Reviewerly/exHarmony huggingface dataset:
Multiclass Classifiers for Processing Archives of Accidents in Manufacturing

The impossibility of total state control in hundreds of thousands enterprises leads to numerous industrial accidents that is a significant social problem. The traditional way for solution of this problem includes careful work of experts with the archives of accidents, which reflect their causes and preventive measures to avoid such cases in the future. Nowadays, the work with these archives is carried out manually due to the lack of tools for processing acts of accidents. In this paper we offer classifiers for these archives, which could facilitate the work of experts and thus improve the quality of their solutions. Our contribution is: 1) comparison and choice of the best procedures for preprocessing data, and 2) choice and adjustment of the best methods of classifications. The program platform is Python. The results of experiments proved to be very promising.
Scalable Video Classification using Bag of Visual Words on Spark

Video classification is an important and active field of computer vision research due to its variety of applications (e.g. action recognition), ranging from intelligent surveillance to human-computer interaction. Recently, the rapid growth of camera devices and multimedia sharing platforms has created a tremendous amount of video data. Accordingly, there is the growing demand of the parallel and distributed video processing for dealing with the data-intensive and computing-intensive tasks of the recognition system. These tasks are widely recognized to be time-consuming when running over large-scale video data. In this paper, we propose a distributed framework built on the top of the emerging big data technology Spark which is well-designed for the in-memory parallel computing system. Particularly, based on the state-of-the-art bag of visual words model (BoV) for video classification, we provide a comprehensive study of key steps in BoV (i.e. feature extraction, codebook generation, and feature encoding), and propose their fast and distributed algorithms. In experiments, our results further demonstrate the efficiency of proposed algorithms as well as the scalability of our framework on a public dataset UCF101 by greatly speeding up the process of video classification.
An Improved Container Scheduling Algorithm Based on PSO for Big Data Applications

Existing big data computing and storage platforms are generally based on traditional virtual machine technology, which often results in low resource utilization, a long time for flexible scaling and expanding clusters. To deal with these problems, this paper proposes an improved container scheduling algorithm, Kubernetes-based Particle Swarm Optimization (K-PSO), for big data applications based on Particle Swarm Optimization (PSO). The K-PSO algorithm converges faster than the basic PSO algorithm, and the algorithm running time is reduced by about half. The K-PSO capacity for big data applications is implemented in the Kubernetes container cloud system. The experimental results show that the node resource utilization rate of the improved scheduling strategy based on K-PSO algorithm is about 20% higher than that of Kube-Scheduler default strategy, BalancedQosPriority strategy, ESS strategy, and PSO strategy while the average I/O performance and average computing performance of Hadoop cluster are not degraded.
Elimination of distortions on images in the existence of turbulence

Abstract The aim of the study is to eliminate the influence of the turbulence model on the photographed images. This problem is solved by computer processing of images containing such distortions. In the course of the work, images with simulated turbulent distortions were obtained, and they were also post-processed using cascade filters.
Simultaneous state and parameter estimation based on AUMV estimator and IBR-KF

We proposed a new robust states and parameters estimator for nonlinear systems in this paper. The proposed estimator is derived based on Approximated Unbiased Minimum Variance (AUMV) estimator [3] and Intrinsically Bayesian Robust Kalman filter (IBR-KF) [4]. So, the proposed method takes over the merits of both method. That is, the estimated states are not influenced by the parameter estimation error as in the AUMV estimator, and the estimated parameter is robust against observation noise like the IBR-KF.We confirm the validity of the proposed methods by numerical simulations.
O princípio da territorialidade nas fronteiras da arquivologia e da história

This article treats the relations between Archival Science and History, from the Principle of Territoriality. The importance of this study lies in the possibility of a better understanding of the theorical and practical dialogues between these two disciplines, since the archival document is an object that unites these two fields of knowledge. The objective is to understand how the interlocutions between Archival Science and History occur through the Principle of Territoriality. Methodologically, the article has a qualitative, exploratory, descriptive and explanatory approach. Based a bibliographical research in the literature of these disciplines, resumes the historical development of Archival Science as a scientific discipline and its interlocutions with History. The results point out the contributions of the Principle of Territoriality for Archival Science, the function of the archival document as proof and testimony, the research instruments as means for the identification and retrieval of documents and, also, the archival document as a link between the two disciplines.
Which Patient to Treat Next? Probabilistic Stream-Based Reasoning for Decision Support and Monitoring

