Reviewerly/exHarmony huggingface dataset:
Adaptive differential privacy in vertical federated learning for mobility forecasting

Differential privacy is the de-facto technique for protecting the individuals in the training dataset and the learning models in deep learning. However, the technique presents two limitations when applied to vertical federated learning, where several organizations collaborate to train a common global model. First, it treats all the training dataset features similarly regardless of the organizations' heterogeneous privacy requirements. Second, most existing works distribute the privacy budget uniformly across training steps, disregarding the impact of the dynamic changes of local gradients on the model's privacy and utility balance. This paper proposes the Adaptive differential privacy for Vertical Federated Learning (AdaVFL) protocol that tackles these limitations. We estimate the organization's feature impact on the global model and design two weighting strategies that adaptively assign privacy budgets to each organization for heterogeneously protecting its features. Moreover, we carefully adjust the privacy budget to the model's convergence at each training iteration using a closed feedback loop to improve the learning model's utility. We experimentally evaluate AdaVFL on two public datasets (Bike New York and Yelp reviews) with a vertical federated learning framework for mobility forecasting in Pytorch. We show that the feature-level budget initialization improves the resiliency to a state-of-the-art feature privacy attack by up to 25%. Furthermore, the experimental evaluation demonstrates that the adaptive privacy budget increases the accuracy by up to 15% on average compared to the state-of-the-art budget allocation strategies.
Exploring progressive mental model representation of core physiology concepts in physician assistant students through word frequency and association analyses

A well-developed mental model is crucial for effectively studying physiology core concepts. However, mental models can be difficult for students to represent and for instructors to evaluate and correct. Systems modeling as a visualization cognitive tool may facilitate mental model development. On the other hand, evidence of mental model development may also be represented verbally, in writing, and therefore, be evaluated. In this study, analysis of writing prompt completions illustrated progress in physician assistant student mental model formation of physiology core concepts, such as homeostasis and cell-cell communication, over time. Two cohorts of physician assistant students were invited to voluntarily submit completions of writing prompts five times over 16 months. Sessions included submissions pre- and post-small group systems modeling participation. Word frequency and word association cluster dendrogram analyses were conducted on submissions using the tm text mining package in R to provide insight into progressive changes in core concepts of word use and associations. Students demonstrated expanded core concepts systems thinking over time. This was apparent through the increased use of systems process terms, such as homeostasis, in submissions immediately following systems modeling activities. Students also increasingly included terms and associations emphasizing cell-cell communication and systems integration. The inclusion of these concepts within student mental models was demonstrably enhanced by participation in systems modeling activities.NEW & NOTEWORTHY This study applies text mining, an artificial intelligence form of natural language processing, to evaluate a series of physiology student-written prompt completions. Text mining of student writing in physiology has not yet been reported in the literature. Through the application of this technique, longitudinal trends in student development of mental models of core concepts were identified and visualized through word frequency distributions and cluster dendrograms.
P‐2.8: Metaverse: Origin, Current Applications and Prospects for Future Development

The Metaverse is a mixed reality environment that combines virtual and real worlds, originating from the 1980s. Today, the Metaverse has become a hot topic, attracting a large influx of funding and talent. It is considered the next generation of the internet, with wide-ranging applications in areas such as gaming, social media, healthcare, education, tourism, and retail. In the future, the Metaverse is expected to become a significant economic industry, completely changing people's production and lifestyle, much like the internet did 20 years ago.
Anticipating food price crises by reservoir computing

Anticipating price crises in the market of agri-commodities is critical to guarantee both the sustainability of the food system and to ensure food security. However, this is not an easy task, since the problem implies analyzing small and very volatile time series, which are highly influenced by external factors. In this paper, we show that suitable reservoir computing algorithms can be developed that outperform traditional approaches, by reducing the Mean Absolute Error and, more importantly, increasing the Market Direction Accuracy. For this purpose, the applicability of five variants of such method to forecast this market is explored, and their performance evaluated by comparing the results with those obtained with the standard LSTM and SARIMA benchmarks. We conclude that decomposing the time series and modeling each component with a separate RC is essential to successfully anticipate price trends, and that this method works even in the complex changing temporal scenario of the Covid-19 pandemic, when part of the data were collected.
A novel adaptive approach for improvement in the estimation of hourly diffuse solar radiation: A case study of China

