Reviewerly/exHarmony huggingface dataset:
BlockDL: Privacy-Preserving and Crowd-Sourced Deep Learning Through Blockchain

Deep learning has become a key technology on modeling large amounts of multi-sourced data. For privacy concerns, the data sharing among companies and organizations is increasingly difficult. In this paper, we present a crowd-sourced federated learning solution to train neural networks with a hybrid blockchain architecture. Smart contracts are used to share data authentications on the main chain, where the proxy re-encryption is for the privacy preserving. A consensus-based asynchronous practical byzantine federated learning (APBFL) algorithm is proposed on the side chains, to improve the model reliability and security. Experiments show that our solution is efficient, secure and robust.
Research on Behavior Mathematical Modeling of CGF Based on Reinforcement Learning under Complex Electromagnetic Environment

This paper focuses on the modeling of agent behavior in complex electromagnetic environment. By using reinforcement learning technology, The traditional Markov decision process and dynamic programming algorithm are improved. The electromagnetic field intensity function is integrated with Markov decision process and dynamic programming algorithm, and high precision virtual battlefield environment is evaluated based on system simulation, in order to facilitate the modeling of the agent’s behavior, this paper meshes the complex electromagnetic battlefield environment and sets up the simulation task scene of agent’s maneuvering task using simulation and evaluation system. Several simulation tests were implemented to collect relative test data. The comparison was made with the traditional Markov decision algorithm from three dimensions, including time, space and working condition of electronic devices inside equipment. The results show that the improved Markov decision algorithm and dynamic programming algorithm proposed in this paper show better adaptability to adapt complex electromagnetic environment than traditional Markov algorithm, it will provide a strong support for efficient completion of combat tasks and improvement of combat effectiveness in complex electromagnetic simulation environment, and also lays a foundation for the subsequent research on the CGF behavior modeling method.
Phishing schemes in the banking sector: Recommendations to Internet users on protection and development of regulatory tasks

The aim and objectives of the article are to analyze fraudulent phishing schemes and develop recommendations for Internet use and relevant regulatory tasks. The relevance of the article is due to the peculiarities of working in cyberspace with the emergence of new sources of banking risks, both for customers and organizations. The scientific novelty of the manuscript consists of a detailed analysis of phishing schemes, the development of recommendations and directions in relation to the Russian Federation. The object of the study is cyber fraud in the credit and financial sphere; the subject is social engineering and phishing schemes. The methodology of the paper includes a systematic analysis of the literature and sources on the research topic, general scientific methods (analysis, synthesis, deduction, analogy, classification), correlation analysis of data, graphical visualization of information. The authors consider the main methods of phishing and the most common techniques used by cybercriminals. Based on the critical analysis of the literature the authors determined a promising direction for the scientific and technical potential of Russia. A correlation analysis of the relationship between the number of cybercrimes and commercial banks is performed. The study offers recommendations to Internet users (how to recognize the signs of fraud), and to regulatory bodies on improving the system of supervision over the dissemination of information in cyberspace. The authors concluded that it is necessary to increase the level of cyber literacy and general literacy of the population, on the one hand, and to modernize the methods of supervision and control of the information posted on the Internet, on the other hand, to effectively counter financial and cybercrime. The research results can be used in the further development of remote banking services for the population to increase competitiveness in the banking services market. Prospects for further research on this topic lie in expanding its structure, developing the competencies of specialists in the field of remote banking technologies, as well as developing the scientific and technical potential of Russia.
A Blockchain Based Aircraft Tow Tractor Operating Planning and Scheduling Oversight System

Civil aviation aircraft tractors are used at high frequency, operated with much difficulty and in complicated operating environments, involving many civil aviation departments, and recent years saw operational accidents and incidents one after another. In this paper, basic operating manner and standard operating procedures of tractors are briefly introduced in the beginning, current status of research on accident prevention in tractor operating planning and scheduling at home and abroad is analyzed and main causes leading to incidents are summarized. Upon full analysis on unsafe factors and the defects of the existing tractor operation planing and scheduling, this paper proposes a blockchain based aircraft tow tractor operating planning and scheduling oversight system framework, which specific to regulatory requirements for the planning and scheduling safety, addresses the realization of four major functions, i.e., saving evidence of production data and regulations, the availability and invisibility of key production data, the identification of authentic hazards and the recording and tracing of risks. The framework helps civil aviation regulatory authorities promptly discover potential risks in production process and is expected to become a mean of support for the transition from post-event oversight to in-process regulation.
Transformation of the Program Strategy of a Niche Entertainment TV Channel under Conditions of a Competitive Media Environment (on the Example of STS and TNT)

