Reviewerly/exHarmony huggingface dataset:
Local Path Planning of Unmanned Underwater Vehicle Based on Improved APF and Rolling Window Method

Aiming at the unknown complex underwater environment in the research of local path planning of unmanned underwater vehicle (UUV), a fusion algorithm of improved artificial potential field method and rolling window method is designed. Due to the limited detection range of UUV sensors, the rolling window method is used to model the local environment detected by UUV sensors. On this foundation, combined with the UUV motion constraint, the sub target points are set in the window, and the artificial potential field method is used for local path planning in the window. To solve the local minimum point problem of the traditional artificial potential field method, the position factor is introduced to the repulsion function of the traditional artificial potential field method. At the same time, UUV at the local minimum point is forced to turn and jump out of the local minimum point. Finally, the simulation results are given to show that the algorithm can bypass the local minimum point and realize real-time path planning successfully.
Forecasting of photovoltaic power generation using deep learning AI

The generation of electricity with photovoltaic (PV) panels is one of the alternatives that is currently the most expanding among renewable energy sources. In this scenario, the prediction of PV generation is essential for the safe and reliable operation of energy distribution systems, especially when using smart grids. The present study aims to predict the energy generation of a PV plant through the use of artificial intelligence (AI) with the most up-to-date knowledge in this area. For this, an artificial neural network (ANN) with a Long Short Term Memory layer and ReLU activation function was implemented in Matlab software. This ANN configuration, also known as deep learning (DL), uses an information bank containing data on energy generated at daily intervals. The methodology used in this approach brings a broad update of this theme and made it possible to define the parameters for better functioning of the DL. Finally, it can be concluded that DL results in a more accurate prediction when using a larger database in the network training phase. Thus, this work contributes to better planning of PV generation and to the estimation of energy generation using AI.
Intrusion Detection System for IoMT through Blockchain-based Federated Learning

Federated Learning (FL) is a feasible technology to collaboratively train a model without sharing private data. This approach differs from traditional machine learning techniques, which aggregate local datasets in a single server. Thus, FL is adopted in the Healthcare sector to preserve the privacy of collected sensitive medical data from heterogenous and resource-constrained Internet of Medical Things (IoMT). However, FL requires aggregating all trained local models in a central server that presents a single point of failure. To address this issue, we propose a novel Blockchain-based Federated Learning architecture, which is applied to detect malicious network traffic in IoMT environments. In this paper, the proposed architecture takes advantage of a Hyperledger Fabric channel coupled with FL to manage efficiently and securely the learning process of an Intrusion Detection System (IDS). The Blockchain channel replaces the commonly used central server with the traditional FL approach. Moreover, the proposed approach benefits from the inherent features of Blockchain and FL. Besides, it secures patient data collection from the IoMT by examining the network traffic for unauthorised behaviour or policy breaches.
Development and Implementation Experience of Regional Human Resources Capacity Management System: Subsystem of the Educational Organization

The functional of the regional human resources capacity management system, which is currently in continuous development, is considered. Changes in the main menu of the system are described. The functions of the personal account of the educational organization are considered in detail. The description is illustrated with screenshots of the system. The content of the main functions of the office of the educational institution is briefly described.
An Enhanced Cloud-Native Deep Learning Pipeline for Network Traffic Classification

Cloud-native computing approaches can be used with deep learning techniques to offer enhanced solutions to real-world problems. Given that much reliance on modern technology has emerged as a substitute to face to face interaction, deep learning techniques can be very effective in supporting modern services such as the classification of network traffic. Such classification can have many purposes like quality-of-service provisioning, security, and the detection of unauthorized traffic. Deep learning, however, requires lots of extensive experimentation and plenty of data that are labeled. In this paper, we propose an enhanced cloud-native deep learning pipeline to mitigate these problems and use network traffic classification as a use case. We use a cloud-native serverless approach for data pre-processing, model building (hyperparameter tuning), and model serving. Our approach also supports fully and partially labeled data scenarios. We were able to achieve speedup and scalability values that are near to theoretically calculated ones. Furthermore, in a limited time budget, our approach reached better accuracy compared to existing work. Our paper defines the problem, surveys background work that attempt to use various approaches, presents the proposed approach, and finally describes the experiments and associated results.
Application of Decisions' Roots for Data Analysis on Example of Dataset with Magnetic Susceptibility Values of the Brain Veins and the Alzheimer's Disease

