of the library as a third space based on the third space theory of Oldenburg through using the 5Es usability model (effective, efficient, engaging, error tolerant, easy to learn) based on its publication, website information, and email inquiries with its manager. These finding indicate that HKUSTLC has been well-received, effective, efficient, and attractive. Yet, based on the limitations and shortcomings, the authors suggest solutions regarding activity delivery, space re-engineering, regular feedback collection, and environmental improvements. This study also provides insights for constructing learning commons in physical libraries as a third space for Gen Z students, which scant studies have covered, especially in Asia.
Exploring users' adoption intentions of intelligent virtual assistants in financial services: An anthropomorphic perspectives and socio-psychological perspectives

With artificial intelligence entering all spheres of technology, it has also prompted financial businesses to enter into Intelligent Financial Technologies (FinTech). Chatbots and virtual assistants all are examples of how AI is used in the financial services industry. Whereas many companies are humanizing their customer service chatbots and Intelligent Virtual Assistants by giving them names and avatars, no research has been conducted on how IVAs (Intelligent Virtual Assistants) influence customer responses in the financial service setting. The present study aims to examine the role of artificial intelligence-related factors (Perceived Anthropomorphism, Perceived Intelligence and Perceived Animacy) and Socio-Psychological factors (Technological self-efficacy and Social Presence) and how these factors affect the attitude (Hedonic & Utilitarian) towards the acceptance of Intelligent Virtual Assistants in the context of financial services. The moderating role of the need for human intelligence has also been examined between the AI factors and attitude. A total of 435 fintech users responded to an online questionnaire after interacting with a chatbot. Smart PLS SEM 4 was used to analyze the data and to test the hypothesis. The results showed that all the AI factors (Perceived Intelligence, perceived anthropomorphism, Perceived animacy) of the Intelligent Virtual Assistants (IVAs) have a significant impact on the attitude of the user except the influence of perceived anthropomorphism on the hedonic attitude which was insignificant. The results also showed that the need for human interaction positively moderates the relationships between AI constructs and attitudes. This study provides empirical insight into the opportunities, prospects and challenges pertaining to the use of AI in the area of financial services and provides practical implications in the design of IVAs in financial technologies. Additionally, the results demonstrated that Intelligent Virtual Assistants have enormous potential in the area of financial services. Finally, we look into the implications of these findings for IVA design principles and prospective research directions.
CGINet: Cross-modality grade interaction network for RGB-T crowd counting

Crowd counting is a fundamental and challenging task that requires rich information to generate a pixel-level crowd density map. Additionally, the development of thermal sensing and its applicability to computer vision has enabled the use of thermal information for crowd counting. Considering the complementary characteristics of RGB (red–green–blue) and thermal images in different feature encoding stages, we propose a cross-modality grade interaction network (CGINet) for RGB-T (RGB and thermal) crowd counting. We introduce an RGB cooperative enhancement module for thermal information to correctly extract low-level features from scenes containing objects with different scales. As RGB information is sensitive to lighting and occlusion while extracting high-level features, we propose a thermal information supplementary module to increase the RGB feature robustness. In addition, a novel multilayer decoding module fully integrates features at different levels, exploits the features of different layers, and predicts the crowd density map. Results from comprehensive experiments on the RGBT-CC benchmark demonstrate the effectiveness of the proposed CGINet for RGB-T crowd counting. In addition, CGINet achieves excellent results on the ShanghaiTechRGB dataset containing paired RGB images and depth maps. The experimental results highlight the advanced architecture and generalization ability of CGINet for multimodality crowd counting.
Towards Real-Time 4K Image Super-Resolution