Liu & Jordan–type models are still widely used to estimate diffuse radiation in various regions because of their simplicity. This paper proposes an adaptive approach of combining the grouping of kt–k data points with Liu & Jordan–type models to estimate the hourly diffuse radiation more accurately. This study was conducted using hourly radiation data measured in three regions in China (Shanghai, Xi'an, and Lhasa). The main novelties reported herein are the introduction of α as a constraint indicator and the proposal of a criterion for grouping kt–k data points. Grouping models, which adopted the functional form of cubic polynomials, were established in three regions based on the grouped data. Compared with the all-data model results, the percentage of mean absolute bias error (MABE%) and relative root mean square error (RMSE%) for the grouping model decreased by approximately 4% in Shanghai and Xi'an and 6% in Lhasa. Furthermore, weather condition analysis showed that the grouping models had the best adaptability on overcast days, followed by cloudy days and sunny days. A similar analysis was performed for solar time, with the best adaptability occurring at sunrise and sunset and the worst occurring at 9 am. The results also suggest that similar working procedures could contribute to hourly diffuse radiation estimation in other fields.
Robust watermarking method for securing color medical images using Slant-SVD-QFT transforms and OTP encryption

This paper proposes a new robust watermarking method for securing color medical images, where the proposed method relies on combining Slant, Singular Value Decomposition (SVD), and quaternion Fourier-Transform (QFT). The stimulus behind this combination is to improve the invisibility and durability of the proposed method. First, the input cover image is split into four equal parts for fast computation and then encrypted using one-time padding (OTP) encryption to increase robustness. Slant transform is applied to encrypted blocks to increase image compaction. The SVD is applied to the transformed image blocks to preserve quality during the embedding process. Finally, the QFT is applied to increase visual imperceptibility. The binary watermark is first compressed using SVD, scrambled using Arnold encryption to raise security, and then embedded in the modified QFT coefficients. The extraction procedures are the inverse operations of the embedding procedures. The proposed method achieves a good tradeoff between invisibility and robustness compared to existing schemes versus many geometrical, processing, and hybrid attacks. Also, the proposed method attains high visual imperceptibility. The extracted watermark seems to be the original watermark with minimum BER and high NC values. Empirical findings indicate that the proposed method is giving better invisibility and robustness results while maintaining a high capacity compared to other existing watermarking methods.
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis

We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural volume-surface scene representation designed to have well-behaved level sets that correspond to surfaces in the scene. We then bake this representation into a high-quality triangle mesh, which we equip with a simple and fast view-dependent appearance model based on spherical Gaussians. Finally, we optimize this baked representation to best reproduce the captured viewpoints, resulting in a model that can leverage accelerated polygon rasterization pipelines for real-time view synthesis on commodity hardware. Our approach outperforms previous scene representations for real-time rendering in terms of accuracy, speed, and power consumption, and produces high quality meshes that enable applications such as appearance editing and physical simulation.
Ultra Sharp : Study of Single Image Super Resolution Using Residual Dense Network

For years, Single Image Super Resolution (SISR) has been an interesting and ill-posed problem in computer vision. The traditional super-resolution (SR) imaging approaches involve interpolation, reconstruction, and learning-based methods. Interpolation methods are fast and uncomplicated to compute, but they are not so accurate and reliable. Reconstruction-based methods are better compared with interpolation methods, but they are time-consuming and the quality degrades as the scaling increases. Even though learning-based methods like Markov random chains are far better than all the previous ones, they are unable to match the performance of deep learning models for SISR. This study examines the Residual Dense Networks architecture proposed by Yhang et al. and analyzes the importance of its components. By leveraging hierarchical features from original low-resolution (LR) images, this architecture achieves superior performance, with a network structure comprising four main blocks, including the residual dense block (RDB) as the core. Through investigations of each block and analyses using various loss metrics, the study evaluates the effectiveness of the architecture and compares it to other state-of-the-art models that differ in both architecture and components.
Sharpness-Aware Graph Collaborative Filtering