Under conditions of a competitive media environment, linear TV channels should pay special attention to broadcast planning. This article presents the results of a study that was an attempt to analyze the changes that have occurred since 2012 in the programming of niche entertainment channels STS and TNT. Created in the second half of the 1990s, both TV networks remain popular among the target audience today; it seemed relevant to find out how their programming strategies were transformed taking into account the increased competition, including the one from non-linear services. The study was based on the method of quantitative content analysis with further comparison of the results obtained. In the course of the work, the genre and thematic characteristics of broadcasters’ content were determined, the origin of telecasts was established, the quality of films and series was assessed, and the programming tactics used in the formation of broadcast grids were analyzed. The study found that the changed media environment had a significant impact on the broadcast planning of both TV channels: over the years, both TNT and STS have become more active in using programming techniques aimed at retaining the audience and ensuring its natural flow between time slots, as well as taking a more careful approach to selecting content for showing. Taking into account the audience ratings of STS and TNT, we can talk about the success of such an approach to broadcast planning, which becomes more effective in combination with the broadcaster’s active presence on the Internet.
ExTree—Explainable Genetic Feature Coupling Tree Using Fuzzy Mapping for Dimensionality Reduction with Application to NACA 0012 Airfoils Self-Noise Data Set

This research presents an AI-based tool (ExTree) that provides high explainability in prediction problems that involve multiple continuous inputs. The algorithm uses an input coupling tree that gradually reduces the dimension of the system. The desired dimension reduction is achieved developing a network of fuzzy inference systems (FIS) wherein in each layer of the network, two inputs get combined to yield a single outcome. These outcomes are then submitted to the same procedure at the following layer until we arrive at a single output, thereby reducing the dimensionality of the problem in every step. Hence, large scale problems with more inputs require more layers. The final outcome is that we obtain a set of FIS nodes across the network, where each FIS may be characterized using an explainable control surface. The structure of the tree is optimized using a genetic algorithm that gets the best hierarchy of fuzzy features to minimize the dispersion of the final outcome. This tool has been benchmarked using NASA’s wind tunnel testing database of NACA 0012 Airfoils. The results, demonstrating accurate validation, are of value not only from the perspective of a high performing AI-based algorithm, but also because of the substantial amount of interpretability and traceability that the algorithm offers.
Cooperative Hypothesis Building for Computer Security Incident Response

We have been developing an AI-based system that automatically collects evidence from logs on computers within an intranet and infers the most plausible hypothesis of a cyber-attack. In this paper, we propose a user-interactive functionality which enables the system to incorporate knowledge and insight from the user in order to build the hypothesis. We show the system’s practicality with example use cases and also confirm the necessity of such functionality for the system by interviewing with cyber security analysts.Motoura, Shota Hoshino, Ayako Hosomi, Itaru
Streaming Detection and Classification Performance of a POWER9 Edge Supercomputer

We present training and inference benchmarks on a POWER9 edge supercomputer called SCOUT using two production-level artificial intelligence systems: the Video Processing Exploitation Framework (VPEF) and the Ordnance Threat Target Automated Recognition (OTTAR) system. For training benchmarks, we use Horovod to train ResNet-50 on synthetic data for a baseline benchmark, and then train Faster R-CNN on an available WASABI dataset using SCOUT’s six-way V100 GPU nodes. For inference benchmarks, we use GStreamer to stream both synthetic and real Motion Imagery (MI) and then use Single-Shot Detector trained on MobileNetV2 data with real and synthetic MI at four different resolutions (720p, 1080p, 1280p, and 2160p) while measuring average inference time. We also test reduced-precision inference performance using TensorRT and distributed inference performance using the Inference Benchmark "iBench" framework. We compare our results with equivalent work on x86_64 systems and provide suggestions on tuning for optimal performance. We find that V100s work well for training and offline inferencing in batches, while T4 GPUs outperform V100 GPUs only in very specific usage scenarios, such as streaming detection at reduced precision.
Construction of Key-dependent S-box for Secure Cloud Storage

