Reviewerly/exHarmony huggingface dataset:
Overview Of Measurement And Evaluation Methods Of Wind Farm And System Inertia Under Large-scale Wind Power Grid Connection

Abstract With the large-scale development of new energy, large-scale wind power is incorporated into the power grid, the inertia level of the power system continues to decrease, the system regulation ability continues to weaken, and the system stability is seriously challenged. The evaluation of inertia has become a hot research issue for scholars. Firstly, this paper introduces the basic concept and components of power system inertia. Then, the different methods of inertia measurement and evaluation of wind farm and power system are reviewed. Finally, the future research directions in the field of power system inertia evaluation are summarized.
Trading-off Information Modalities in Zero-shot Classification

Zero-shot classification is the task of learning predictors for classes not seen during training. A practical way to deal with the lack of annotations for the target categories is to encode not only the inputs (images) but also the outputs (object classes) into a suitable representation space. We can use these representations to measure the degree at which images and categories agree by fitting a compatibility measure using the information available during training. One way to define such a measure is by a two step process in which we first project the elements of either space (visual or semantic) onto the other and then compute a similarity score in the target space. Although projections onto the visual space has shown better general performance, little attention has been paid to the degree at which the visual and semantic information contribute to the final predictions. In this paper, we build on this observation and propose two different formulations that allow us to explicitly trade-off the relative importance of the visual and semantic spaces for classification in a zero-shot setting. Our formulations are based on redefinition of the similarity scoring and loss function used to learn the projections. Experiments on six different datasets show that our approach lead to improve performance compared to similar methods. Moreover, combined with synthetic features, our approach competes favorably with the state of the art on both the standard and generalized settings.
Blockchain and Supply Chain Management

Supply chain is one of critical components of vaccination drive. A robust and efficient supply chain of vaccines would help increase the speed and efficiency of vaccination, therefore reducing vaccine wastage. This system uses centralised algorithms. They are prone to single point of failure in terms of transparency, trackability and traceability, immutability, audit, and trust. These issues stymie and slow the distribution of COVID-19 vaccinations, and they make it impossible to provide a safe, secure, transparent, and reliable distribution and delivery process of COVID-19 vaccines. The authors propose a blockchain-based approach to manage data linked to COVID-19 vaccines. To automate vaccination tracing, a smart contract for vaccine distribution is being created. The authors discuss and implement the proposed solution, as well as their implementation testing and validation; they evaluate the proposed solution by performing cost and security analyses and comparing them to existing solutions, and they evaluate the proposed solution by performing cost and security analyses and comparing them to existing solutions.
Leveraging Test-Time Consensus Prediction for Robustness against Unseen Noise

We propose a method to improve DNN robustness against unseen noisy corruptions, such as Gaussian noise, Shot Noise, Impulse Noise, Speckle noise with different levels of severity by leveraging ensemble technique through a consensus based prediction method using self-supervised learning at inference time. We also propose to enhance the model training by considering other aspects of the issue i.e. noise in data and better representation learning which shows even better generalization performance with the consensus based prediction strategy. We report results of each noisy corruption on the standard CIFAR10-C and ImageNet-C benchmark which shows significant boost in performance over previous methods. We also introduce results for MNIST-C and TinyImagenet-C to show usefulness of our method across datasets of different complexities to provide robustness against unseen noise. We show results with different architectures to validate our method against other baseline methods, and also conduct experiments to show the usefulness of each part of our method.
GAGE: Geometry Preserving Attributed Graph Embeddings

Node embedding is the task of extracting concise and informative representations of certain entities that are connected in a network. Various real-world networks include information about both node connectivity and certain node attributes, in the form of features or time-series data. Modern representation learning techniques employ both the connectivity and attribute information of the nodes to produce embeddings in an unsupervised manner. In this context, deriving embeddings that preserve the geometry of the network and the attribute vectors would be highly desirable, as they would reflect both the topological neighborhood structure and proximity in feature space. While this is fairly straightforward to maintain when only observing the connectivity or attribute information of the network, preserving the geometry of both types of information is challenging. A novel tensor factorization approach for node embedding in attributed networks is proposed in this paper, that preserves the distances of both the connections and the attributes. Furthermore, an effective and lightweight algorithm is developed to tackle the learning task and judicious experiments with multiple state-of-the-art baselines suggest that the proposed algorithm offers significant performance improvements in downstream tasks.
Novel-View Synthesis of Human Tourist Photos

