Reviewerly/exHarmony huggingface dataset:
TE-SAGAN: An Improved Generative Adversarial Network for Remote Sensing Super-Resolution Images

Resolution is a comprehensive reflection and evaluation index for the visual quality of remote sensing images. Super-resolution processing has been widely applied for extracting information from remote sensing images. Recently, deep learning methods have found increasing application in the super-resolution processing of remote sensing images. However, issues such as blurry object edges and existing artifacts persist. To overcome these issues, this study proposes an improved generative adversarial network with self-attention and texture enhancement (TE-SAGAN) for remote sensing super-resolution images. We first designed an improved generator based on the residual dense block with a self-attention mechanism and weight normalization. The generator gains the feature extraction capability and enhances the training model stability to improve edge contour and texture. Subsequently, a joint loss, which is a combination of L1-norm, perceptual, and texture losses, is designed to optimize the training process and remove artifacts. The L1-norm loss is designed to ensure the consistency of low-frequency pixels; perceptual loss is used to entrench medium- and high-frequency details; and texture loss provides the local features for the super-resolution process. The results of experiments using a publicly available dataset (UC Merced Land Use dataset) and our dataset show that the proposed TE-SAGAN yields clear edges and textures in the super-resolution reconstruction of remote sensing images.
Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.
A lightweight YOLOv3 algorithm used for safety helmet detection

Abstract YOLOv3 is a popular and effective object detection algorithm. However, YOLOv3 has a complex network, and floating point operations (FLOPs) and parameter sizes are large. Based on this, the paper designs a new YOLOv3 network and proposes a lightweight object detection algorithm. First, two excellent networks, the Cross Stage Partial Network (CSPNet) and GhostNet, are integrated to design a more efficient residual network, CSP-Ghost-Resnet. Second, combining CSPNet and Darknet53, this paper designs a new backbone network, the ML-Darknet, to realize the gradient diversion of the backbone network. Finally, we design a lightweight multiscale feature extraction network, the PAN-CSP-Network. The newly designed network is named mini and lightweight YOLOv3 (ML-YOLOv3). Based on the helmet dataset, the FLPSs and parameter sizes of ML-YOLOv3 are only 29.7% and 29.4% of those of YOLOv3. Compared with YOLO5, ML-YOLOv3 also exhibits obvious advantages in calculation cost and detection effect.
Constrained and virtually traveling? Exploring the effect of travel constraints on intention to use virtual reality in tourism

The COVID-19 pandemic gravely affected the travel industry, with many changes in terms of customers’ attitudes and behavior. Since people had limited ability to visit far-away destinations, this paper studies the effect of travel constraints on behavioral intention to use virtual reality (VR) in tourism. The data revealed the relationship between structural and intrapersonal travel constraints and intention to use VR in tourism is mediated by the perceived ease of use and usefulness of VR. This finding implies that, in the case of a constrained environment such as a sanitary crisis, VR becomes one viable solution to deal with the situation only when coupled with good perceived usefulness and ease of use. The interpersonal travel constraints do not affect the same intention. This result might lead to a new way to increase VR adoption to explore a destination in the virtual world, namely by sharing the experience with family and friends. These findings show that any restricted environment may benefit from the use of virtual reality to compensate for the inability to travel.
IDE-3D

Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution, or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initializes the latent codes from the semantic and texture encoder, and further optimizes them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and produces high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness and efficiency.
Masked Generative Distillation

Knowledge distillation has been applied to various tasks successfully. The current distillation algorithm usually improves students’ performance by imitating the output of the teacher. This paper shows that teachers can also improve students’ representation power by guiding students’ feature recovery. From this point of view, we propose Masked Generative Distillation (MGD), which is simple: we mask random pixels of the student’s feature and force it to generate the teacher’s full feature through a simple block. MGD is a truly general feature-based distillation method, which can be utilized on various tasks, including image classification, object detection, semantic segmentation and instance segmentation. We experiment on different models with extensive datasets and the results show that all the students achieve excellent improvements. Notably, we boost ResNet-18 from 69.90% to 71.69% ImageNet top-1 accuracy, RetinaNet with ResNet-50 backbone from 37.4 to 41.0 Boundingbox mAP, SOLO based on ResNet-50 from 33.1 to 36.2 Mask mAP and DeepLabV3 based on ResNet-18 from 73.20 to 76.02 mIoU. Our codes are available at https://github.com/yzd-v/MGD .
Asymmetric nexus between Bitcoin, gold resources and stock market returns: Novel findings from quantile estimates