As cloud storage systems have developed and been applied in complex environments, their data security has become more prevalent in recent years.The issue has been approached through many models.Data is encrypted and stored in these models.One of the most widely used encryption methods is the Advanced Encryption Standard (AES).In AES, the Substitution box(S-box) is playing a significant part in imparting the job of confusion.The security of the entire cryptosystem depends on its nonlinearity.In this work, a robust and secure S-box is constructed using a novel method, i.e., fingerprint features-based permutation function.Two stages are considered to construct a strong S-box.Firstly, random numbers are generated from the fingerprint features such as bifurcation and ridge endings of the user transmitting data.Subsequently, the permutation function is adapted on the random numbers (developed in the first stage) to augment the strength of the S-box.National Institute of Standards and Technology (NIST) STS 800-22 test suite is considered to evaluate the randomness of the enhanced fingerprint-based S-box.Also, the robustness of the constructed S-box is tested using cryptographical properties, namely Strict Avalanche Criterion (SAC), Nonlinearity (NL), Differential Approximation (DA) probability and output Bits Independence Criterion (BIC).Later, the cryptographical properties of the proposed S-box are compared with several existing S-boxes.After analyzing the characteristics of the proposed scheme, it is revealed that the newly constructed S-box is powerful, robust, and safe against linear and differential assaults.
Mahalanobis classifier and neural network algorithms for mineral exploration

In the literature, mineral explorations by remote sensing are restricted to conventional image processing techniques, which are embedded to available commercial software; for instance, ERDAS, PCI Geomatica, and the Environment for Visualizing Images (ENVI). Numerous remote-sensing geology works in a developed country is ill-defined. Since the beginning of remote-sensing mineralogy applications; there is no precise algorithm developing to state the quantity of detected mineralogists. This chapter aims at delivering new approaches for accurate automatic detection of mineral deposits in microwave remote-sensing data. The new approach is based on the unitization such Mahalanobis Classifier and Neural Network Algorithms as apart from the machine learning in image processing. Indeed, the machine learning is a keystone to construct automated systems, which can classify and recognize complex pattern spatial variations in such SAR data. It is not surprising that the demonstration of the SAR data as a coherent system cooperates as a vital role in determining what sorts of patterns can be automatically revealed. Finally, the results reveal that ANN distinguishes mineral deposits from encompassing completely different rock pattern options with a standard deviation of 0.12. Lastly, integration of various algorithms for mineral deposit detections provides higher ways in which of obtaining effective such mineral spatial detection. The ANN algorithmic rules is an acceptable algorithm for mineral deposits and gold potential zone detections, whereas the W2 mode is acceptable for the various rock and mineral deposit sort discriminations and detections exploitation RADARSAT-1 SAR data.
An Encryption Traffic Classification Method Based on ResNeXt

Encryption traffic classification technology classifies traffic data according to different applications or different traffic types. It is one of the most important technologies to monitor network traffic security and collect network traffic information. In view of this, this paper proposes an encrypted traffic classification method based on the ResNeXt network. Ethernet headers and payloads in the traffic are removed in data preprocessing, and then the improved and simplified ResNeXt model is used to identify encrypted traffic data. The preprocessing method can greatly reduce the size of input data, save time, and achieve higher accuracy. The experimental results show that the classification accuracy of the proposed method for 12 types of encrypted traffic in "ICSX VPN-NonVPN" data set is 98.58%, and the average accuracy rate, recall rate and F1 score are 98.70%, 98.49%, and 0.9859, respectively.
An Automated Detection and Segmentation of Thyroid Nodules using Res-UNet

Recently, some countries have been distressing with the increasing number of thyroid cancer cases. The number of cases is increased every year. Practically, one of the causes of the increase in the number of patients was due to manual examination. Recently, some researchers have involved in the development of CAD to solve this problem. However, CAD itself still has some limitations. One of the major limitations is that the nodules segmentation process was not well-conducted. Thus, to overcome that problem, we proposed a scheme for detecting and segmenting the thyroid nodules. Our scheme consisted of four major steps which were data augmentation process, normalization process, segmentation and evaluation process. The proposed scheme was tested in 480 thyroid ultrasound images. The proposed scheme successfully achieved more than 90% in all evaluation metrics in both detection and segmentation process. According to this achievement, we concluded that our proposed method had potential to be integrated as part of the intelligent system for detecting and segmenting thyroid cancer.
Motor Behavior Changes Are Predictive of Acute Events in Skilled Nursing

