Reviewerly/exHarmony huggingface dataset:
Photographic Visualization of Weather Forecasts with Generative Adversarial Networks

Abstract Outdoor webcam images jointly visualize many aspects of the past and present weather, and since they are also easy to interpret, they are consulted by meteorologists and the general public alike. Weather forecasts, in contrast, are communicated as text, pictograms, or charts, each focusing on separate aspects of the future weather. We therefore introduce a method that uses photographic images to also visualize weather forecasts. This is a challenging task because photographic visualizations of weather forecasts should look real and match the predicted weather conditions, the transition from observation to forecast should be seamless, and there should be visual continuity between images for consecutive lead times. We use conditional generative adversarial networks to synthesize such visualizations. The generator network, conditioned on the analysis and the forecasting state of the numerical weather prediction (NWP) model, transforms the present camera image into the future. The discriminator network judges whether a given image is the real image of the future, or whether it has been synthesized. Training the two networks against each other results in a visualization method that scores well on all four evaluation criteria. We present results for three camera sites across Switzerland that differ in climatology and terrain. We show that even experts struggle to distinguish real from generated images, achieving only a 59% accuracy. The generated images match the atmospheric, ground, and illumination conditions visible in the true future images in 67% up to 99% of cases. Nowcasting sequences of generated images achieve a seamless transition from observation to forecast and attain good visual continuity.
Poincaré Fréchet mean

Generalizing the Fréchet mean from the Euclidean metric is not able to properly capture the geometric characteristics of many non-trivial operations, such as the non-dot inner product and non-Euclidean gradients defined on the manifold. One effective solution is to derive its hyperbolic representations in the Poincaré or hyperboloid model. Our goodness-of-fit testing shows that the Poincaré Fréchet mean achieves much lower χ2 power than that of the hyperboloid and typical non-linear kernels with regard to parameter perturbations. However, recent advanced optimization solvers, such as Riemannian gradient descent and minimizing upper bound, may result in imprecise convergences. This paper presents an (1−ϵ)-approximation approach to search a core-set on the Poincaré model, reducing deviations of the Poincaré Fréchet mean to its optimum. A hierarchical splitting algorithm that implicitly explores the hyperbolic representations for an arbitrary manifold is then presented. Experiments show that the (1−ϵ) Poincaré Fréchet mean adopted in hierarchical splitting, achieves better representations than Euclidean, kernel, and Lorentzian Fréchet means in graph and image data.
Resnet Features and Optimization Enabled Deep Learning for Indoor Object Detection and Object Recognition

Indoor object detection and recognition (IODR) helps to progress the quality life of the visually weakened person by helping them in indoor navigation along with their day-to-day activities. This research introduced the African Adam Optimization-based SqueezeNet (AAO-based SqueezeNet) for object detection and recognition. Moreover, Generative Adversarial Network (GAN) is exploited to detect the thing in the images with the assistance of a generator and discriminator in GAN. The features, such as texture and Resnet features are extracted in the detected objects. In addition, the objects are recognized using SqueezeNet based on the extracted features. Moreover, the training process of SqueezeNet is done using devised AAO algorithm, which is the concatenation of the African Vultures Optimization Algorithm (AVOA) and Adam optimizer (AO). Analysis is done based on the metrics, such as testing accuracy, precision, and recall using the Mendeley dataset and it accomplished the values of 0.905, 0.902, and 0.939, respectively.
Revisiting lower dimension lattice attacks on NTRU

Vectors are called short vectors when the Euclidean norm of vectors is relatively small. By now, some researches have been done to find sufficient short vectors in certain variants of NTRU lattice to analyze the security of NTRU systems. These variants are all taking advantage of the property of NTRU cryptosystem to modify the standard NTRU lattice. Related variants include zero-run lattices with dimension 2N, zero-forced lattices with dimension 2(N−r), r<N, and IN-lattices with dimension N. Motivated by different constructions, we revisit related variants and develop some techniques to further reduce the dimension of IN-lattice, such that the expected breaking time can be decreased significantly. Firstly, by working with the cyclic matrix associated with the NTRU public key h, shifts of the private vector f can be accommodated. Hence, t randomly chosen coefficients in a shift of the private vector f can be forced to equal zero. This not only results in a new lattice with a lower dimension but also creates an environment to utilize parallel mechanisms to improve the probability of success. Finally, experiments have demonstrated that our approach outperforms the previous methods.
A multiscale feature fusion method for cursive text detection in natural scene images

