Reviewerly/exHarmony huggingface dataset:
Fingerspelling Alphabet Recognition Using CNNs with 3D Convolutions for Cross Platform Applications

The proposed communication technology is developed with cross-platform frameworks and consists of two parts: modeling and recognition of Ukrainian dactyl signs. Modeling is performed using realistic 3d hand model with animations of dynamic gesture and transitions between gesture, developed in Unity3D framework. User is able specify different words in the user interface and adjust number of polygons and step of animation to get satisfying performance. The computations can be done both on the device or in web. Recognition model training and serving is done with Tensorflow, which allows to deploy the model on different devices, including mobile, or to perform the model prediction on server in cloud. The dataset with Ukrainian dactyl signs was collected with 50 persons and 1500 images per each gesture, which allowed to train the model with high enough accuracy and robust in different environment conditions. The model is based on the MobileNetv3 convolutional neural network architecture, and with the optimal configuration of layers and network parameters, also in order to take into account temporal data, 3d convolutions were used. On the collected test dataset, which is 10% of the overall augmented dataset, and is 15000 images, with different light, noise and blurring condition and different personas hands, accuracy of over 98% is achieved.
Trade secrets, big data and artificial intelligence innovation: a legal oxymoron?

This chapter examines the Trade Secrets Directive from a technologically informed legal perspective, looking at the possibilities and scope of protection that the Directive offers for knowledge-based activities and business models. Different types of informational resources have long been an important asset of businesses. Informational technologies, automated data retrieval and cross-referencing will produce large quantities of valuable data that can be used for research, development and marketing of all types of products and services. However, artificial intelligence and data mining tools pose a huge challenge to trade secrets as a legal concept.
Translating Natural Language Instructions for Behavioral Robot Navigation with a Multi-Head Attention Mechanism

We propose a multi-head attention mechanism as a blending layer in a neural network model that translates natural language to a high level behavioral language for indoor robot navigation. We follow the framework established by (Zang et al., 2018a) that proposes the use of a navigation graph as a knowledge base for the task. Our results show significant performance gains when translating instructions on previously unseen environments, therefore, improving the generalization capabilities of the model.
Binary sequences derived from differences of consecutive quadratic residues

For a prime $ p\ge 5 $ let $ q_0,q_1,\ldots,q_{(p-3)/2} $ be the quadratic residues modulo $ p $ in increasing order. We study two $ (p-3)/2 $-periodic binary sequences $ (d_n) $ and $ (t_n) $ defined by $ d_n = q_n+q_{n+1}\bmod 2 $ and $ t_n = 1 $ if $ q_{n+1} = q_n+1 $ and $ t_n = 0 $ otherwise, $ n = 0,1,\ldots,(p-5)/2 $. For both sequences we find some sufficient conditions for attaining the maximal linear complexity $ (p-3)/2 $.Studying the linear complexity of $ (d_n) $ was motivated by heuristics of Caragiu et al. However, $ (d_n) $ is not balanced and we show that a period of $ (d_n) $ contains about $ 1/3 $ zeros and $ 2/3 $ ones if $ p $ is sufficiently large. In contrast, $ (t_n) $ is not only essentially balanced but also all longer patterns of length $ s $ appear essentially equally often in the vector sequence $ (t_n,t_{n+1},\ldots,t_{n+s-1}) $, $ n = 0,1,\ldots,(p-5)/2 $, for any fixed $ s $ and sufficiently large $ p $.
Adversarial Fooling Beyond "Flipping the Label"