Over the past few years, high-definition videos and images in 720p (HD), 1080p (FHD), and 4K (UHD) resolution have become standard. While higher resolutions offer improved visual quality for users, they pose a significant challenge for super-resolution networks to achieve real-time performance on commercial GPUs. This paper presents a comprehensive analysis of super-resolution model designs and techniques aimed at efficiently upscaling images from 720p and 1080p resolutions to 4K. We begin with a simple, effective baseline architecture and gradually modify its design by focusing on extracting important high-frequency details efficiently. This allows us to subsequently downscale the resolution of deep feature maps, reducing the overall computational footprint, while maintaining high reconstruction fidelity. We enhance our method by incorporating pixel-unshuffling, a simplified and speed-up reinterpretation of the basic block proposed by NAFNet, along with structural re-parameterization. We assess the performance of the fastest version of our method in the new NTIRE 2023 Real-Time 4K Super-Resolution challenge and demonstrate its potential in comparison with state-of-the-art efficient super-resolution models when scaled up. Our method was tested successfully on high-quality content from photography, digital art, and gaming content.
Federated Learning for the Healthcare Metaverse: Concepts, Applications, Challenges, and Future Directions

Recent technological advancements have considerably improved healthcare systems to provide various intelligent services, improving life quality. The Metaverse, often described as the next evolution of the Internet, helps the users interact with each other and the environment, thus offering a seamless connection between the virtual and physical worlds. Additionally, the Metaverse, by integrating emerging technologies, such as artificial intelligence (AI), cloud edge computing, Internet of Things (IoT), blockchain, and semantic communications, can potentially transform many vertical domains in general and the healthcare sector (healthcare Metaverse) in particular. The healthcare Metaverse holds huge potential to revolutionize the development of intelligent healthcare systems, thus presenting new opportunities for significant advancements in healthcare delivery, personalized healthcare experiences, medical education, collaborative research, and so on. However, various challenges are associated with the realization of the healthcare Metaverse, such as privacy, interoperability, data management, and security. Federated learning (FL), a new branch of AI, opens up enormous opportunities to deal with the aforementioned challenges in the healthcare Metaverse by exploiting the data and computing resources available at the distributed devices. This motivated us to present a survey on adopting FL for the healthcare Metaverse. Initially, we present the preliminaries of IoT-based healthcare systems, FL in conventional healthcare, and the healthcare Metaverse. Furthermore, the benefits of the FL in the healthcare Metaverse are discussed. Subsequently, we discuss the several applications of FL-enabled healthcare Metaverse, including medical diagnosis, patient monitoring, medical education, infectious disease, and drug discovery. Finally, we highlight the significant challenges and potential solutions toward realizing FL in the healthcare Metaverse.
Phase-Shifting Coder: Predicting Accurate Orientation in Oriented Object Detection

With the vigorous development of computer vision, oriented object detection has gradually been featured. In this paper, a novel differentiable angle coder named phase-shifting coder (PSC) is proposed to accurately predict the orientation of objects, along with a dual-frequency version (PSCD). By mapping the rotational periodicity of different cycles into the phase of different frequencies, we provide a unified framework for various periodic fuzzy problems caused by rotational symmetry in oriented object detection. Upon such a framework, common problems in oriented object detection such as boundary discontinuity and square-like problems are elegantly solved in a unified form. Visual analysis and experiments on three datasets prove the effectiveness and the potentiality of our approach. When facing scenarios requiring high-quality bounding boxes, the proposed methods are expected to give a competitive performance. The codes are publicly available at https://github.com/open-mmlab/mmrotate.
Generalized Relation Modeling for Transformer Tracking

Compared with previous two-stream trackers, the recent one-stream tracking pipeline, which allows earlier interaction between the template and search region, has achieved a remarkable performance gain. However, existing one-stream trackers always let the template interact with all parts inside the search region throughout all the encoder layers. This could potentially lead to target-background confusion when the extracted feature representations are not sufficiently discriminative. To alleviate this issue, we propose a generalized relation modeling method based on adaptive token division. The proposed method is a generalized formulation of attention-based relation modeling for Transformer tracking, which inherits the merits of both previous two-stream and one-stream pipelines whilst enabling more flexible relation modeling by selecting appropriate search tokens to interact with template tokens. An attention masking strategy and the Gumbel-Softmax technique are introduced to facilitate the parallel computation and end-to-end learning of the token division module. Extensive experiments show that our method is superior to the two-stream and one-stream pipelines and achieves state-of-the-art performance on six challenging benchmarks with a real-time running speed. Code and models are publicly available at https://github.com/Little-Podi/GRM.
DropKey for Vision Transformer