Text detection in natural images is a challenging problem due to variations in text size, aspect ratio, alignment and background complexity. This paper proposes a multiscale feature fusion convolutional neural network method to detect cursive and multi-language text in natural images. The proposed method combines VGG-16 features at multi-scales and multi-layers and creates a new convolutional feature map of shallow and deep layers. On top of convolutional feature map, a vertical text proposal generation method is used that generates fixed-size text proposals. A recurrent layer is implemented which takes the convolutional feature maps of 3×3 window as sequential input and updates the recurrent state internally in the hidden layers. The output of recurrent layer is mapped to the two fully connected layers to predict the text/non-text region proposals and bounding boxes regression. The model is evaluated on a custom-developed Urdu scene text dataset and the ICDAR-MLT17 Arabic text image dataset.
The differential spectrum and boomerang spectrum of a class of locally-APN functions

In this paper, we study the boomerang spectrum of the power mapping $$F(x)=x^{k(q-1)}$$ over $${\mathbb {F}}_{q^2}$$ , where $$q=p^m$$ , p is a prime, m is a positive integer and $$\gcd (k,q+1)=1$$ . We first determine the differential spectrum of F(x) and show that F(x) is locally-APN. This extends a result of (IEEE Trans. Inf. Theory 57(12):8127-8137, 2011) from $$(p,k)=(2,1)$$ to general (p, k). We then determine the boomerang spectrum of F(x) by making use of its differential spectrum, which shows that the boomerang uniformity of F(x) is 4 if $$p=2$$ and m is odd and otherwise it is 2. Our results not only generalize the results in Hasan et al. (Des Codes Cryptogr 89:2627–2636, 2021) and Yan et al. (Adv Math Commun 16(4):1111–1120, 2022) but also extend the example $$x^{45}$$ over $${\mathbb F}_{2^8}$$ in Hasan et al. (Des Codes Cryptogr 89:2627–2636, 2021) into an infinite class of power mappings with boomerang uniformity 2.
A green scheduling algorithm for cloud-based honeynets

Modern businesses leverage cloud architecture to achieve agile and cost-effective technology services. Doing so comes at the expense of the environment though cloud technologies consume large quantities of energy. Cloud energy consumption is concerning in light of global climate trends and dwindling fossil fuel reserves. Consequently, increasing attention is given to sustainable and green cloud computing, which seeks to optimize compute-resource allocation and usage of virtualized systems and services. At the same time, progress toward sustainable and green cloud technology is impeded because as more enterprises deploy services into cloud architecture, cybersecurity threats follow. Unfortunately, cybersecurity technologies are optimized for maximum service overwatch without regard for compute resources and energy. This negates the energy reduction achieved in recent sustainable technology advancements. In this work, a generalized cybersecurity honeynet scheduling algorithm is proposed, in which power, CPU, and network overhead are operationalized to increase sustainability while balancing defensive mechanisms. The work describes both the mathematical foundation for the algorithm and a pseudocode proof of concept.
A Novel Classification of Uncertain Stream Data using Ant Colony Optimization Based on Radial Basis Function

There are many potential sources of data uncertainty, such as imperfect measurement or sampling, intrusive environmental monitoring, unreliable sensor networks, and inaccurate medical diagnoses. To avoid unintended results, data mining from new applications like sensors and location-based services needs to be done with care. When attempting to classify data with a high degree of uncertainty, many researchers have turned to heuristic approaches and machine learning (ML) methods. We propose an entirely new ML method in this paper by fusing the Radial Basis Function (RBF) network based on ant colony optimization (ACO). After introducing a large amount of uncertainty into a dataset, we normalize the data and finish training on clean data. The ant colony optimization algorithm is then used to train a recurrent neural network. Finally, we evaluate our proposed method against some of the most popular ML methods, including a k-nearest neighbor, support vector machine, random forest, decision tree, logistic regression, and extreme gradient boosting (Xgboost). Error metrics show that our model significantly outperforms the gold standard and other popular ML methods. Using industry-standard performance metrics, the results of our experiments show that our proposed method does a better job of classifying uncertain data than other methods
Safe Roads: an Integration between Twitter and City Sensing

With the increasing growth of devices connected to the Internet and the evolution of Internet of Things (IoT), the concept of a modern interconnected city is gaining popularity. On the other side, social media is providing citizens and authorities with information near real-time about incidents or events in the city. Therefore, the monitoring of the city and its roads through smart sensing can be coupled with social media to integrate different sources of information and provide better decisions. While some existing works make use of either social media or conveniently located sensors in key spots in a city, our work focuses on creating a platform that analyzes the data from two types of sources - sensors, such as radars and video cameras, and social networks, like Twitter, to find instances of road safety and provide alerts to authorities and the community. This approach is implemented and tested in a real city platform, and results show the different types of alerts received with the integrated platform.
Accelerating Command Injection Vulnerability Discovery in Embedded Firmware with Static Backtracking Analysis