Recent advancements in CNNs have shown remarkable achievements in various CV/AI applications. Though CNNs show near human or better than human performance in many critical tasks, they are quite vulnerable to adversarial attacks. These attacks are potentially dangerous in real-life deployments. Though there have been many adversarial attacks proposed in recent years, there is no proper way of quantifying the effectiveness of these attacks. As of today, mere fooling rate is used for measuring the susceptibility of the models, or the effectiveness of adversarial attacks. Fooling rate just considers label flipping and does not consider the cost of such flipping, for instance, in some deployments, flipping between two species of dogs may not be as severe as confusing a dog category with that of a vehicle. Therefore, the metric to quantify the vulnerability of the models should capture the severity of the flipping as well. In this work we first bring out the drawbacks of the existing evaluation and propose novel metrics to capture various aspects of the fooling. Further, for the first time, we present a comprehensive analysis of several important adversarial attacks over a set of distinct CNN architectures. We believe that the presented analysis brings valuable insights about the current adversarial attacks and the CNN models.
Fast Single-Image HDR Tone-Mapping by Avoiding Base Layer Extraction

The tone-mapping algorithm compresses the high dynamic range (HDR) information into the standard dynamic range for regular devices. An ideal tone-mapping algorithm reproduces the HDR image without losing any vital information. The usual tone-mapping algorithms mostly deal with detail layer enhancement and gradient-domain manipulation with the help of a smoothing operator. However, these approaches often have to face challenges with over enhancement, halo effects, and over-saturation effects. To address these challenges, we propose a two-step solution to perform a tone-mapping operation using contrast enhancement. Our method improves the performance of the camera response model by utilizing the improved adaptive parameter selection and weight matrix extraction. Experiments show that our method performs reasonably well for overexposed and underexposed HDR images without producing any ringing or halo effects.
PERSONALIZED EDUCATION: ORIGINS, CHARACTERISTICS AND TECHNOLOGY OF USE

1 Don State Technical University (RUSSIAN FEDERATION)2 Novosibirsk Teacher Upgrading and Retraining Institute (RUSSIAN FEDERATION)3 Novosibirsk State Pedagogical University (RUSSIAN FEDERATION)
Composition of Ensembles of Recurrent Neural Networks for Phishing Websites Detection

Phishing remains a continual security threat, causing global losses exceeding 3.5 billion USD in 2019, according to the FBI’s Internet Crime Complaint Center. The Anti-Phishing Working Group (APWG) reported as many as 2,172 unique phishing websites detected per day in 2019. Most of the methods to solve the phishing websites’ detection problem proposed by the scientific community are based on classical classification algorithms on phishing datasets with hand-extracted features. Although these methods demonstrate high accuracies, unfortunately, they are sensitive to changing environment: phishers can learn the most relevant URL features and adapt their attacks to overcome the security check. Therefore, in search of less sensitive methods, deep neural networks were started to employ, as they do not require manual feature extraction and can directly learn a representation from the URL’s sequence of characters. The purpose of this research is to propose a new method for phishing websites’ URL detection based on ensembles of Recurrent neural networks and other types of deep neural networks. The results of our approach are presented in this paper and compared with the performance of other Recurrent neural networks. These results are additionally compared with the performance of classical classification algorithms on the same dataset with 48 features extracted. Our method with no manually extracted feature gives a significant increase in classification accuracy, compared with single Recurrent neural networks, and matches the accuracy of classical classification ensembles with manually extracted features.
Features Fusion based Automatic Modulation Classification Using Convolutional Neural Network

The modulation format is a key parameter that influences the monitoring of the intercepted signals. Automatic modulation classification (AMC) is utilized to recognize the modulation format of the intercepted signals. However, most recent AMC methods neglect the complementarity accross different features. In this paper, we propose a novel feature fusion based AMC scheme using the convolutional neural network (FFCNN). Fused feature is generated by concatenating the two-dimensional spectrum correlation function (SCF) images and the graphic constellation (GC) images. Moreover, the FFCNN classifier is adopted to obtain more discriminative representations, leading to improved final modulation classification performance. Extensive simulations demonstrate that the proposed FFCNN scheme outperforms other recent methods.
Review of Non-English Corpora Annotated for Emotion Classification in Text

