Reviewerly/exHarmony huggingface dataset:
Real-Time Human Activity Generation using Bidirectional Long Short Term Memory Networks

Human activity recognition (HAR) has extensive real-life applications. Most of the current HAR researches are based on machine learning and deep learning approaches to multi-sensor data. These approaches particularly focus on feature extraction techniques and tune hyperparameters of the network. However, these techniques exclude the size of the dataset into consideration, which is a critical performance indicator. In this paper, the open-source tri-axial accelerometer WISDM dataset has been used for simulation and Bidirectional Long Short-Term Memory (LSTM) networks have been proposed for data generation. For finding out the similarity between the generated and the original data, the losses for each of the activities have been calculated. To evaluate the effect of generated data on performance, an activity classifier has been developed which shows that the generated data has helped to increase the overall accuracy by 4% having the best recognition accuracy as 94.1%.
Gaussian Soft Margin Angular Loss for Face Recognition

Advances in deep learning has lead to drastic improvements in face recognition. A key part of these deep models is their loss function. Consequently developing an efficient and suitable loss function has been an important topic in face recognition in the recent years. Angular-margin-based losses achieve an acceptable performance and inter-class separability. However they are held back by their enforcement of hard margins on all the samples of the training dataset, regardless of whether these samples actually differ from all the other classes enough to enforce a margin. It can be argued that in a large enough dataset with many different settings and age gaps, some faces will look similar to the faces of other classes. In an intuitive and expressive embedding, we expect some faces to be embedded near similar classes with a small margin. Thus we propose a loss function that while maximizing the inter-class distance and intra-class compactness, allows for the samples which naturally reside further from class center to have a smaller margin. We implement an extremely light and fast to train model using MobileNets and achieve accuracy comparable to state of the art method.
New Challenges in Forensic Analysis in Railway Domain

Traditionally, the railway system was closed, vendor-dependent, old-fashioned with an accent on safety. Digitalization process has not bypassed the railways domain and with new upcoming technology and new functionality in railway domain (digital interlocking system, automated train operation, object recognition, GPS satellites localization, artificial intelligence, predictive maintenance, etc.), traditional railway domain has got a vulnerability that can be exploited. In this paper, authors will deal with this "new" upcoming railway technology and challenges in digital investigation process, in such kind of environment.
Improving Security of Neural Networks in the Identification Module of Decision Support Systems

In recent years, neural networks have been implemented while solving various tasks. Deep learning algorithms provide state of the art performance in computer vision, NLP, speech recognition, speaker recognition and many other fields. In spite of the good performance, neural networks have significant drawback- they have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. While being imperceptible to a human eye, such perturbations lead to significant drop in classification accuracy. It is demonstrated by many studies related to neural network security. Considering the pros and cons of neural networks, as well as a variety of their applications, developing of the methods to improve the robustness of neural networks against adversarial attacks becomes an urgent task. In the article authors propose the “minimalistic” attacker model of the decision support system identification unit, adaptive recommendations on security enhancing, and a set of protective methods. Suggested methods allow for significant increase in classification accuracy under adversarial attacks, as it is demonstrated by an experiment outlined in this article.
Analysis of Polynomial Nonlinearity Based on Measures of Nonlinearity Algorithms

We consider measures of nonlinearity (MoNs) of a polynomial curve in two-dimensions (2D), as previously studied in our Fusion 2010 and 2019 ICCAIS papers. Our previous work calculated curvature measures of nonlinearity (MoNs) using (i) extrinsic curvature, (ii) Bates and Watts parameter-effects curvature, and (iii) direct parameter-effects curvature. In this paper, we have introduced the computation and analysis of a number of new MoNs, including Beale's MoN, Linssen's MoN, Li's MoN, and the MoN of Straka, Duník, and S̆imandl. Our results show that all of the MoNs studied follow the same type of variation as a function of the independent variable and the power of the polynomial. Secondly, theoretical analysis and numerical results show that the logarithm of the mean square error (MSE) is an affine function of the logarithm of the MoN for each type of MoN. This implies that, when the MoN increases, the MSE increases. We have presented an up-to-date review of various MoNs in the context of non-linear parameter estimation and non-linear filtering. The MoNs studied here can be used to compute MoN in non-linear filtering problems.
An Abstraction of Semantic Segmentation Algorithms