Providing decision support for questions such as "Which Patient to Treat Next?" requires a combination of stream-based reasoning and probabilistic reasoning. The former arises due to a multitude of sensors constantly collecting data (data streams). The latter stems from the underlying decision making problem based on a probabilistic model of the scenario at hand. The STARQL engine handles temporal data streams efficiently and the lifted dynamic junction tree algorithm handles temporal probabilistic relational data efficiently. In this paper, we leverage the two approaches and propose probabilistic stream-based reasoning. Additionally, we demonstrate that our proposed solution runs in linear time w.r.t. the maximum number of time steps to allow for real-time decision support and monitoring.
Achieving Data Security, Access Control and Authentication of Controllers in Hierarchical Software Defined Networking with Attribute Based Encryption

Software defined networking (SDN) separates the data layer and the control layer to achieve logical centralization, scalability and programmability. In hierarchical software defined networking (HSDN), controllers are classified into the upper controller- Root Controller (RC) and the lower controller- Local Controller (LC) to improve the scalability of the network. HSDN effectively relieve the workload of controllers. However, the features of HSDN puts forward higher requirements of data privacy protection and access control. Because RC stores global network data, it must ensure authorized access and prevent the forged data. The attribute-based encryption scheme can provide fine-grained data access control and data privacy protection of controllers at the same time. When LC accesses data in RC, the algorithm of ciphertext-policy attribute-based encryption with identity authentication (CP-ABE-IA) is presented to protect the data privacy of RC and guarantee the legitimate access of LC. When LC sends message to RC, we propose an algorithm of key-policy attribute based signcryption for multi-access structures (KP-ABSC-MAS). KP-ABSC-MAS provides data privacy protection and verification as well as the authentication of LC.
Combined coding of bit planes of images

The aim of this work is to reduce the computational complexity of lossless compression in the spatial domain due to the combined coding (arithmetic and Run-Length Encoding) of a series of bits of bit planes. Known effective compression encoders separately encode the bit planes of the image or transform coefficients, which leads to an increase in computational complexity due to multiple processing of each pixel. The paper proposes the rules for combined coding and combined encoders for bit planes of pixel differences of images with a tunable and constant structure, which have lower computational complexity and the same compression ratio as compared to an arithmetic encoder of bit planes.
Multiple Vessel Cooperative Localization Under Random Finite Set Framework With Unknown Birth Intensities

The key challenge for multiple vessel cooperative localization is considered as data association, in which state-of-the-art approaches adopt a divide-and-conquer strategy to acquire measurement-to-target association. However, traditional approaches suffer both the computational time and accuracy issues. Here, an improved algorithm under Random Finite Set statistics (RFSs) is proposed, in which the Probability Hypothesis Density (PHD) filter is utilized to address the aforementioned issues, by jointly estimating both the number of vessels and the corresponding states in complex environments. Furthermore, to avoid the prior requirement constrain with respect to the PHD filter, the pattern recognition method is simultaneously utilized to calculate the birth intensities. Simulation results exhibit the proposed approach performs better than normal PHD for multiple vessel cooperative localization, in scenarios of unknown birth intensity.
GENERATION OF GABOR FILTERS BASED ON ATEB FUNCTIONS

The identification of fingerprints in the security information system is investigated. Fingerprints were performed, papillae thinning was performed, Sherlock and Monroe fields were oriented, Ateb-Gabor filtration was performed, and three-dimensional Ateb-Gabor properties were investigated for different rational parameters. Their effect on filtering and subsequently on identification has been identified. Ateb-Habor filtering for biometric prints is applied, which greatly expands classic filtering by introducing a wider set of filters and providing a holistic approach to identification. The skeleton was made using the Hilditch algorithm. The next step is to create guide fields using the method suggested by Sherlock and Monroe in forming the direction of the papillary lines. Uses a spine / nine pixel average. It simulates a sensor with a resolution of 500 dpi. Frequency in areas decreases depending on position. The Hilditch algorithm generates a vector image. This is an image of a loaded graph - that is, found endpoints, intersections of the top of the graph, and lines and arcs
Example-Guided Identify Preserving Face Synthesis by Metric Learning

Generative adversarial networks (GANs) are commonly applied to example-guided identify preserving face synthesis. A binary classifier is used as style consistency discriminator in GAN model in order to ensure the consistency of style. However, the over-fitting problem of a binary classifier downgrade its discrimination ability on style consistency. In this paper, we propose a style consistency discriminator based on metric learning, which performs better in keeping identity information and guaranteeing consistency in style between input examplar and result. Through separating the positive pairs form the negative, metric learning model can efficiently measure the similarity between the synthesis face and the genuine face. The experimental results indicate that the metric learning performs better than a binary classifier in terms of preserving style consistency.
Partial Label Learning of RF Emitters with LSTMs