Graph Neural Networks (GNNs) have achieved impressive performance in collaborative filtering. However, recent studies show that GNNs tend to yield inferior performance when the distributions of training and test data are not aligned well. Moreover, training GNNs often requires optimizing non-convex neural networks with an abundance of local and global minima, which may differ widely in their performance at test time. Thus, it is essential to develop an optimization strategy that can choose the minima carefully, which can yield strong generalization performance on unseen data. Here we propose an effective training schema, called gSAM, under the principle that theflatter minima has a better generalization ability than thesharper ones. To achieve this goal, gSAM regularizes the flatness of the weight loss landscape by forming a bi-level optimization: the outer problem conducts the standard model training while the inner problem helps the model jump out of the sharp minima. Experimental results show the superiority of our gSAM.
Password strength verification based on machine learning algorithms and LSTM recurrent neural networks

Objectives . One of the most commonly used authentication methods in computer systems, password authentication is susceptible to various attacks including brute-force and dictionary attacks. This susceptibility requires not only the strict protection of user credentials, but also the definition of criteria for increasing a password’s strength to minimize the possibility of its exploitation by an attacker. Thus, an important task is the development of a verifier for checking passwords for strength and prohibiting the user from setting passwords that are susceptible to cracking. The use of machine learning methods to construct a verifier involves algorithms for formulating requirements for password complexity based on lists of known passwords available for each strength category. Methods . The proposed supervised machine learning algorithms comprise support vector machines, random forest, boosting, and long short-term memory (LSTM) recurrent neural network types. Embedding and term frequency–inverse document frequency (TF-IDF) methods are used for data preprocessing, while cross-validation is used for selecting hyperparameters. Results . Password strength recommendations and requirements from international and Russian standards are described. The existing methods of password strength verification in various operating systems are analyzed. The experimental results based on existing datasets comprising passwords having an associated level of strength are presented. Conclusions . A LSTM recurrent neural network is highlighted as one of the most promising areas for building a password strength verifier.
DHIQA: Quality assessment of dehazed images based on attentive multi-scale feature fusion and rank learning

Haze is a ubiquitous atmospheric phenomenon that seriously influences the visibility of images. To this end, numerous image dehazing models have been proposed to improve the visual quality of hazy images. However, the quality assessment of dehazed images has fallen behind. Traditional IQA methods for dehazed images rely on complex handcrafted features that may not accurately evaluate the quality of dehazed images. In this paper, we present a novel no-reference image quality assessment network for dehazed images (called DHIQA). Firstly, we propose a multi-scale feature fusion network that considers typical dehazed distortion-related features, such as contrast, dark channel, edge, etc. Then, inspired by the contrast sensitivity function of the Human Visual System (HVS), we propose a contrast attentive module to enhance the effectiveness of the proposed IQA network. Moreover, in addition to the absolute quality score of dehazed images, their rankings are also important for quality assessment. Thus, we propose a new ranking loss, which takes both the absolute quality score and their rankings into consideration. Extensive experimental results on six publicly available dehazed IQA databases show that the proposed model achieves superior performance compared to current state-of-the-art metrics.
YOLO-RS: A More Accurate and Faster Object Detection Method for Remote Sensing Images

In recent years, object detection based on deep learning has been widely applied and developed. When using object detection methods to process remote sensing images, the trade-off between the speed and accuracy of models is necessary, because remote sensing images pose additional difficulties such as complex backgrounds, small objects, and dense distribution to the detection task. This paper proposes YOLO-RS, an optimized object detection algorithm based on YOLOv4 to address the challenges. The Adaptively Spatial Feature Fusion (ASFF) structure is introduced after the feature enhancement network of YOLOv4. It assigns adaptive weight parameters to fuse multi-scale feature information, improving detection accuracy. Furthermore, optimizations are applied to the Spatial Pyramid Pooling (SPP) structure in YOLOv4. By incorporating residual connections and employing 1 × 1 convolutions after maximum pooling, both computation complexity and detection accuracy are improved. To enhance detection speed, Lightnet is introduced, inspired by Depthwise Separable Convolution for reducing model complexity. Additionally, the loss function in YOLOv4 is optimized by introducing the Intersection over Union loss function. This change replaces the aspect ratio loss term with the edge length loss, enhancing sensitivity to width and height, accelerating model convergence, and improving regression accuracy for detected frames. The mean Average Precision (mAP) values of the YOLO-RS model are 87.73% and 92.81% under the TGRS-HRRSD dataset and RSOD dataset, respectively, which are experimentally verified to be 2.15% and 1.66% higher compared to the original YOLOv4 algorithm. The detection speed reached 43.45 FPS and 43.68 FPS, respectively, with 5.29 Frames Per Second (FPS) and 5.30 FPS improvement.
Multi-task neural network for solving the problem of recognizing the type of QAM and PSK modulation under parametric a priori uncertainty