Semantic segmentation is a process of classifying each pixel in the image. Due to its advantages, semantic segmentation is used in many tasks like cancer detection, robot-assisted surgery, satellite images, self-driving car, etc. in this process, accuracy and efficiency are the two crucial goals for this purpose, there are several state-of-the-art neural networks. In each method, by employing different techniques, new solutions have been presented for increasing efficiency, accuracy, and saving the costs. The diversity of the implemented approaches for semantic segmentation makes it difficult for researchers to achieve a comprehensive view. Due to this, in this paper, an abstract framework for semantic segmentation is offered. This framework consists of 4 blocks that cover the majority of the methods that have been proposed for semantic segmentation. In this paper, we also attempt to compare different approaches and consider the importance of each part in semantic segmentation. Although our proposed framework considers most of the previous methods, maybe a few papers need new blocks.
Classifying Nuclei Shape Heterogeneity in Breast Tumors with Skeletons

In this study, we demonstrate the efficacy of scoring statistics derived from a medial axis transform, for differentiating tumor and non-tumor nuclei, in malignant breast tumor histopathology images. Characterizing nuclei shape is a crucial part of diagnosing breast tumors for human doctors, and these scoring metrics may be integrated into machine perception algorithms which aggregate nuclei information across a region to label whole breast lesions. In particular, we present a low-dimensional representation capturing characteristics of a skeleton extracted from nuclei. We show that this representation outperforms both prior morphological features, as well as CNN features, for classification of tumors. Nuclei and region scoring algorithms such as the one presented here can aid pathologists in the diagnosis of breast tumors.
Object Detection in Natural Scene Images Using Thresholding Techniques

A lot of attention has emerged regarding the aspects of text detection and identification as OCR has generated a lot of prominence over the years. There has been a number of experiments conducted in this field to make the results more and more accurate. Most of the experiments carried out have paid attention to only a few attributes and not a lot of trails have been done for unusual scenarios, like a lot of techniques produces accurate results only for horizontal textual orientation. So there should different techniques for analyzing such images which have a complex background, different font styles, colors, textual orientations. Text detection on images containing texts of different orientations, different font types, and images with complex backgrounds is taken for the proposed work. There are mainly 3 steps in the algorithm proposed, the Canny edge detection approach for gradient filtering is applied in the first stage to detect the skeletal structure of various objects in the image. In the next stage textual threshold-based object filtering is carried out using the convolution technique with a heuristic thresholding model. The textual object filtering after convolution is subject to the last stage called post enhancement technique. In this stage, partial non-textual objects being filtered out are employed for removal based on geometrical properties of gradients of images, thus retaining only the textual objects. Finally, the textual object filtered gradient image is considered as a mask image for mapping it to the original image for text detection. Experimentations are conducted on Google Street View Datasets for which a subjective evaluation procedure is adapted to validate the results resulting in promising outcomes for more than 50% of images.
Classification of First-Episode Schizophrenia Using Wavelet Imaging Features.

This work explores the design and implementation of an algorithm for the classification of magnetic resonance imaging data for computer-aided diagnosis of schizophrenia. Features for classification were first extracted using two morphometric methods: voxel-based morphometry (VBM) and deformation-based morphometry (DBM). These features were then transformed into a wavelet domain using the discrete wavelet transform with various numbers of decomposition levels. The number of features was then reduced by thresholding and subsequent selection by: Fisher's Discrimination Ratio (FDR), Bhattacharyya Distance, and Variances (Var.). A Support Vector Machine with a linear kernel was used for classification. The evaluation strategy was based on leave-one-out cross-validation.
Speeding up multi-SVMs through modified working set selection

Support vector machines (SVMs) possess good accuracy in big data classification. However, the computational cost in both training and testing stages is a critical issue. In this study, we speed up the training speed through an integrated working set selection which is based on a modification of the selection pool of the working set of SVMs. In the testing stage, a lost-min strategy is proposed to accelerate the voting algorithm used in multi-SVMs. The number of the used binary classifiers is reduced from an order of n2 to n (nearly to n−1 or 1.5n−3.5+3/n). The proposed methods were tested with DNA dataset (bioinformatics), Usps datasets (handwritten digits), Letter dataset (English alphabet) and Satimage dataset (satellite imagery of Earth). We further theoretically derive the time complexity of the proposed method approaches to n−1 algorithm in the case of high accuracy. This result is demonstrated through the experimental results for these datasets.
Chinese long text summarization using improved sequence-to-sequence lstm

Abstract Text summarization is an important issue in natural language processing. The existing method has the problem of low accuracy when performing long text summarization. In this paper, We use the LSTM to construct the sequence-to-sequence model, and combine the attention mechanism to process automatic Chinese long text summarization.The experimental results indicate that our method can accurately extract key information from long text, generate high-quality summary.
Poorly Differentiated Colorectal Gland Segmentation Approach Based on Internal and External Stress in Histology Images