In the context of the debate on Bitcoin, gold resources, and stock markets, the current study examines the nexus between the gold market, Bitcoin, and stock market returns. We explore the effects of gold resources, the stock market, and Bitcoin within the Chinese context using the data in Chinese currency and under the lens of the Chinese economic structure. We collected daily data from 3 November 2014 to 31 October 2019 on all our key variables of interest. The study employs the quantile-on-quantile and quantile regression methods for comprehensive empirical analysis. Key findings suggest significant effects of Chinese stock market returns on Bitcoin and gold price effects on Bitcoin exist. In addition, gold prices negatively affect stock market returns and vice versa. The study offers novel implications regarding future cryptocurrency policies, stock markets, and resources in a Chinese context.
A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities

Due to limited resources and environmental pollution, monitoring the geological environment has become essential for many countries’ sustainable development. As various high-resolution remote-sensing (RS) imaging platforms are continuously available, the remote sensing of the geological environment (GERS) provides a fine-grain, all-weather, and low-cost method for identifying geological elements. Mainstream machine learning (ML) and deep learning (DL) methods can extract high-level high-dimensional semantic information and thus supply an efficient tool for high-precision classification and recognition in many fields. Therefore, the integration of advanced methods and multi-source RS images for GERS interpretation has achieved remarkable breakthroughs during the past decades. However, to the best of our knowledge, a systematic survey of the advances of GERS interpretation regarding ML and DL methods is still lacking. Through the collection of extensive published research in this area, this survey outlines and analyzes the challenges, progress, and promising directions of GERS interpretation. Specifically, the main challenges and difficulties in identifying GERS elements are first summarized in four aspects: sufficient element characteristics and variations, complex context disturbance, RS image quality and types, and other limitations in GERS interpretation. Second, we systematically introduce various RS imaging platforms and advanced ML and DL methods for GERS interpretation. Third, the research status and trends of several GERS applications, including their use for lithology, soil, water, rock glacier, and geological disaster, are ultimately collected and compared. Finally, potential opportunities for future research are discussed. After the systematic and comprehensive review, the conclusive findings suggest that longtime large-scale GERS interpretation and corresponding change pattern analysis will be a significant future direction to meet the needs of environment improvement and sustainable development. To complete the above goals, a fusion of satellite, airplane, environmental monitoring, geological survey, and other types of data will provide enough discriminative information, and expert knowledge, GIS, and high-performance computing techniques will be helpful to improve the efficiency and generalizability of ML and DL methods for processing the multi-platform RS data.
TrOCR: Transformer-Based Optical Character Recognition with Pre-trained Models

Text recognition is a long-standing research problem for document digitalization. Existing approaches are usually built based on CNN for image understanding and RNN for char-level text generation. In addition, another language model is usually needed to improve the overall accuracy as a post-processing step. In this paper, we propose an end-to-end text recognition approach with pre-trained image Transformer and text Transformer models, namely TrOCR, which leverages the Transformer architecture for both image understanding and wordpiece-level text generation. The TrOCR model is simple but effective, and can be pre-trained with large-scale synthetic data and fine-tuned with human-labeled datasets. Experiments show that the TrOCR model outperforms the current state-of-the-art models on the printed, handwritten and scene text recognition tasks. The TrOCR models and code are publicly available at https://aka.ms/trocr.
A Note on the Communication Complexity of Multiparty Computation in the Correlated Randomness Model