Abstract Background: Common acute medical conditions among older adults with dementia in skilled nursing include falls, delirium, and pneumonia. This study utilized a sensor technology to examine how motor behaviors may predict these acute events. Methods: Radio frequency identification (RFID) technology continuously measured time and distance travelled, gait speed, and continuous walking with little/no breaks (paths) across 3 long-term facilities for up to 1 year (N=51). Change point analysis estimates the probability of whether a sudden change occurred and provides the location of the change point (in days prior to the event) in a time series model. Results: Gait speed had very low probability to detect a change point across all events (22 falls, 10 delirium and 8 pneumonia). Sensitivity estimates ranged from 63% (number of paths) to 90% (distance travelled) for a fall; 37.5% (number of paths) to 100% (rest of the motor behaviors) for pneumonia. Except for gait speed, all other motor behaviors had high probability (100%) to detect a delirium change point. There was intra-individual variability in the location of the change points (mean of 10 days). Linear regression models for time and distance travelled using baseline predictors of age, ethnicity, gait and balance explained 89% and 90% of the variance in change point locations. Conclusions: Prior to an acute event there is a significant change in motor behavior, suggesting these are an early signal that may be used to prevent a fall or provide for the earlier recognition and treatment of delirium and pneumonia.
Time-Frequency Feature Enhancement of Radar Signal Using 2D Gaussian Filter

Millimetre-wave radar plays an increasing role in wireless sensing for hand gesture recognition. Despite its huge potential, several difficulties still exist, including the problem of the poor and unreliable feature of radar micro-Doppler signal due to the weak hand reflectivity and noisy environment. Therefore, enhancing the features will result in higher recognition accuracy. In this paper, we proposed a simple scheme to enhance the micro-Doppler feature. Specifically, the radar micro-Doppler signal is transformed to a time-frequency domain, and we reduce the effect of noise by simple spectral subtraction and squared magnitude. Finally, we apply a Gaussian filter to smooth spectra magnitudes, thereby enhancing the signature pattern from the background noise. Experimental results using a real hand gesture dataset show that the proposed scheme achieved a high classification accuracy of 97.5%.
Image Super-Resolution for Arthropod Identification

Super-resolution techniques have recently made great strides, especially in the context of deep learning. In spite of this, not much research has been conducted on the explicit application of these techniques to biodiversity related problems such as species identification. We took a state-of-the-art super-resolution model (enhanced deep super-resolution network, i.e. EDSR), and enhanced it further with perceptual and texture losses, and a test-time-augmentation solution. Furthermore, we designed a qualitative assessment framework and studied its relationship with automated performance metrics. Our results show that our proposed modifications to EDSR improve the recovery of details, and that current automated metrics (e.g. Peak Signal-to-Noise Ratio) are inadequate in the context of super-resolution for species identification.
Control of a transport robot in extreme weather based on computer vision systems

Abstract This article presents a new way to use special computer vision techniques to aim with assisting in controlling a transport robot in a dynamic environment under exceptional and difficult environmental conditions. An analysis and development of algorithm for obstacle detection in the robot’s environment proposed based on data from an RGB-D video camera using computer vision methods. Contour analysis was the base method to detecting objects featured fragment taking into account difficult vision conditions. Based on open-source library (Open CV), we adopted methods program implementation which confirmed its applicability to detect objects in mobile robot environment.
A multi-criteria analysis of sewer monitoring methods for locating pipe blockages and manhole overflows

This article is devoted to the aggregation of existing methods for monitoring sewerage systems into a single symbiosis, in particular methods for identifying the locations of clogged pipes and manhole overflows. Clogging of sewers is a frequent problem in large cities, entailing overfilling of manholes with sewage and disruption of the whole sewage system. Today, there are several methods for monitoring sewers: visual, acoustic and laser. Each method is represented by a wide range of devices with different characteristics and applications. The analysis identified the main technical and economic characteristics for each solution presented. Then, on the basis of the data obtained, a multi-criteria analysis was made according to several parameters: measurement accuracy, maximum diameter of the inspected pipe, type of pipe, cost. For the most objective selection, each parameter was given its own weight, and all parameters were normalized for their objective comparison. On this basis, all solutions were sorted by maximum values for each criterion, taking into account the selection by weights. As a result of the multicriteria analysis, five combinations of solutions were built, including several monitoring methods.
Cybersecurity Risk Assessment Based on Cognitive Attack Vector Modeling with CVSS Score

The modeling of scenarios of complex multistep targeted cyber attacks is considered. To determine all scenarios for the implementation of an attack, the FSTEC of Russia Methodology for assessing threats to information security and CAPEC attack patterns are used. Attack vector is presented in the form of an attack graph with further formalization in the form of a hierarchical fuzzy cognitive map for the possibility of multiple scale analysis. Automated modeling of a set of possible attacks allows extract information about infrastructure weaknesses, the most dangerous vulnerabilities and potential weaknesses of system components, identify the most successful attack scenarios and assess their consequences for the enterprise. The calculation of the quantitative assessment of the local relative risk is conducted based on the vulnerability severity levels, considering the CVSS environmental metrics for the industrial control system for the transport of commercial oil.