As modern military radars are becoming more agile, Radio Frequency (RF) is becoming less of a discriminator for identification. Along with RF agility, radars that are low probability of intercept (LPI) make consistent detection and measurement of discriminating modulation features more difficult. In light of these challenges, a class of Recurrent Neural Networks (RNNs) called Long Short Term Memory (LSTM) networks will be demonstrated on a real dataset to exploit the temporal features of measured RF from two ambiguous agile emitters and classify with an accuracy of 92.5%. Future areas of research and application on this topic will be discussed as well.
Binarization Using Morphological Decomposition Followed by cGAN

This paper presents a novel binarization scheme for stained decipherable patterns. First, the input image is downsized, which not only saves the computation time, but the key features necessary for the successful decoding is preserved. Then, high or low contrast areas are decomposed by applying morphological operators to the downsized gray image, and subtracting the two resulting output images from each other. If necessary, these areas are further subjected to decomposition to obtain finer separation of regions. After the preprocessing, the binarization can be done either by GMM to estimate a binarization threshold for each region, or the binarization problem is treated as an image-translation task and hence the conditional generative adversarial network (cGAN) is trained using the high or low contrast areas as conditional inputs.
LDSNE: Learning Structural Network Embeddings by Encoding Local Distances

Network embedding algorithms learn low-dimensional features from the relationships and attributes of networks. The basic principle of these algorithms is to preserve the similarities in the original networks as much as possible. However, existing algorithms are not expressive enough for structural identity similarities. Therefore, we propose LDSNE, a novel algorithm for learning structural representations in both directed and undirected networks. Networks are first mapped into a proximity-based low-dimension space. Then, structural embeddings are extracted by encoding local space distances. Empirical results demonstrate that our algorithm can obtain multiple types of representations and outperforms other state-of-the-art methods.
Modelling Chaotic Time Series Using Recursive Deep Self-organising Neural Networks

This paper proposes a Machine Learning (ML) algorithm that exhibits markedly different behaviour when trained with chaotic input data, compared to non-chaotic data. It will be demonstrated that the output of the ML system is itself chaotic when it is trained on a chaotic input. The proposed algorithm is a deep network with both direct and recurrent connections between layers, as well as recurrent connections within each layer.
Medical Image Text Area Detection Based on Feature Reuse Convolutional Neural Network

In order to solve the problem of Chinese medical image text being missed and misdetected under the CTPN model, a new convolutional neural network DVNet based on the fusion of VGG convolutional neural network and DenseNet dense network was proposed. DVNet takes the first two layers of VGG network for deep feature extraction, and then connects DenseNet dense modules. Using the idea of feature reuse, the features of the front convolutional layer and the features of the back convolutional layer are output together. During post-processing, NMS is used to filter out redundant text boxes. In the Chinese medical text data set provided, three different networks, VGG, DenseNet and DVNet, were used to detect the text. The experimental results showed that the precision rate of DVNet were improved by 2%-3% compared with VGG and DenseNet.
An Efficient Dynamic Group Signatures Scheme with CCA-Anonymity in Standard Model

Group signatures is a cryptographic primitive, simultaneously supporting anonymity and traceability. Though many group signatures schemes have been presented, some drawbacks still exist in these schemes at the aspects of security, performance and functionality. In order to overcome these issues, this paper proposes a new dynamic group signatures scheme with CCA-anonymity, allowing the members enroll the group dynamically. By making use of the Groth-Sahai proof system and verifiable encryption technologies, this paper constructs the scheme in detailed under the decisional linear assumption and q-strong diffie-hellman assumption. And in standard model, we prove our scheme meet CCA-anonymity, traceability and non-frameability. Finally, compared with other existing group signatures schemes, the proposed scheme is more secure and efficient on both the computation cost and communication cost.
Development of Underwater Mobile Robot Equipped with Landing Mechanism for Nuclear Reactor Survey

This paper discusses the development of exploration robot inside containment vessel aiming at the decommissioning of Fukushima Daiichi Nuclear Power Plant. Focusing on ultrasonic measurement, the developed robot supports transportation and measurement of the measuring instrument. In order to achieve this, the robot has the landing function for stationary and the underwater moving function. In order to integrate these two functions, we designed and prototyped, examined optimization, and verified its effectiveness by control experiment.
Improved Single Image De-Hazing Via Sky Region Detection, Classification and Illumination Refinement