In this paper we try to systematize the information about the available corpora for emotion classification in text for languages other than English with the goal to find what approaches could be used for low-resource languages with close to no existing works in the field. We analyze the corresponding volume, emotion classification schema, language of each corresponding corpus and methods employed for data preparation and annotation automation. We’ve systematized twenty-four papers representing the corpora and found that corpora were mostly for the most spoken world languages: Hindi, Chinese, Turkish, Arabic, Japanese etc. A typical corpus contained several thousand of manually-annotated entries, collected from a social network, annotated by three annotators each and was processed by a few machine learning methods, such as linear SVM and Naïve Bayes and (more recent ones) a couple of neural networks methods, such as CNN.
Covid-19 Dataset: Worldwide spread log including countries first case and first death

The article represents coronavirus spread log history. The duration, coronavirus takes to spread from one country to another country, could be seen in this dataset and could predicted the same for future pandemics through this dataset. It is highly dependent on the cabalistic number of variables that is the main navel of these datasets. Information for this dataset is collected from trusted websites, local and international popular newspapers. This coronavirus dataset not only help to track the spreading route of coronavirus but also can be used for predicting the possible spreading route of similar future pandemics. This dataset consists of 186 countries' useful data related to COVID-19 pandemic from November 17, 2019, to May 16, 2020, with 8 unique variables that provide the information of the nature of the spread of COVID-19. The datasets mainly focus on two major fields, first one is First Case which consists of information of Date of First Case(s), Number of confirm Case(s) at First Day, Age of the patient(s) of First Case, Last Visited Country and the other one First Death information consist of Date of First Death and Age of the Patient who died first for every Country mentioning corresponding Continent.This dataset also can perform a bunch of predictions using Machine Learning applications, like -how fast the virus is spreading, affect rate, death rate, death rate and able to represent comparison between other pandemics. Using this dataset, any similar pandemic spreadness could be predicted earlier and necessary precaution measures could be taken.
A Wearable IoT-Based Fall Detection System Using Triaxial Accelerometer and Barometric Pressure Sensor

The aim of this research work is to develop a wearable and IoT-based fall detection system that can potentially be integrated within a smart home or a community health center to improve the quality of life of the elderly. This system would enable caregivers to remotely monitor the activities of their dependents and to immediately be notified of falls as adverse events. The proposed hardware architecture includes a processor, a triaxial accelerometer, a barometric pressure sensor, a Wi-Fi module, and battery packs. This unobtrusive architecture causes no interference with daily living while monitoring the falls. The output of the fall detection algorithm is a two-state flag, transmitted to a remote server in real-time.
Weights for short quartic Boolean functions

A Boolean function in n variables is 2-rotation symmetric if it is invariant under even powers of ρ(x1,…,xn)=(x2,…,xn,x1), but not under the first power (ordinary rotation symmetry); we call such a function a 2-function. A 2-function is called monomial rotation symmetric (MRS) if it is generated by applying powers of ρ2 to a single monomial. If the quartic MRS 2-function in 2n variables has a monomial x1xqxrxs, then we use the notation 2-(1,q,r,s)2n for the function. A detailed theory of equivalence of quartic MRS 2-functions in 2n variables was given in a 2020 paper by Cusick, Cheon and Dougan. This theory divides naturally into two classes, called mf1 and mf2 in the paper. Next to describing the equivalence classes, the second major problem is giving details of the linear recursions that the Hamming weights for any sequence of functions 2-(1,q,r,s)2n (with q<r<s, say), n=s,s+1,… can be shown to satisfy. This problem was solved for the mf1 case only in the 2020 paper. In this paper the problem for the mf2 case is solved, using new ideas about short functions, as defined in this paper. These short functions are also of independent interest, and further results about them are in the paper.
Comparison of the effect of two virtual reality stimuli on pressure pain sensitivity and autonomic response