Histological assessment of glands is one of the key factors in colon cancer grading. We propose an approach for segmentation of glands in colon cancer histopathology images, which bases on the characteristics of lumens and rough gland boundary. First, we present a U-net for stain separation of H& E image to obtain the H-stain, E-stain, and background stain intensity maps. Subsequently, the epithelial nuclei was identified on the H stain intensity map, and the lumen segmentation was performed on the background intensity map. Then, we employ the axis of least inertia and chain to establish a connection between the lumen and the epithelial nuclei. In the light of connection of the lumen and the epithelial nuclei, more accurately gland segmentation is performed based on the rough gland boundary. The proposed stain separation approach is unsupervised, and the stain separation makes the information contained in the H& E image more independent and easy to identify, so as to deal with the uneven stain intensity and the inconspicuous stain difference. We compare our results on a publicly available data set. The results show that the segmentation approach combining the characteristics of lumens and rough gland boundary has improved the segmentation accuracy.
Nowhere to hide: Anonymous User Recognition Based on Multidimensional Trajectory Set

Abstract Anonymous user identification is one of the core issues in the field of information security and e-commerce applications. Accurately identifying the implicit characteristics of users’ online behavior and the unique identity is important, it has theoretical significance and commercial application value. Based on Massive online clickstream data, this paper proposed AUI algorithms and multidimensional trajectory set model (MTS) to verify the feasibility anonymous user identification. Through the AUI algorithm and vectorization method, a unique multidimensional trajectory set is created for each user. Experiments show that compared with the decision tree and user document methods, the multidimensional trajectory set based on the click data stream has improved the accuracy of recognition and provides an effective solution for user recognition.
Oversampling Algorithm Based on Spatial Distribution of Data Sets for Imbalance Learning

Imbalance problem is widespread in machine learning. Most learning algorithms can't get satisfied performance when they are applied on imbalance data sets, because they can be deteriorated by this problem easily. This paper proposed SDSMOTE method which captures the spatial distribution of imbalance data sets, and changes the tendency of learning algorithm by over sampling by oversampling according to the recognition difficulty. Experiments on 5 UCI data sets validate the effectiveness of this oversampling algorithm.
Biometric System for Identification Person by Face Image

The article provides a classification of existing approaches to face recognition in an image and describes the implementation of a system of biometric identification of a person by his or her face. To search for a face in an image, use the pyramid of histogram of orientation gradients method in combination with a Support Vector Machine (SVM) classifier. A method for constructing a unique human descriptor is presented. To build a descriptor using a trained cascade of regressors, 68 key points of the face are allocated. After that, the coordinates of the key points are passed to the input of the ResNet34 type neural network with the classification layers removed. At the output of the neural network, a descriptor consisting of 128 numbers is formed. Using a neural network allows you to compensate for the rotation of the face in the image to a certain extent. Euclidean distance is used to estimate the proximity of two descriptors. The accuracy of the method on marked faces in the wild (LFW) benchmark was 99.38%. The proposed method is compared with the results of the Microsoft Cognitive Services service.
Increasing the input data length of RSA cryptosystem by applying a hybrid lossless data compression algorithm

Abstract RSA is a well-known and widely used cryptosystem. One major limitation of RSA is its maximum input length depends on the chosen key size and the selected padding. This paper proposed a hybrid data compression algorithm to increase the input data length to be encrypted by RSA. Firstly, a plain text is compressed by the LZW algorithm. The compressed data is padded. After that, the size of the compressed data is further reduced by using continued fraction. Double-compressed data is, then, used as an input of RSA. When using a combination of continued fraction and LZW algorithm, the results showed that, on average, the input data length is increased by 18.91 per cent. In most cases, the processing time is much better than not doing any compression. In the case of decryption, a cipher text is decrypted by RSA first. The padding is removed. Then, the Euclidean algorithm and LZW are used respectively to restore the original data. The hybrid data compression is not only relieved the limitation of RSA but also enhances the security level.
Modification of Chaos Game with variation of compression ratio

Abstract Chaos Game is a form of game drawing a point on a field with certain rules that are repeated iteratively. In general, the new point in the game chaos is the midpoint of the starting point and reference point. The new point is the result of the dilatation with the center of the reference point with a half dilatation factor. The resulting points will form a pattern by paying attention to the nature of self-similarity. This paper will examine the modification of the game chaos rules in generating new points by varying the r compression ratio applied to triangles and rectangles. The compression ratio will be divided into four different intervals. The results of this study indicate that variations in the compression ratio for r = −1, 1orr → +®or 0 &lt; r &lt; 1or −1 &lt; r &lt; 0 do not produce a fractal object, while r &gt; 1or r &lt; −1 will produce a fractal object.
A Study on the Policy Trends for the Revitalization of Medical Big Data Industry