Command injection vulnerability is a severe threat to the embedded device. Most methods detect command injection vulnerability with taint analysis and symbolic execution and achieve promising results. However, they waste too much time analyzing secure sink call-sites, resulting in less efficient vulnerability detection.
XR Communication System for Remote Control Wheelchairs

With the introduction of the IoT, communication is becoming more diverse. While IoT is becoming more and more widespread, there are some forms of human communication are being lost. The development of self-propelled robots has progressed, and electric and automated wheelchairs are also being utilized. A caregiver was originally required to operate a wheelchair. However, with the advent of IoT wheelchairs, wheelchairs can be operated by wheelchair users only, increasing convenience. On the other hand, wheelchairs have been used as a kind of communication between caregivers and wheelchair users, and the development of IoT wheelchairs has reduced the opportunities for communication between caregivers and wheelchair users. In this paper, we propose a method to reproduce communication scenarios in the following two scenarios using an IoT wheelchair and present the design and implementation of the prototype created.
Targeted Black-Box Side-Channel Mitigation for IoT✱

In this paper we present techniques for generating targeted mitigation strategies for network side-channel vulnerabilities in IoT applications. Our tool IoTPatch profiles the target IoT application by capturing the network traffic and labeling the network traces with the corresponding user actions. It extracts features such as packet sizes and times from the captured traces, and quantifies the information leakage by modeling the distribution of feature values. In order to mitigate the side-channel vulnerabilities, IoTPatch uses the information leakage measure over features to prioritize specific features and synthesizes a packet padding and delaying strategy based on an objective function for minimizing information leakage and time and space overhead. IoTPatch provides a tunable mitigation strategy where the trade-off between the information leakage and performance overhead can be adjusted to accommodate needs of different applications. We evaluate IoTPatch on three network benchmarks and demonstrate that IoTPatch can discover and quantify the information leakage and synthesize a set of Pareto optimal mitigation strategies performing better than the prior work in terms of reducing leakage and overhead.
Autographs of Peter the Great: Reading with Artificial Intelligence Technologies and Creating an Electronic Archive

The article is devoted to modern digital methods of working with the handwritten heritage of Peter I. They were applied within the framework of the scientific project "Autographs of Peter the Great: Reading by artificial intelligence technologies". The project was initiated by the Russian Historical Society and implemented by specialists of the St. Petersburg Institute of History of the Russian Academy of Sciences, Sberbank PJSC. The article describes the methodology of preparing a data set for creating a program for machine reading of the manuscripts of Peter the Great ("Digital Peter"). Special emphasis is placed by the authors on the principles of transcribing of the historical text developed during the project. In addition, the cases of the use of non-letter characters by Peter I and the difficulties caused by this in the formation of a data set are analyzed. The article also reflects the results of the created algorithm and identifies variants of the organization of the text of Peter I, which reduce the quality of recognition. The authors also paid attention to the electronic archive "Autographs of Peter I", which became a continuation of the project on machine reading of the manuscripts of the first Russian emperor. The archive, which is being worked on, contains digital copies of Peter's autographs, the results of their recognition by the Digital Peter program, as well as scientific publications of these unique historical sources. The Internet portal "Autographs of Peter I" is associated with the resource: "Biochronics of Peter the Great day by day" (created on the HSE website). The connection of the two sites opens up additional opportunities for researchers: each digitized autograph is introduced into a historical context.
Organization of the Educational Process in Modern Conditions of Digital Transformation of Education

The object of the study is the pedagogical training of highly qualified personnel within the framework of the adjunct system of the Ministry of Internal Affairs of Russia. The subject of the study is the organization of the educational process in educational institutions of higher education of the Ministry of Internal Affairs of Russia based on the use of digital technologies corresponding to the digitalization of education developing in our country. The main requirements of the state policy in the field of education in the conditions of digitalization of society are considered. Based on the study of this issue, as well as the use of practical experience, the paper highlights the main approaches and principles of the organization of the modern educational process, taking into account the personalization of learning. The necessity of changing the attitude to the organization of the educational process aimed at updating the goals, content, methods, means and forms of education due to the influence of digital technologies is emphasized. The methodology of the research is presented by empirical methods that provide an analysis of the existing practice of organizing the educational process in educational institutions of higher education of the Ministry of Internal Affairs of Russia, the use of methodological experience, as well as generalization of existing models of organizing the learning process in the conditions of digitalization of society, using personal experience of organizing the educational process according to training programs for scientific and pedagogical personnel. The novelty of the research is the authors' identification of principles and methods, as well as stages of the organization of the educational process in the adjunct. The conclusion is made about the need to expand the possibilities of using digital technologies to ensure the individualization of training programs for the training of scientific and pedagogical personnel.
Rendering the image of glare effect based on paired and unpaired dual generative adversarial network