This paper presents an automated sky detection technique based on statistical and fuzzy rule-based edge detection for improved hazy image contrast enhancement. This is significant since most conventional de-hazing approaches yield hazy images with over-enhanced sky regions and under-enhanced detail regions due to inability to adaptively determine and enhance such regions. Earlier and current schemes developed to remedy this issue are highly complex, usually require training with vast amount of images and manual tuning of one or several parameters. The proposed method utilizes standard deviation and fuzzy logic-based edge detection combined with thresholding algorithms to generate a homogeneity map identifying sky and non-sky regions. The areas of these regions are subsequently computed and used to obtain a homogeneity ratio. The ratio is then used to trigger a decision-based, switching scheme incorporated into a partial differential equation (PDE) de-hazing algorithm to improve results. Alternatively, a log illumination refinement method is proposed as a less complex alternative combined with the modified PDE algorithm to process hazy images without degrading sky regions, while yielding brighter images. Several image datasets from the literature were used to validate the proposed approaches and yielded mostly consistent and comparable results similar to or better than algorithms from the literature.
Multiple Feature Point Discriminant Analysis and Its Application to Feature Extraction

In this paper, a novel linear subspace learning approach, named Multiple Feature Point Discriminant Analysis (MFPDA), is proposed. MFPDA is in order to maximize the multiple feature point between class scatter and minimize the multiple feature point within-class scatter. Some experiments are performed on FKP database, AR face database, and ORL face database to evaluate the effectiveness of the proposed MFPDA. Compared with some popular subspace learning methods, such as PCA, LDA, LLP, UNDFLA, JSPCA, the proposed MFPDA has highest average recognition accuracy. The experimental results confirm the effectiveness of the proposed algorithm.
Unified Approach to Solving the Trajectory Problem by Usage of Neural Maps

Abstract In modern robotics, there are many approaches to planning the trajectory of the portal manipulator. Methods based on the use of graphs, methods of potential fields, with the use of cellular decomposition, intelligent and others are used. Many varieties are based on similar mathematical apparatus. An important aspect of the trajectory search method applicability is convenience of chosen workspace and obstacles representation method. The approach proposed for manipulator path modelling implies a multi-level process, which includes periodic calculation and updating of invalid configurations set, considering the held cargo dimensions, a neural map creation and activation by the wave front propagation method etc. During experimental simulations with different activation functions, numbers of artificial neurons and synaptic connection implementation types, few methods of neural map activation have been considered, including flat and/or three-dimensional raster methods and wave propagation with static obstacles.
Estimating Chemotherapy Side Effect in Histopathologically Diagnosed Patients Over 65 With Metastatic Breast Cancer Using Deep Learning

Breast cancer has become one of the increasing social health problems. Breast cancer cases are increasing every year in Turkey, one in 8 women develops breast cancer and every year, 25,000 new breast cancer cases are diagnosed. Increased obesity and menopausal age, decreased fertility, environmental and genetic factors also increase the rate of breast cancer. Diagnosis and treatment of breast cancer is of great importance.Deep learning is a part of machine learning that began to be used in the early 2010s. It is a method that provides more effective inferences by using machine learning methods with the help of multiple layers. With deep learning, very realistic results were obtained in estimations based on historical data. As in many other fields, deep learning methods have been used in the field of medicine.In this study, it is aimed to make an analysis study to predict side effects in patients who are given chemotherapy by using deep learning methods in breast cancer patients diagnosed over 65 years. As a result of this study, it was aimed to give an idea to physicians whether or not to apply chemotherapy to breast cancer patients over 65 years. In this study, a two-class problem, positive and negative, was discussed and as a result 74% accuracy was obtained. This is the first study on this subject, there is no previous study on whether breast cancer should be given with deep learning in the elderly with breast cancer.
Experimental Study of Impermeability Boundary in the Earth Crust

Results of study of supposed boundary of impenetrability in the crystalline earth crust are discussed in the article. The Murman-2018 experiment on distance DC sounding in combination with frequency and audio magnetotelluric sounding was undertaken to solve the problem. Methods of field work and data processing are presented in the first part of the work. The second part of the work is devoted to the interpretation method description and to presentation of results obtained. Impenetrability boundary is detected in the shape of the sharp resistivity increase from 104 to 106 Ω · m at a depth of 10–15 km. That boundary is accepted as transition zone between the brittle and ductile states of the earth crust substance (Brittle-Ductile Transition zone, BDT). To obtain the more reliable information on parameters of the impenetrability boundary it is necessary to conduct additional DC studies using remote sensing at distances up to 1000 km between transmitter and receiver.
Markov Chain Monte Carlo Extensions to Gaussian Processes Approach on Orbit Predictions

The previously proposed machine learning (ML) approach can improve the satellite orbit prediction accuracy via learning from historical data. This study attempts to extend the ML approach from a new perspective. Specifically, Gaussian Processes (GPs) with the Markov chain Monte Carlo (MCMC) sampling technique are used. We compared several different combinations of GPs, whether it is a full or sparse GP, inferred using maximum a posterior (MAP) algorithm or MCMC integration. We observed that MCMC can provide better performance than MAP in most cases, but it also requires more computational time. Other improvements to GPs and MCMC will be studied in the near future.