Secure multiparty computation ( $$\mathsf {MPC}$$ ) addresses the challenge of evaluating functions on secret inputs without compromising their privacy. A central question in multiparty computation is to understand the amount of communication needed to securely evaluate a circuit of size s. In this work, we revisit this fundamental question in the setting of information-theoretically secure $$\mathsf {MPC}$$ in the correlated randomness model, where a trusted dealer distributes correlated random coins, independent of the inputs, to all parties before the start of the protocol. This setting is of strong theoretical interest, and has led to the most practically efficient $$\mathsf {MPC}$$ protocols known to date. While it is known that protocols with optimal communication (proportional to input plus output size) can be obtained from the LWE assumption, and that protocols with sublinear communication o(s) can be obtained from the DDH assumption, the question of constructing protocols with o(s) communication remains wide open for the important case of information-theoretic $$\mathsf {MPC}$$ in the correlated randomness model; all known protocols in this model require O(s) communication in the online phase. In this work, we exhibit the first generic multiparty computation protocol in the correlated randomness model with communication sublinear in the circuit size, for a large class of circuits. More precisely, we show the following: any size-s layered circuit (whose nodes can be partitioned into layers so that any edge connects adjacent layers) can be evaluated with $$O(s/\log \log s)$$ communication. Our results holds for both boolean and arithmetic circuits, in the honest-but-curious setting, and do not assume honest majority. For boolean circuits, we extend our results to handle malicious corruption.
ICO Success Drivers: <i>A Textual and Statistical Analysis</i>

Recently, tech-savvy start-ups and SMEs have begun to finance their business by raising funds through the issuance and sale of “branded” newly minted digital currencies. Since this alternative capital market is largely unregulated, investors need to separate the drivers of business success from those that indicate failure, or worse, fraudulent money-raising. Through standard logistic regression and extreme value logistic regression, the authors are able to shed light on the riskiest business projects. In particular, they find that the existence of a white paper, the number of advisors, the number of people on the team, and the presence of a Telegram chat are significant factors useful for discerning a successful capital raising from an unsuccessful one. Conversely, the non-existence of a company website and an inactive Twitter account are indicators of a possible scam. <b>TOPICS:</b>Statistical methods, currency
Learning Scale-Adaptive Tight Correlation Filter for Object Tracking

In this paper, we propose a novel tracking method by formulating tracking as a correlation filtering as well as a ridge regression problem. First, we develop a tight correlation filter-based tracking framework from the signal detection perspective. In this formulation, the correlation filter is set as the same size as the target, which can make full use of the relations of the adjacent image patches and effectively exclude the influence of the background. Specifically, we point out that the novel correlation filter model can be regarded as the ridge regression model which takes into account the different importance of the samples and has the consistent objective with tracking. Second, we focus on the scale variation problem in tracking. By making use of the spatial structure of the correlation filter, the multiscale filter banks can be generated via interpolation to handle the scale estimation problem easily. Third, we present a novel distance importance-based confidence calculation model to determine the final tracking result, which not only makes use of the fine discriminability of the correlation filter but also takes the distance importance of the candidate samples into account to alleviate the impact of similar distractors. Experimental results demonstrate that our method is superior to several state-of-the-art trackers and many other correlation filter-based methods in the benchmark datasets.
Process alarm prediction using deep learning and word embedding methods

Industrial alarm systems play an essential role for the safe management of process operations. With the increase in automation and instrumentation of modern process plants, the number of alarms that the operators manage has also increased significantly. The operators are expected to make critical decisions in the presence of flooding alarms, poorly configured and maintained alarms and many nuisance alarms. In this environment, if the incoming alarms can be correctly predicted before they actually occur, the operators may have a chance to address and possibly avoid abnormal behaviors by taking corrective actions in time. Inspired by the application of deep learning in natural language processing, this paper presents an alarm prediction method based on word embedding and recurrent neural networks to predict the next alarm in a process setting. This represents both a novel approach to alarm management as well as a novel application of natural language processing and deep learning techniques to this problem. The proposed method is applied to an actual case study to demonstrate its performance.
Hiding data in images using steganography techniques with compression algorithms