In this paper, we focus on analyzing and improving the dropout technique for self-attention layers of Vision Transformer, which is important while surprisingly ignored by prior works. In particular, we conduct researches on three core questions: First, what to drop in self-attention layers? Different from dropping attention weights in literature, we propose to move dropout operations forward ahead of attention matrix calculation and set the Key as the dropout unit, yielding a novel dropout-before-softmax scheme. We theoretically verify that this scheme helps keep both regularization and probability features of attention weights, alleviating the overfittings problem to specific patterns and enhancing the model to globally capture vital information; Second, how to schedule the drop ratio in consecutive layers? In contrast to exploit a constant drop ratio for all layers, we present a new decreasing schedule that gradually decreases the drop ratio along the stack of self-attention layers. We experimentally validate the proposed schedule can avoid overfittings in low-level features and missing in high-level semantics, thus improving the robustness and stableness of model training; Third, whether need to perform structured dropout operation as CNN? We attempt patch-based block-version of dropout operation and find that this useful trick for CNN is not essential for ViT. Given exploration on the above three questions, we present the novel Drop-Key method that regards Key as the drop unit and exploits decreasing schedule for drop ratio, improving ViTs in a general way. Comprehensive experiments demonstrate the effectiveness of DropKey for various ViT architectures, e.g. T2T, VOLO, CeiT and DeiT, as well as for various vision tasks, e.g., image classification, object detection, human-object interaction detection and human body shape recovery.
VoxFormer: Sparse Voxel Transformer for Camera-Based 3D Semantic Scene Completion

Humans can easily imagine the complete 3D geometry of occluded objects and scenes. This appealing ability is vital for recognition and understanding. To enable such capability in AI systems, we propose VoxFormer, a Transformer-based semantic scene completion framework that can output complete 3D volumetric semantics from only 2D images. Our framework adopts a two-stage design where we start from a sparse set of visible and occupied voxel queries from depth estimation, followed by a densification stage that generates dense 3D voxels from the sparse ones. A key idea of this design is that the visual features on 2D images correspond only to the visible scene structures rather than the occluded or empty spaces. Therefore, starting with the fea-turization and prediction of the visible structures is more reliable. Once we obtain the set of sparse queries, we apply a masked autoencoder design to propagate the information to all the voxels by self-attention. Experiments on SemanticKITTI show that VoxFormer outperforms the state of the art with a relative improvement of 20.0% in geometry and 18.1% in semantics and reduces GPU memory during training to less than 16GB. Our code is available on https://github.com/NV1abs/VoxFormer.
Autoregressive Visual Tracking

We present ARTrack, an autoregressive framework for visual object tracking. ARTrack tackles tracking as a coordinate sequence interpretation task that estimates object trajectories progressively, where the current estimate is induced by previous states and in turn affects subsequences. This time-autoregressive approach models the sequential evolution of trajectories to keep tracing the object across frames, making it superior to existing template matching based trackers that only consider the per-frame localization accuracy. ARTrack is simple and direct, eliminating customized localization heads and post-processings. Despite its simplicity, ARTrack achieves state-of-the-art performance on prevailing benchmark datasets. Source code is available at https://github.com/MIV-XJTU/ARTrack.
Visual Language Pretrained Multiple Instance Zero-Shot Transfer for Histopathology Images