We present a novel framework for performing novel-view synthesis on human tourist photos. Given a tourist photo from a known scene, we reconstruct the photo in 3D space through modeling the human and the background independently. We generate a deep buffer from a novel viewpoint of the reconstruction and utilize a deep network to translate the buffer into a photo-realistic rendering of the novel view. We additionally present a method to relight the renderings, allowing for relighting of both human and background to match either the provided input image or any other. The key contributions of our paper are: 1) a framework for performing novel view synthesis on human tourist photos, 2) an appearance transfer method for relighting of humans to match synthesized backgrounds, and 3) a method for estimating lighting properties from a single human photo. We demonstrate the proposed framework on photos from two different scenes of various tourists.
Multi-hop Temporal Knowledge Graph Reasoning over Few-Shot Relations with Novel Method

Multi-hop knowledge graph reasoning is a method to predict the target entity via reasoning paths. This method can not only get the effective target entity, but also obtain the interpretable reasoning path. Most of the previous reasoning methods require that every relation should be supported by enough triples, without considering the triples of few-shot relations, and ignored the gain from temporal information in the reasoning process. Based on the above analysis, we propose a model (FS-Path) which is suitable for temporal knowledge graph reasoning over few-shot relations. This model extends the triple representation for knowledge graph to the 4-tuple representation with temporal information, and it can increase the accuracy of reasoning path by the temporal information. In addition, the FS-Path utilizes the meta-learning to study meta-parameters from high-frequency relations, and then uses the meta-parameters to adapt to the tasks of few-shot relations to improve the generalization ability of the model over few-shot relations. Meanwhile, we construct a new reward function which use the LSTM to capture observation results from environment, and then output the relation vector and temporal vector to environment. Experiments show that the model we proposed can actualize the multi-hop reasoning in temporal knowledge graph, and has outstanding effect over few-shot relations.
Comparing Automated Reuse of Scripted Tests and Model-Based Tests for Configurable Software

Highly configurable software gives developers more flexibility to meet different customer requirements and enables users to better tailor software to their needs. However, variability causes higher complexity in software and complicates many development processes, such as testing. One major challenge for testing of configurable software is adjusting tests to fit different configurations, which often has to be done manually. In our previous work, we evaluated the use of an automated reuse technique to support the reuse of existing tests for new configurations. Research on automated reuse of model variants and on applying model-based testing to configurable software encouraged us to also evaluate the automated reuse of model-based test variants. The goal is to investigate differences in applying automated reuse to the different testing paradigms. Our evaluation provides evidence for the usefulness of automated reuse for both testing paradigms. Nonetheless we found some differences in the robustness of tests to small inaccuracies of the reuse approach.
Computer technology applications to calculate the insolation of photoelectric power plant

Abstract The article considers the method of calculating solar insolation, the algorithm and the program operation for calculating insolation, the graphs are obtained showing the change in solar radiation due to the influence of weather factors and the location of solar-heat collectors installation. The results of the study of the regionally adapted photoelectric power plant using the developed computer model in the Simulink program are presented. The developed model allows to investigate the influence of the main input parameters, such as: latitude and longitude of the power plant installation, difference between the local official time and Greenwich Mean Time, inclination angle of the solar panel, the simulated day, atmospheric transparency factor, albedo and azimuth angle on the insolation intensity of the photoelectric power plant. The obtained dependences can be taken into account in the optimization of power supply systems that use solar energy conversion.
Development of High Performance Hardware by High-Level Synthesis of Median-Based Dynamic Background Subtraction Method with Multiple Line Buffers

Hardware processing is suitable for embedded image processing systems because of its lower power consumption and higher performance compared to software processing. To facilitate development, a tool called high-level synthesis, which automatically converts high-level languages into hardware description languages, is used. However, high-level synthesis of pure software does not necessarily generate efficient hardware. In this study, we attempted to generate high-performance image processing hardware using a median-based dynamic background subtraction method. As a result, we found that high-performance hardware can be generated when multiple line buffers are introduced. Compared to the non-introduced one, the performance was improved by about 13 times.
Temporally Consistent Relighting for Portrait Videos