Objectives . Automatic modulation recognition of unknown signals is an important task for various fields oftechnology such as radio control, radio monitoring, and identification of interference and sources of radio emission. The paper aims to develop a method for recognizing the types of signal modulation under conditions of parametric a priori uncertainty, including the uncertainty of carrier frequency- and initial signal phase values. An additional task consists in estimating the offset values of the carrier frequency or signal phase at the initial stage of the recognition process. Methods . A multi-task learning with artificial neural network and the theory of cumulants of random variables are used. Results . For signals with a carrier frequency and initial phase shift, cumulant approaches for QAM-8, APSK-16, QAM-64, and PSK-8 modulations are calculated. A multi-task learning with artificial neural network using cumulant features and a data standardization algorithm is presented. The results of the experiment show that using multi-task learning with an artificial neural network provides high accuracy of recognizing QAM-8 and APSK-16, QAM-64 and PSK-8 modulations with small mismatches of the carrier frequency or initial phase. The accuracy of determining the offset values from the carrier frequency or the initial phase for QAM-8, APSK-16, QAM-64, and PSK-8 modulation is high. Conclusions . The multi-task learning with neural network using high-order signal cumulants makes it possible not only to recognize modulation types with high accuracy under conditions of a priori uncertainty of signal parameters, but also to determine the offset values of carrier frequency or initial signal phase from expected values.
SSL-GAN-RoBERTa: A robust semi-supervised model for detecting Anti-Asian COVID-19 hate speech on social media

Abstract Anti-Asian speech during the COVID-19 pandemic has been a serious problem with severe consequences. A hate speech wave swept social media platforms. The timely detection of Anti-Asian COVID-19-related hate speech is of utmost importance, not only to allow the application of preventive mechanisms but also to anticipate and possibly prevent other similar discriminatory situations. In this paper, we address the problem of detecting Anti-Asian COVID-19-related hate speech from social media data. Previous approaches that tackled this problem used a transformer-based model, BERT/RoBERTa, trained on the homologous annotated dataset and achieved good performance on this task. However, this requires extensive and annotated datasets with a strong connection to the topic. Both goals are difficult to meet without employing reliable, vast, and costly resources. In this paper, we propose a robust semi-supervised model, SSL-GAN-RoBERTa, that learns from a limited heterogeneous dataset and whose performance is further enhanced by using vast amounts of unlabeled data from another related domain. Compared with the RoBERTa baseline model, the experimental results show that the model has substantial performance gains in terms of Accuracy and Macro-F1 score in different scenarios that use data from different domains. Our proposed model achieves state-of-the-art performance results while efficiently using unlabeled data, showing promising applicability to other complex classification tasks where large amounts of labeled examples are difficult to obtain.
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks

Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classification, and node clustering. In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual information, while others are not; (2) diversity of types: Nodes and edges of multiple types form a heterogeneous network structure. As pretrained language models (PLMs) have demonstrated their effectiveness in obtaining widely generalizable text representations, a substantial amount of effort has been made to incorporate PLMs into representation learning on text-rich networks. However, few of them can jointly consider heterogeneous structure (network) information as well as rich textual semantic information of each node effectively. In this paper, we propose Heterformer, a Heterogeneous Network-Empowered Transformer that performs contextualized text encoding and heterogeneous structure encoding in a unified model. Specifically, we inject heterogeneous structure information into each Transformer layer when encoding node texts. Meanwhile, Heterformer is capable of characterizing node/edge type heterogeneity and encoding nodes with or without texts. We conduct comprehensive experiments on three tasks (i.e., link prediction, node classification, and node clustering) on three large-scale datasets from different domains, where Heterformer outperforms competitive baselines significantly and consistently. The code can be found at https://github.com/PeterGriffinJin/Heterformer.