Steganography is the science and art of secret communication between two sides that attempt to hide the content of the message. It is the science of embedding information into the cover image without causing a loss in the cover image after embedding.Steganography is the art and technology of writing hidden messages in such a manner that no person, apart from the sender and supposed recipient, suspects the lifestyles of the message. It is gaining huge attention these days as it does now not attract attention to its information's existence. In this paper, a comparison of two different techniques is given. The first technique used Least Significant Bit (LSB) with no encryption and no compression. In the second technique, the secret message is encrypted first then LSB technique is applied. Moreover, Discrete Cosine Transform (DCT) is used to transform the image into the frequency domain. The LSB algorithm is implemented in spatial domain in which the payload bits are inserted into the least significant bits of cover image to develop the stego-image while DCT algorithm is implemented in frequency domain in which the stego-image is transformed from spatial domain to the frequency domain and the payload bits are inserted into the frequency components of the cover image.The performance of these two techniques is evaluated on the basis of the parameters MSE and PSNR.
Stochastic Variational Inference for Bayesian Phylogenetics: A Case of CAT Model

Abstract The pattern of molecular evolution varies among gene sites and genes in a genome. By taking into account the complex heterogeneity of evolutionary processes among sites in a genome, Bayesian infinite mixture models of genomic evolution enable robust phylogenetic inference. With large modern data sets, however, the computational burden of Markov chain Monte Carlo sampling techniques becomes prohibitive. Here, we have developed a variational Bayesian procedure to speed up the widely used PhyloBayes MPI program, which deals with the heterogeneity of amino acid profiles. Rather than sampling from the posterior distribution, the procedure approximates the (unknown) posterior distribution using a manageable distribution called the variational distribution. The parameters in the variational distribution are estimated by minimizing Kullback–Leibler divergence. To examine performance, we analyzed three empirical data sets consisting of mitochondrial, plastid-encoded, and nuclear proteins. Our variational method accurately approximated the Bayesian inference of phylogenetic tree, mixture proportions, and the amino acid propensity of each component of the mixture while using orders of magnitude less computational time.
Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness

In the optimization design process, particle swarm optimization (PSO) is limited by its slow convergence, low precision, and tendency to easily fall into the local extremum. These limitations make degradation inevitable in the evolution process and cause failure of finding the global optimum results. In this paper, based on chaos idea, the PSO algorithm is improved by adaptively adjusting parameters <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn fontstyle="italic">2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math>. The improved PSO is verified by four standard mathematical test functions. The results prove that the improved algorithm exhibits excellent convergence speed, global search ability, and stability in the optimization process, which jumps out of the local optimum and achieves global optimality due to the randomness, regularity, and ergodicity of chaotic thought. At last, the improved PSO algorithm is applied to vehicle crash research and is used to carry out the multiobjective optimization based on an approximate model. Compared with the results before the improvement, the improved PSO algorithm is remarkable in the collision index, which includes vehicle acceleration, critical position intrusion, and vehicle mass. In summary, the improved PSO algorithm has excellent optimization effects on vehicle collision.
A Quantum Particle Swarm Optimization Algorithm with Teamwork Evolutionary Strategy

The quantum particle swarm optimization algorithm is a global convergence guarantee algorithm. Its searching performance is better than the original particle swarm optimization algorithm (PSO), but the control parameters are less and easy to fall into local optimum. The paper proposed teamwork evolutionary strategy for balance global search and local search. This algorithm is based on a novel learning strategy consisting of cross-sequential quadratic programming and Gaussian chaotic mutation operators. The former performs the local search on the sample and the interlaced operation on the parent individual while the descendants of the latter generated by Gaussian chaotic mutation may produce new regions in the search space. Experiments performed on multimodal test and composite functions with or without coordinate rotation demonstrated that the population information could be utilized by the TEQPSO algorithm more effectively compared with the eight QSOs and PSOs variants. This improves the algorithm performance, significantly.