Ensuring ideal lighting when recording videos of people can be a daunting task requiring a controlled environment and expensive equipment. Methods were recently proposed to perform portrait relighting for still images, enabling after-the-fact lighting enhancement. However, naively applying these methods on each frame independently yields videos plagued with flickering artifacts. In this work, we propose the first method to perform temporally consistent video portrait relighting. To achieve this, our method optimizes end-to-end both desired lighting and temporal consistency jointly. We do not require ground truth lighting annotations during training, allowing us to take advantage of the large corpus of portrait videos already available on the internet. We demonstrate that our method outperforms previous work in balancing accurate relighting and temporal consistency on a number of real-world portrait videos.
Detection and nudge-intervention on sensitive information in social networks

Abstract Detecting sensitive information considering privacy is a relevant issue on Online Social Networks (OSNs). It is often difficult for users to manage the privacy associated with their posts on social networks taking into account all the possible consequences. The aim of this work is to provide information about the sensitivity of the content of a publication when a user is going to share it in OSN. For this purpose, we developed a privacy-assistant agent that detects sensitive information. Based on this information, the agent provides a message through a nudge mechanism warning about the possible risks of sharing the message. To avoid being annoying, the agent also considers the user’s previous behaviour (e.g. if he previously ignored certain nudges) and adapts the messages it sends to give more relevance to those categories that are more important to the user from the point of view of the privacy risk. This agent was integrated into the social network Pesedia. We analysed the performance of different models to detect a set of sensitive categories (i.e. location, medical, drug/alcohol, emotion, personal attacks, stereotyping, family and association details, personal details and personally identifiable information) in a dataset of tweets in Spanish. The model that obtained the best results (i.e. F1 and accuracy) and that was finally integrated into the privacy-assistant agent was transformer-based.
Transformation of the pedagogical process in the context of digital education

The pedagogical thought of the twenty-first century addresses the pressing issues of education. The article presents an analysis of the development of the ideas in the didactic theory of general education. The purpose of the study is to analyze the current state of the education system in Russia and to present the impact of a certain historical period on the social, cultural, and scientific attitudes of school education. The scientific novelty of the study is our classification of the content of education: classical - non-classical-post-non-classical. New approaches to the organization of the pedagogical process and the education of the cultural type of personality in it are identified. This indicates the need for the development of modern human competencies in the field of communication, the development and processing of information, and social actions. As a result of our research, we were able to identify the following. A new target for school education is being formed. It is important to form knowledge that adequate to the current level of the scientific picture of the world; to develop constructive cognitive abilities; to contribute to the development of moral values and personal meanings. The trend in understanding the content of school education and its pedagogical process is determined: from the "transfer of the basics of science" in the aspect of classical concepts to the personal development of the student. Thus, all these changes are now considered as social and cultural challenges to modern education, didactics as a theory and school teaching as a practice, which are designed to provide an answer based on both the traditions of Russian education and innovations.
Fusion-UWnet: Multi-channel Fusion-based Deep CNN for Underwater Image Enhancement

Underwater image enhancement has been considered as one of the prime research areas due to its massive significance in underwater surveillance and the development of underwater autonomous robotics. Deep learning methods have been used for image processing, where heavy models like GANs and very deep CNNs are being deployed for the task. Due to the bulky nature of the models, they consume significant memory and are numerically expensive in computational tasks, making them inefficient to some degree in underwater exploration tasks. These models are primarily trained over synthetically generated data which makes them less correlative for real-world tasks. This paper proposes a deep network architecture that uses a series of convolutional blocks to fuse significant complementary features of two separate enhanced versions of the input image along with the input one. Further, a combination of perceptual and structural similarity losses is used to find out the error. We have also benchmarked our model on three underwater datasets, highlighting the generalizing capabilities over a mix of real-world and synthetic data.
ADMM based Deep Denoiser Prior for Enhancing Single Coil Magnitude MR images