In this paper, an attempt was made to approximate medical data and establish the relationship between the magnetic susceptibility values of the cerebral veins and the Alzheimer's disease in the form of integrated rating mechanisms, or as they are also called recently “decisions' roots”. Quantitative susceptibility mapping indicators are used as medical data. These data allowed us to obtain the value of magnetic susceptibility and provides information on oxygen metabolism. This information is important in assessing neuronal metabolism. Significant factors (the values of the magnetic susceptibility of the cerebral veins) were determined using the methods of system-cognitive analysis. These factors can affect the cognitive state of the patient. Discrete function was constructed using the decision roots identification algorithm. This function described with high reliability the initial set of research data (values of the magnetic susceptibility of the cerebral veins) on the condition of patients for the presence of Alzheimer's disease.
The Role of Synthetic Data in Improving Neural Network Algorithms

This review article describes synthetic data, its applications, and examples of improving neural network algorithms with synthetic data. Using these examples, we show the important role of synthetic data in the improvement of neural network algorithms and the development of artificial intelligence.
Research on Cartoon Face Generation Based on CycleGAN Assisted with Facial Landmarks

Turn real faces into cartoon faces is a topic of style transfer, and style transfer is a hot topic in the application of generative adversarial networks in image. CycleGAN is one of generative adversarial networks. It has obvious universal applicability, and has a good transformation effect on various types of style transfer. But to the facial style transfer, it only focuses on the transformation of the whole face, and it is not ideal for the transformation of the details of the facial features. How can this situation be improved? In this paper, we use facial landmarks to assist the transformation of facial features. In the beginning, we use stacked hourglass networks to detection and capture landmarks of real faces. And then, use them to assist cartoon faces generation. In view of the fact that the hourglass network has its own advantages in feature extraction, we use it to replace the generator structure of the original CycleGAN for transformation. And in order to avoid the Checkerboard Artifacts and ensure the quality of image generation, we use bilinear interpolation in the upsampling part of the generator to replace the deconvolution of the original generator and the nearest interpolation of the hourglass network. Experiments show that these practices have good results in optimizing conversion performance and improving image quality.
Challenges on prioritizing software patching

The prioritization of software security patches is an important demand of all modern institutions due to the amount of vulnerabilities reported daily and the complexity required to solve them. The state of the art patch prioritization mechanisms focus exclusively on the severity of the reported vulnerabilities. They do not take important factors, such as the likelihood of a vulnerability being exploited, into consideration. In this paper we present an open-source software that uses machine learning techniques to prioritize the application of software security updates based on several factors. Our solution can estimate the probability of exploitation of public vulnerabilities, to complement the risk analysis. This is accomplished using interconnected artificial neural networks optimized by genetic algorithms. Our model was trained with a large amount of data that we have collected from public web sources. We also make this database publicly available to be used in other projects. We ran experiments composed of 20 evaluations, from 2000 to 2020. Our results present a higher F1-Score than all strategies based on CVSS+, proving that the usual procedure of focusing on the severity of vulnerabilities is not the best strategy to guide the prioritization of security patches.
Comparison of Learning Hierarchical Dynamic Neuro-neighborhood Models Based on Perceptron and Radial-basic Functions

This article will describe hierarchical dynamic neuro-neighborhood models. A review of previous studies in the field of hierarchical dynamic neuro-neighborhood modeling is carried out. The idea of hierarchical dynamic neuro-neighborhood models is the possibility of representing complex systems and their subprocesses using a neighborhood model with neural networks nested in its nodes. An algorithm for identifying a model of the «input-state-output» type is presented. The training of hierarchical dynamic neuro-neighborhood models based on a perceptron and a network of radial basis functions was compared.
Missing Data Imputation Using Fuzzy System

The article presents a new approach to missing data imputation. The authors suggest using the developed fuzzy system in order to impute missing values in sensor data. The new fuzzy system is generated using ellipsoidal clustering. After obtaining clusters of an ellipsoidal shape each cluster is projected on coordinate axis and matched a fuzzy rule. In order to decrease the size of ellipsoids and increase accuracy of the system Khachiyan's algorithm is applied.
User Story Risk Prioritization Model for Agile Software Development