BACKGROUND AND OBJECTIVES:Virtual reality can have a modulatory action on pain and the autonomic nervous system.This study sought to verify the effect of two different VR stimuli on the pressure pain threshold and heart rate variability.METHODS: Healthy volunteers were randomized into two groups of virtual reality, nature and control.The pressure pain threshold was measured pre and post stimulus and heart rate variability measurements were collected pre, during and after virtual reality stimuli.The exploratory data analysis was performed.The t test was used to compare pressure pain threshold.For the differences in heart rate variability measurements, the linear mixed model was used.The level of significance adopted was 95%. RESULTS:The virtual reality nature group was composed of 19 participants with mean age of 22.7±4.72 years and the control virtual reality group was composed of 22 participants with mean age of 21.13±2.42years.The virtual reality nature group showed an increase in the pressure pain threshold after virtual reality and the mean difference was -0.41 [CI (95%)= -0.74 to -0.07; p=0.01].There was no difference in heart rate variability parameters measured during and after virtual reality stimulation. Comparison of the effect of two virtual reality stimuli on pressure pain sensitivity and autonomic responseComparação do efeito de dois estímulos de realidade virtual na sensibilidade de dor a pressão
An Improved Text Classification Model Based on Memory Convolution Neural Network

This paper proposes a text classification model, called improved memory neural network model, which is used to process large-scale training data. In this model, the optimized transformer feature extractor is used to replace the memory neural network which can not be parallelized. At the same time, the multi-level void convolution matrix is designed to replace the convolution neural network, so as to extract more accurate semantic unit features. Finally, in order to reduce the model parameters, each level of the convolution network pooling layer and the full connection layer are eliminated, but the global average pooling layer is instead used. The experimental results on THUCNews dataset and Twitter dataset show that the proposed method achieves competitive results in the accuracy, model parameters and convergence rate.
AN INTEGRATED MINERAL SPECTRAL LIBRARY USING SHARED DATA FOR HYPERSPECTRAL REMOTE SENSING AND GEOLOGICAL MAPPING

Abstract. Mineral spectral library (MSL) is the foundation of hyperspectral remote sensing, and a significant tool of storing and managing massive mineral spectral data to facilitate the matching or identifying of unknown rocks and minerals conveniently and fast. However, mineral spectral data are scattered and stored in different spectral libraries worldwide, which behave different spectral resolutions, mineral categories and measurement parameters, and hinder its application in field investigation, mineral identification, landcover identification and geological mapping. An integrated MSL using shared data is developed currently in Central South University, China, to improve the properties of MSL. We collected the shared spectral data and related information (e.g., mineral attribute data, spectrometer information, etc.) worldwide, performed data cleaning measures to retain the qualified spectral data and consolidated all the data in a common framework so as to establish a reliable and comprehensive dataset, and developed an integrated MSL for data management and diversified applications. The user can analysis the target spectrum with the spectrum absorption characteristic parameters, and match the measured spectral curve with the reference spectrum in the integrated MSL to find the most similar spectrum curve. It’s crucial to note that a new spectrum classifier was designed to limit the scope of matching for improving the efficiency of identification when the experimental sample lacks the specific information. The integrated MSL is developed in B/S and C/S website environments. A demonstration of functions of the integrated MSL and its preliminary applications are introduced in the article.
A New Local Gaussian Variational Level Set for Globus Pallidus Segmentation

Globus Pallidus is an object in Magnetic Resonance Imaging (MRI) with severe intensity inhomogeneity and noises, such as small, low contrast, and weak boundary.Existing active contour methods fall into false boundaries while segmenting objects, which is smaller than the background like Globus Pallidus.This paper proposes a new local gaussian variational level set (NGVLS) for Globus Pallidus segmentation.We developed an energy term to create a smooth curve and prevent evolving curve into the false boundary.In the experiment, we compare NGVLS qualitatively and quantitively with existing methods such as Chan-Vese (CV), Region Scalable Fitting (RSF), Improved Region Scalable Fitting (Im-RSF), Local Pre-Fitting (LPF), and Local Gaussian Distribution Fitting (LGDF).We use Dice Similarity Coefficent (DSC) and Misclassification Error (ME) to measure the accuracy and error of the segmentation, respectively.The experiment is using 40 MRI datasets from Rumah Sakit National Hospital Surabaya, Indonesia.Qualitatively, the results show that NGVLS shows the best segmentation compared to the existing methods.Quantitatively, NGVLS achieves the highest average, minimum, and maximum value from DSC in 0.8291, 0.7119, and 0.9050, respectively.Also, NGVLS achieves the lowest average, minimum, and maximum value from ME in 0.0050, 0.0023, and 0.0155, respectively.
Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case