Recently, deep learning methods are employed for image restoration tasks. An unsupervised learning technique is appropriate for many real time applications due to the scarcity of a large amount of data for training. The conventional deep image prior (DIP) is a CNN based denoiser prior that perform different image restoration tasks by using only a single degraded image. Alternating Direction Method of Multipliers (ADMM) framework over a standard sub-gradient method has already been proposed with DIP method. Inspired by this, we propose a variant of ADMM-DIP method for enhancing single coil magnitude magnetic resonance (MR) images. It is well known that the noise distribution in single coil magnitude MR images is stationary Rician. We achieve the Rician noise removal from single MR image by utilizing the combined effect of MSE, KL divergence and perceptual loss functions. Also, the attention guided dense upsampling network (AUNet) was engaged as the CNN denoiser prior. Our experiments on simulated MR images indicate a better performance of the proposed method. We evaluated different denoising methods both qualitatively and quantitatively.
Using Machine Learning for Explosion Yield Estimation

ABSTRACT We developed a machine learning approach to estimate an explosion yield and height-of-burst or depth-of-burial (HOB or DOB) using a combination of seismic ground-motion and acoustic measurements. The technique employs artificial neural networks (ANNs) with Bayesian regularization suitable for small datasets to reduce the potential for overfitting and improve the network generalization. Using data from multiple explosion experiments conducted in various rock types, we investigated the effect of different seismic and acoustic measurement methods combined with estimated seismic velocity on the yield and the scaled HOB or DOB estimates. The training dataset for the ANN comprised data from 42 explosions conducted in various media at different HOB or DOB. Two additional explosions were used for the method validation. The input features included seismic and acoustic amplitudes, peak frequency, positive phase duration, and the apparent seismic velocity. The presented approach is not limited to a single lithology. Instead, it uses a diverse set of parameters, such as apparent seismic velocity and P-wave peak frequency, to implicitly identify the medium properties within the data-driven framework. Incorporating these parameters resulted in significant improvement of the method performance for both scaled depth and yield estimates. The root mean square error of the scaled depth estimate is on the order of 0.1 m/kg1/3. For the yield estimate, the mean absolute percentage error is less than 10% for both training and validation datasets. The main challenge of using machine learning for yield estimate is a small number of calibration explosions with known yields available for the ANN training. In the future, the developed approach can be further improved by training the ANNs with larger datasets, as more explosion data become available.
XOR ciphers model and the attack to it

This paper will propose a natural language model based on all coherent (meaningful) texts of fixed length used in encryption. We use Shannon's cipher model to describe XOR ciphers by incorporating equinumerous alphabets of plaintexts, keys, and ciphertexts. The encryption function between the plaintext alphabet and the key should be defined as bijective with regard to each variable. The selection of key symbols from the key alphabet to encrypt a text of finite length is made randomly (and possibly randomly and with equal probability). The supposed attack consists of identifying at least two plaintext segments with a given length D in a known ciphertext of a given length. Further, we will estimate the complexity of the attack and calculate the reliability of the attack lower estimate.
Customer-Centric E-commerce Implementing Artificial Intelligence for Better Sales and Service

E-commerce organization started implementing artificial intelligence, since 2016. AI in Web development will redefine the UX experience with more customized content. The learning machine will implement its vision for a better experience in Web apps. The study states that human workers will replace by AI machines. AI means intelligent machines, which make manual tasks such as dynamic pricing, efficient delivery, virtual agents and many more in future. Early adopters of AI were successful for companies like Amazon and Netflix. The research work on artificial intelligence started long back in 1950s. Games include efficient tool for involving and engaging an individual in a particular way. Gaming with AI models can be incorporated in shopping sites to make online shopping fun and stress free. Artificial intelligence is on high demand in the online services especially in E-commerce industry. E-commerce firms are into vast research on improving their artificial intelligence tool to meet the current market demands. The enterprises which implement their Web application with AI are observed to benefit more. The study of the paper starts from AI, AI in gaming to AI implementation in Web applications. It includes vision, predictive analysis, strategy and uncertainty handling in AI model. We have made Pong game analysis in Java script programming and match the various predictive analyses, to apply in E-commerce. It provides customers with their personalized interface which is interactive. AI-enabled E-commerce systems can view their customers’ preferences in real time. The study states that organization which implements this AI model will achieve large sales as well as customer satisfaction.