Today's rapidly developing health technology is accumulating vast amounts of data through medical devices based on the Internet of Things in addition to data generated in hospitals. The collected data is a raw material that can create a variety of values, but our society lacks legal and institutional mechanisms to support medical Big Data. Therefore, in this study, we looked at four major factors that hinder the use of medical Big Data to find ways to enhance use of the Big Data based healthcare industry, and also derived implications for expanding domestic medical Big Data by identifying foreign policies and technological trends. As a result of the study, it was concluded that it is necessary to improve the regulatory system that satisfies the security and usability of healthcare Big Data as well as establish Big Data governance. For this, it is proposed to refer to the Big Data De-identification Guidelines adopted by the United States and the United Kingdom to reorganize the regulatory system. In the future, it is expected that it will be necessary to have a study that has measures of the conclusions and implications of this study and to supplement the institutional needs to play a positive role in the use of medical Big Data.
Performance Evaluation of Gradient-based Dimensionality Reduction Methods on Different Devices

In this paper, we study the efficiency of the nonlinear mapping dimensionality reduction technique based on the stochastic and classic gradient descent algorithms. In particular, we implemented these algorithms using CUDA for NVIDIA GPU, HIP for AMD GPU, and OpenMP with AVX2 for CPU. We study algorithms for different volumes of data, different parameter values (gradient step size), and various computing devices, including AMD Radeon Vega 56, NVIDIA GeForce GTX 1080 Ti GPUs, and AMD Ryzen 7 3700X CPU. We found that the effectiveness of GPUs grows with the number of points. Using AMD Radeon Vega 56, we achieved 3.5 and 6.75 times performance improvement over 16-threaded CPU implementation for classic and stochastic gradient descent, respectively. All the experiments are carried out using a synthetic dataset.
Individuation as the key factor of modern socio-philosophical methodology

Switching from trivial representations on society to the level of its theoretical comprehension, we inevitably face the question how to translate the structures that form society from pure abstractions into people&amp;rsquo;s living experience. This described problem is the key topic of this research. Teaching on individuation of the leading theoretician in this area C. R. Jung requires additional refinement taking into account remarks made by H. G. Gadamer regarding changes in the character of discourse that unfolded in Christianity. M. K. Mamardashvili demonstrates in his lectures how the verbal art may become the means of existing of truth and its realization. We not only describe, but simultaneously apply individuation as something that becomes a methodological key to comprehension of modern society. At the same time, it is necessary to shift reasoning on the concept of &amp;ldquo;individuation&amp;rdquo; from psychological plan to a broader ontological plan. The novelty of this research consists in individuation being the key factor in solution of the aforementioned problem. The main conclusion is that individuation can be the method for the development of forms of activity, cognition and reasoning, which ensuring the co-existence of a subject with other subjects and systems do not erase, but rather identify and produce the forms of its distinctness. The active development of existence by people becomes equally comparable with the standards of habitual experience and everyday perceptions. This can restore the logic of common sense, and thus, the reliable structures of practice. &amp;nbsp;
Professional training for future specialist in Bachelor’s degree in Norway

The article reveals the peculiarities of professional training of future specialists in bachelor degrees in Norway, presents information about higher education and duration of studies at the level of first bachelor, organizational and scientific activity of students, presents the difference between practical professional practice and theoretical training, theoretical, self-study search in the field of theory and practice in your chosen specialty. The curriculum provides students with the knowledge to master the general and professional competencies of a physical therapist through both theoretical and practical teaching methods.&#x0D; The overall purpose of organizing the training of future physical therapy professionals in Norway is to create the highest possible correspondence between the practical methods used by the physical therapist in professional practice and to foster students' professional competence in the learning process.&#x0D; In Norway, physical therapists are leading experts in health promotion and physical activity in the health field. Higher educational establishments of the department carry out the preparation of bachelors in physical therapy for three years through different educational courses and through the introduction of innovative methods and techniques of physical therapy. At the end of the program, graduates receive a diploma recognized by the leading health organizations in Norway and the world. Understanding the connection between practical professional practice and theoretical perspectives is basic. The educational program provides knowledge-based practice. The curriculum emphasizes the reciprocity and interaction between thought and action, assessment, and practical skills, since physical therapy is an activity of professional and ethical understanding, physical skills and the ability to evaluate should be integrated into specific situations of interaction with others.