Contrastive visual language pretraining has emerged as a powerful method for either training new language-aware image encoders or augmenting existing pretrained models with zero-shot visual recognition capabilities. However, existing works typically train on large datasets of imagetext pairs and have been designed to perform downstream tasks involving only small to medium sized-images, neither of which are applicable to the emerging field of computational pathology where there are limited publicly available paired image-text datasets and each image can span up to 100,000 × 100,000 pixels. In this paper we present MI-Zero, a simple and intuitive framework for unleashing the zero-shot transfer capabilities of contrastively aligned image and text models on gigapixel histopathology whole slide images, enabling multiple downstream diagnostic tasks to be carried out by pretrained encoders without requiring any additional labels. MI-Zero reformulates zero-shot transfer under the framework of multiple instance learning to overcome the computational challenge of inference on extremely large images. We used over 550k pathology reports and other available in-domain text corpora to pretrain our text encoder. By effectively leveraging strong pretrained encoders, our best model pretrained on over 33k histopathology image-caption pairs achieves an average median zero-shot accuracy of 70.2% across three different real-world cancer subtyping tasks. Our code is available at: https://github.com/mahmoodlab/MI-Zero.
On Distillation of Guided Diffusion Models

Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALL.E 2, Stable Diffusion and Imagen. However, a downside of classifier-free guided diffusion models is that they are computationally expensive at inference time since they require evaluating two diffusion models, a class-conditional model and an unconditional model, tens to hundreds of times. To deal with this limitation, we propose an approach to distilling classifier-free guided diffusion models into models that are fast to sample from: Given a pre-trained classifier-free guided model, we first learn a single model to match the output of the combined conditional and unconditional models, and then we progressively distill that model to a diffusion model that requires much fewer sampling steps. For standard diffusion models trained on the pixel-space, our approach is able to generate images visually comparable to that of the original model using as few as 4 sampling steps on ImageNet <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$64\times 64$</tex> and CIFAR-10, achieving FID/IS scores comparable to that of the original model while being up to 256 times faster to sample from. For diffusion models trained on the latent-space (e.g., Stable Diffusion), our approach is able to generate high-fidelity images using as few as 1 to 4 denoising steps, accelerating inference by at least 10-fold compared to existing methods on ImageNet <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$256\times 256$</tex> and LAION datasets. We further demonstrate the effectiveness of our approach on text-guided image editing and inpainting, where our distilled model is able to generate high-quality results using as few as 2–4 denoising steps.
Dynamic Coarse-to-Fine Learning for Oriented Tiny Object Detection

Detecting arbitrarily oriented tiny objects poses intense challenges to existing detectors, especially for label assignment. Despite the exploration of adaptive label assignment in recent oriented object detectors, the extreme geometry shape and limited feature of oriented tiny objects still induce severe mismatch and imbalance issues. Specifically, the position prior, positive sample feature, and instance are mismatched, and the learning of extreme-shaped objects is biased and unbalanced due to little proper feature supervision. To tackle these issues, we propose a dynamic prior along with the coarse-to-fine assigner, dubbed DCFL. For one thing, we model the prior, label assignment, and object representation all in a dynamic manner to alleviate the mismatch issue. For another, we leverage the coarse prior matching and finer posterior constraint to dynamically assign labels, providing appropriate and relatively balanced supervision for diverse instances. Extensive experiments on six datasets show substantial improvements to the baseline. Notably, we obtain the state-of-the-art performance for one-stage detectors on the DOTA-v1.5, DOTA-v2.0, and DIOR-R datasets under single-scale training and testing. Codes are available at https://github.com/Chasel-Tsui/mmrotate-dcfl.
Twin Contrastive Learning with Noisy Labels

Learning from noisy data is a challenging task that sig-nificantly degenerates the model performance. In this paper, we present TCL, a novel twin contrastive learning model to learn robust representations and handle noisy labels for classification. Specifically, we construct a Gaussian mixture model (GMM) over the representations by injecting the supervised model predictions into GMM to link label- free latent variables in GMM with label-noisy annotations. Then, TCL detects the examples with wrong labels as the out- of-distribution examples by another two-component GMM, taking into account the data distribution. We further propose a cross-supervision with an entropy regularization loss that bootstraps the true targets from model predictions to handle the noisy labels. As a result, TCL can learn discriminative representations aligned with estimated labels through mixup and contrastive learning. Extensive experimental results on several standard benchmarks and real-world datasets demonstrate the superior performance of TCL. In particular, TCL achieves 7.5% improvements on CIFAR-10 with 90% noisy label-an extremely noisy scenario. The source code is available at https://github.com/Hzzone/TCL.