It is a great challenge to rendering glare on image as the current rendering algorithms did not consider well the refraction of human eyes, thus the effect of rendering, in some critical application such as vehicle headlamps, is not real and may affect the safety evaluation. The traditional glare rendering algorithm relies on a large number of hand-designed wave optics processing operators, not only cannot complete the rendering work online in real time, but also cannot cope with the complex and changeable imaging conditions in reality. The mainstream generative adversarial network based algorithms in the field of image style translation are introduced to generate glare effect, which could be rendering online in a real time, however they still fail to render some effects such as detail distortion. In this work, we present a novel glare simulation generation method which is the first algorithm to apply a generative model based style transfer method to glare rendering. In a nutshell, a new method named Glare Generation Network is proposed to aggregate the benefits of content diversity and style consistency, which combines both paired and unpaired branch in a dual generative adversarial network. Our approach increase the structural similarity index measure by at least 0.039 on the custom darkroom vehicle headlamp dataset. We further show our method significantly improve the inference speed.
Pengaruh Pembelajaran E-Learning Terhadap Efektifitas Guru Mata Pelajaran Di SMTK Kota Kupang

This research aims to determine the effect of e-learning learning on the effectiveness of subject teachers in Christian Theology Middle School, Kupang City. This study uses a quantitative approach. The analysis technique used is by using linear regression analysis techniques. Then the results obtained are Reliability test results for variables X and Y, all of which are reliable because of the value of Cronbach, s Alpha&gt; r Table (0.05). After the reliability test, the researcher conducted a descriptive statistical test and continued with the normality test. In the residual normality test, the data is normal because the sig value is 406&gt; 0.05. Followed by the normality analysis test, the researchers continued to the linearity analysis test. Based on the results of the linearity test, it was known that the sig. deviation linearity value was 0.189. Because of the sig. 0.189.&gt; 0.05, then it can be concluded that Ho is rejected and Ha is accepted. This means that there is a linear relationship between e-learning learning and subject teachers' effectiveness in the last analysis test, namely hypothesis testing. Based on the results of the correlation test, it is known that the sig. Value is 0.00. Because of the sig. 0.00. &lt;0, 05 it can be concluded that Ho is rejected and Ha is accepted. This means that there is a significant influence between e-learning learning and the effectiveness of subject teachers. With the reason that the value of R (correlation) 0.989&gt; 0.05 with the influence (R square) 0.979 or 97.9%. Thus, the hypothesis testing in this study produces Ha.
Typologies and Graphics

What happens in this method is that the whole thing rises into a picture. It helps the researcher to present the idea clearly and to make it clearer to the reader. This is a very old methodology. Such offerings include the swastika of ancient Indian religions, the Dharma Chakra, which condenses the teachings of the Buddha, and the cross of Christianity.
Quantum Fisher information matrix for the two-axis twisting model at thermal equilibrium

Quantum Fisher Information Matrix (QFIM) is a fundamental concept in quantum-theoretical measurement that emphasizes the importance of quantum Cramér–Rao bound in estimating quantum parameters. Many relationships have been discovered between QFIM and many recent areas of research in quantum information. These relationships indicate that QFIM is not just a concept in quantum metrology but is a fundamental quantity in quantum information. The ability to estimate parameters and describe the optimal induced conditions for the thermal state of the two-axis twisting model was examined. QFIM is used as an estimator for multiple parameters. We show that simultaneous estimation reduces the estimate of parameter variation, so the accuracy of simultaneously estimating parameters is much better than individually. Each parameter can be used to control the criterion to reduce the minimum differences between other parameters.
Active Learning Entropy Sampling based Clustering Optimization Method for Electricity Data

Clustering is a crucial part in the field of data mining, and common clustering methods include divisionbased methods, hierarchy-based methods, density-based methods, and grid-based methods. In order to improve the accuracy of clustering, an optimization study is made mainly for the division-based method FCM clustering, and an FCM clustering method that integrates active learning and principal component analysis (PCA) is proposed. The method first uses principal component analysis to reduce the dimensionality of the data to reduce the computation of electricity data, then trains the sample model by active learning, and introduces the entropy (Entropy) method in the uncertainty sampling method, the larger the entropy means the greater the uncertainty of the sample, and the smaller the entropy means the smaller the uncertainty of the sample, so as to filter the electricity data, and finally the electricity data are clustered by FCM clustering The power data is finally categorized by FCM clustering, and with the proliferation of power data, the power data can be more accurately categorized using this method to achieve the stability of the power grid as well as the utilization rate. Experimental results on three datasets show that this method improves the accuracy of power data clustering by up to 2 percentage points compared to the traditional clustering method without active learning, and achieves good results in each dataset compared to other methods.