User stories capture software requirements for an agile software project. One of the problems that cause project failure is that the project does not consider risks that result from low-quality user stories from the beginning and hence does not prepare for such risks. That causes an impact on both software development and project management. This paper presents a user story risk prioritization model to raise the project awareness on user story risks and risk prioritization for better responses to the risks. The model describes 10 user story risks and 2 main criteria for risk prioritization, i.e. impact of occurrence that consists of 10 subcriteria and probability of occurrence that consists of 1 subcriterion. The model has been evaluated by members of agile teams who have experiences in agile software development. To prioritize user story risks, the model can be used with other methods, such as the Analytic Hierarchy Process (AHP) or weighted scoring method, to rank the user story risks by the criteria and determine priorities of the risks. In this research, the AHP has been applied with the proposed model to prioritize user story risks of an agile software project. The experiment shows that the risk prioritization results from the model are consistent with the actual risk situation of the experimental project. The proposed model therefore can be used as a supporting tool for risk management in an agile software project.
Blockchain-based Light-weight Authentication Approach for a Multiple Wireless Sensor Network

The Internet of Things (IoT) uses Wireless Sensor Networks (WSNs) to gather data from particular locations. Environmental monitoring, Army, agronomic, disaster risk management, and tracking systems are just a few of the industries that heavily rely on the WSN of the IoT. Wireless Sensor Network (WSN) is the massive number of sensor nodes dispatched to track a large area, where the control strategies are frequently challenging. In order to prepare these networks for the market, sufficient protection requirements for medium- and large-scale WSNs are a challenging and important target to achieve. To ensure its security, node identity authentication is a significant one. So, we proposed a public blockchain-based multi-wireless sensor network verification method through a Light-Weight Authentication Algorithm (LWAA) for IoT which is used to enhance the secure authentication and performance in WSN in a multi-Wireless Sensor Network model. Here, the nodes are separated between access points, group head nodes, and regular nodes indicated by their power variations that are created into a hierarchical model. The common authentication of nodes in a number of communication scenarios is identified through blockchain. Our cryptography technique enhances the network life span and decreases the computation time efficiently. The evaluated performance in total power consumption, throughput, latency, packet delivery ratio, time computation, and also proved better results in comparison with the existing authentication methods such as ECDSA, EIBAS, ECBR, and AES.
FIMF score‐CAM: Fast score‐CAM based on local multi‐feature integration for visual interpretation of CNNS

The interpretability of the model is a hot issue in the field of computer vision. Score-CAM is a kind of interpretable CAM method with good discrimination and gradient free calculation. It is a representative work in this field. However, it has the disadvantages of long calculation time and incomplete heatmap coverage. Therefore, this paper proposes an improved Score-CAM method named FIMF Score-CAM, which can fast integrate multiple features. Its contribution is reflected in two aspects: The weight of the feature map is directly calculated by using the feature template. Unlike Score-CAM, this model greatly saves computation time because it only requires one convolutional calculation. Another contribution is that the feature map used to generate the heatmap integrates various semantic features of the local space, so that the heatmap of the object of interest can be generated with more complete coverage and better interpretation. The FIMF Score-CAM is superior to the existing mainstream models in interpreting the visual performance and fairness indicators of the decision-making, having more complete explanations of object classes and the advantage of faster calculation speed.
Snapshot on-axis Fizeau polarization phase-shifting interferometer

We propose and experimentally demonstrate a highly stable on-axis near common-path snapshot Fizeau-polarization phase-shifting interferometer by combining the polarization phase-shifting scheme with Fizeau on-axis interferometry. The compact on-axis design of the Fizeau interferometry is achieved by utilizing the characteristic feature of a wire grid polarizer, which generates the orthogonal polarization components required for the proposed polarization phase-shifting scheme. The quantitative phase measurement capability is experimentally validated by estimating the phase profile of various phase-sensitive standard and unknown phase samples.
Impact of common fabrication errors on the performance of diffractive neural networks

We numerically investigate the performance of optical implementations of deep neural network for complex field data processing in the form of multi-layer nanoscale diffractive neural networks, trained to perform image classification tasks. We discuss the parameter optimization and the limitations that fabrication errors put on the performance of such direct phase retrieval systems. The diffractive neural networks studied here may have transformative impact on adaptive optics, data processing and sensing and may be crucial in the development of robust and generalized quantitative phase imaging methods.
Characterization of Quantum Grade Interconnects

Abstract Quantum grade interconnects, which includes quantum grade optical fibre links and Quantum Photonic Integrated Circuits (QPICs), are rapidly developing components central to the successful development of the Quantum Internet and Quantum Key Distribution (QKD). These interconnects will present new metrological challenges that need to be addressed to ensure key support of future scientific and industrial development. Potential metrological solutions evolving alongside associated standardisation will ensure success and reliability of this advanced technology and provide key support to future scientific research and industrial manufacturing.
Social and Economic Maxims of ICT in Education