Sophisticated infrastructures often exhibit misbehaviour and failures resulting from complex interactions of their constituent subsystems. Such infrastructures use alarms, event and fault information, which is recorded to help diagnose and repair failure conditions by operations experts. This data can be analysed using explainable artificial intelligence to attempt to reveal precursors and eventual root causes. The proposed method is first applied to synthetic data in order to prove functionality. With synthetic data the framework makes extremely precise predictions and root causes can be identified correctly. Subsequently, the method is applied to real data from a complex particle accelerator system. In the real data setting, deep learning models produce accurate predictive models from less than ten error examples when precursors are captured. The approach described herein is a potentially valuable tool for operations experts to identify precursors in complex infrastructures.
A Real-time Algorithm for USV Navigation Based on Deep Reinforcement Learning

Aiming at the demand of flexibility and real-time performance in unknown aquatorium, a path planning algorithm based on Deep Reinforcement Learning (DRL) is proposed. According to plan-avoid-acclimate request, the proposed algorithm involves optimization of net structure and navigation data enrichment based on $A$ 3C, re-regulation of action space of the agent, and is trained with specific tasks in three kinds of maps to improve flexibility. The algorithm is integrated with GPU, which helps achieve high training efficiency and real-time performance by creating a neural network to collect pre-training data. Experimental results show that obstacle ability is confirmed. In comparison with current algorithm, training time reduces by 59.3% and efficiency rises by more than 71.7%. Meanwhile, performance of trained model in unknown environment is validated.
A High-Performance Face Illumination Processing Method via Multi-Stage Feature Maps

In recent years, Generative Adversarial Networks (GANs)-based illumination processing of facial images has made favorable achievements. However, some GANs-based illumination-processing methods only pay attention to the image quality and neglect the recognition accuracy, whereas others only crop partial face area and ignore the challenges to synthesize photographic face, background and hair when the original face image is under extreme illumination (Image under extreme illumination (extreme illumination conditions) means that we cannot see the texture and structure information clearly and most pixel values tend to 0 or 255.). Moreover, the recognition accuracy is low when the faces are under extreme illumination conditions. For these reasons, we present an elaborately designed architecture based on convolutional neural network and GANs for processing the illumination of facial image. We use ResBlock at the down-sampling stage in our encoder and adopt skip connections in our generator. This special design together with our loss can enhance the ability to preserve identity and generate high-quality images. Moreover, we use different convolutional layers of a pre-trained feature network to extract varisized feature maps, and then use these feature maps to compute loss, which is named multi-stage feature maps (MSFM) loss. For the sake of fairly evaluating our method against state-of-the-art models, we use four metrics to estimate the performance of illumination-processing algorithms. A variety of experimental data indicate that our method is superior to the previous models under various illumination challenges in illumination processing. We conduct qualitative and quantitative experiments on two datasets, and the experimental data indicate that our scheme obviously surpasses the state-of-the-art algorithms in image quality and identification accuracy.
Noisy three-player dilemma game: robustness of the quantum advantage

Games involving quantum strategies often yield higher payoff. Here, we study a practical realization of the three-player dilemma game using the superconductivity-based quantum processors provided by IBM Q Experience. We analyze the persistence of the quantum advantage under corruption of the input states and how this depends on parameters of the payoff table. Specifically, experimental fidelity and error are observed not to be properly anti correlated, i.e., there are instances where a class of experiments with higher fidelity yields a greater error in the payoff. Further, we find that the classical strategy will always outperform the quantum strategy if corruption is higher than half.