ICT in education, in terms of terminology and structural composition, may carry out three basic components, namely education, information, and communication technology. ICT in education has its sole concern of identifying the most suitable and appropriate use of developed technology (both hardware and software) for serving the educational needs of society at a particular time and place. Education using ICT tools also aids in expanding educational opportunities to socially vulnerable section of people such as low-income groups, women, aged, and differently abled. This effectiveness of ICT in education depends on the presence or absence of the conditions and elements, facilitating or obstructing the proper flow of information and technological methods. Proceeding from simple to complex always provides an appropriate learning sequence. Hence, a wise teacher cares for a beautiful integration of psychological principles to logical organisation of sequence of activities in his teachings for social and economic growth of the community altogether.
SACNet: Shuffling atrous convolutional U‐Net for medical image segmentation

Medical images exhibit multi-granularity and high obscurity along boundaries. As representative work, the U-Net and its variants exhibit two shortcomings on medical image segmentation: (a) they expand the range of reception fields by applying addition or concatenate operators to features with different reception fields, which disrupts the distribution of the essential feature of objects; (b) they utilize the downsampling or atrous convolution to characterize multi-granular features of objects, which can obtain a large range of reception fields but leads to blur boundaries of objects. A Shuffling Atrous Convolutional U-Net (SACNet) for circumventing those issues is proposed. The significant component of SACNet is the Shuffling Atrous Convolution (SAC) module, which fuses different atrous convolutional layers together by using a shuffle concatenate operation, so that the features from the same channel (which correspond to the same attribute of objects) are merged together. Besides the SAC modules, SACNet utilizes an EP module during the fine and medium levels to enhance the boundaries of objects, and utilizes a Transformer module during the coarse level to capture an overall correlation of pixels. Experiments on three medical image segmentation tasks: abdominal organ, cardiac, and skin lesion segmentation demonstrate that, SACNet outperforms several state-of-the-art methods and facilitates easy transplant to other semantic segmentation tasks.
Data preservation practices for enhancing agricultural research data usage among agricultural researchers in Tanzania

The objective of this study was to investigate the role of research data preservation for enhanced data usage among agricultural researchers in Tanzania. Specifically, the study aimed to examine the data preservation methods used by agriculture researchers, find out how long agriculture researchers preserve their agriculture research data, and determine factors that influence agriculture researchers on their choice of data preservation methods for use. The study employed a cross-sectional research design. The study employed both qualitative and quantitative approaches. A survey was conducted to collect data in 11 research institutions. A simple random sampling technique was used to select 204 respondents from the study area while purposive sampling techniques were used to select 11 agriculture research institutions including 10 Tanzanian Agricultural Research Institution (TARI) centers, and Sokoine University of Agriculture (SUA). Also, 12 respondents were selected purposively for an in-depth interview as key informants. The study adopted Data Curation Centre (DCC) Lifecycle Model to explain data preservation process. Findings indicated that a majority of more than 90% of researchers preferred to preserve their data using different storage devices such as field notebooks, computers, and institutional libraries. Moreover, findings indicated that about 74% of agricultural researchers preferred to preserve their data for more than 6 years after the end of the project. Findings also indicated factors that influence researchers in the choice of data preservation methods to be easy to reach, cost-effective storage devices, support to use the devices, adequate infrastructure for data preservation, and reliable power supply. It can be concluded that there is yet a great role of research data preservation in enhancing data usage among researchers in Tanzania. It is recommended that the government should establish an agricultural research data bank to guarantee permanent availability of data at all times when needed.
Matching by pixel distribution comparison: Multisource image template matching

Abstract Efficient and accurate template matching in multisource images is a difficult task when hardware platform resources are limited. Motivated by the problems encountered in present methods, a template matching scheme, called Matching by Pixel Distribution Comparison (MPDC), is proposed to reduce the use of system resources by combining traditional algorithms and neural networks. The MPDC scheme first extracts the distribution information of image pixels through the developed extended Slice Transform (eSLT) matrix to overcome the non-linear intensity difference between heterogeneous images. Then the similarity between heterogeneous images is evaluated by comparing the pixel distribution rules reflected by the eSLT matrix. A similarity score table is constructed based on the pixel distribution comparison, and the similarity score between heterogeneous image pairs can be calculated by querying this table. The comparison of eSLT matrices can be completed through either experience scoring or neural network learning. The experimental results show that, on a hardware platform with limited resources, the 64 × 64 template can be matched by sliding on the 256 × 256 query image in 2.85 s, and it only takes at least 19.84 s for neural network training on the CPU. The matching performance is also better than many popular multisource image algorithms.