Reviewerly/exHarmony huggingface dataset:
General Algorithm for Cognitive Material Measurements by the Radio Structuroscopy Method

In this work, a new general approach to the measurement of material parameters is proposed. The basis of this approach is the joint use of a computer model, a discriminative model, and a Bayesian network to organize the process of restoring material parameters. The purpose of the measuring system is to provide relevant information about the parameters of the material under investigation. We have successfully attempted to use the concept of self-awareness in a system for the automated determination of complex medium properties in real time. This method combines the advantages of both direct experimental research and numerical reconstruction in a virtual model. The work describes in detail the functioning algorithm the support machine and the structure of the Bayesian network. These two elements are crucial to the proposed measuring complex.
Novel Four-stage Comprehensive Analysis Approach for Population-based Optimization Algorithms

This paper presents a novel four-stage comprehensive analysis approach of population-based optimization algorithms in terms of their performance and versatility of application. The first stage involves the selection of algorithms that do not possess the center-bias operator, i.e., those that do not tend to converge to the center of the search area. In the second and third stages, the accuracy and speed of the selected algorithms are computed, respectively. In the fourth stage, a multi-objective optimization problem is solved, resulting in a list of the most efficient algorithms. The main distinctive feature of this approach is the application at the first stage of the procedure for selecting algorithms that do not possess the center-bias operator, which allows to narrow down the list of algorithms analyzed at the next stages, excluding the obviously defective ones. The proposed approach was used to investigate the capabilities of 10 most popular population-based optimization algorithms in order to select the best of them in terms of performance efficiency and versatility of application. The obtained results can be applied to justify the choice of a particular optimization algorithm for solving a particular applied problem.
On Factors Selection in CatBoost Models Construction

When solving Machine Learning problems, the construction of models capable of capturing the variability of factors in the training data and then recognizing it when making a prediction is of particular importance. Ensemble models provide such an opportunity. One class of the mentioned models is bousting models, in particular, the CatBoost model. This paper presents an approach to identifying influential inputs of the CatBoost model, provided that factor modifications such as, for example, exponent or logarithm of factors are used. The approach removes the problem of multicollinearity caused by the presence of different representations of the same factor. A numerical example shows the effectiveness of the proposed approach.
Life Cycle and Tools for Designing and Implementing a Typical Scenario for the Operation of an Automated System

The concept of the life cycle will be topical in the foreseeable future. The main reason for this forecast is the increase of the complexity level of software and hardware complexes of automated systems. As a rule, the life cycle concept is applied to all subsystems which are involved in the information support, software and hardware. This approach covers almost all elements of automated systems. The fact leads to the high level of complexity of CALS systems. In addition to the existing approaches and models the paper proposes to apply the ideas of the life cycle support to the scenario of the automated system operating. It is obvious, that the success of the hardware and software complex mainly depends on the possibility of creating a scenario for solving problems in the subject area which is supported by automated system.
Decentralized and Incentivized Voting System with Web3 Technology Ensuring Anonymity and Preventing Double Voting

Due to the impact of the pandemic and globalization, many governments worldwide have begun to replace paper voting with electronic voting systems. This is primarily intended to increase voting efficiency and reduce personnel costs. However, electronic voting is usually centralized, which creates difficulties in ensuring the integrity of the voting process and protecting voter privacy. As a solution, we propose an efficient and anonymous web3-based voting system that provides incentives. The use of blockchain technology ensures that voting results are stored in a decentralized manner, reducing the possibility of tampering and ensuring vote integrity. To protect voter privacy, our system incorporates stealth addresses to conceal ballot content and lightweight ring signatures to verify voter validity without the risk of double voting. Furthermore, our voting system utilizes smart contracts to encourage voting and self-tallying, resulting in improved transparency. Experimental results demonstrate that our scheme outperforms several existing e-voting systems, thus providing a robust and secure alternative for governments seeking to improve their voting systems.
Empirical evidence on the relationship between migrants' remittances and private investment in Burkina Faso: A dynamic simultaneous equation model

Abstract This research explores the channels through which migrant remittances can promote private investment. The research method favours a dynamic simultaneous equation model estimated using a combination of the bootstrap method and the three‐stage least squares estimator to account for the interdependence between remittances and private investment. The study area is Burkina Faso, and the period of analysis covers the period 1985–2021. One of the main findings is that the level of corruption risk and the demographic dependency ratio are important transmission channels. Findings suggest creating an institutional framework that enhances the level of transparency and accountability and accelerates the demographic transition process.
Co-Design of SIMON Lightweight Block Cipher Algorithm on an FPGA

This article presents a new datapath architecture for the SIMON 64/128 lightweight cryptography algorithm in IP design, with a specific focus on its applicability to IoT and RFID systems. The increasing prevalence of these technologies has created new security challenges, and cryptography is an essential tool for ensuring the confidentiality and integrity of transmitted data. The SIMON algorithm has been specifically designed for use in resource-constrained environments and is well-suited for IoT and RFID applications due to its low power consumption and small area. In this paper, we describe the hardware implementation of the new module SIMON 64/128 algorithm on a Field Programmable Gate Array (FPGA) using hardware description language VHDL and The C language is utilized for programming the software that runs on the ARM processor to control the system for the IP design. We also detail the testing and verification process to ensure that the hardware is fully functional. This work demonstrates the feasibility of implementing the proposed module of SIMON algorithm on FPGA and highlights its potential for improving the security of IoT and RFID systems. Due to their good performance achieved after the implementation hardware and software of the module and IP design compared to other algorithms.
A New Chaotic Symetric Key Stream Cipher for Image Security in the Internet of Things

This paper proposes a new chaotic symmetric key stream cipher specifically designed for securing images in the Internet of Things (IoT) environment. The cipher utilizes chaos theory to generate a secure and unpredictable key stream, ensuring image confidentiality and integrity. It is based on W7 cipher and 3D-Chebyshev chaos map. It operates in a stream cipher mode, encrypting each pixel individually, and incorporates synchronization mechanisms for error-free decryption. A hardware design of the proposed Cipher is elaborated by using VHDL description language and implemented on Xilinx PYNQ-Z2 FPGA device. Experimental results demonstrate its resistance to attacks and efficient performance, making it suitable for real-time image transmission in resource-constrained IoT applications.
Application of Face Forgery Detection in Civil Aviation

Aiming at the challenges related to face forgery in civil aviation, we investigated urgent issues and the requirements for face forgery detection. The application scenarios were described based on the requirements, accompanied by a literature review and application methodsˈ including face adversarial attacks in physical domain, the face liveness detection method based on rPPG signals, as well as the system testing.
Enhancement of Energy Yield for Photovoltaic System by the Installation Above a Water Body

The presented paper illustrates a study about energy increasing of photovoltaic (PV) system with a water body structure. The presented system is based on a new structure of PV cells with imitate installation above a dam. Also, imitate solar and water level tracker system is used to make a movable system with the amount of rain in the dam and with the seasons of the year. The purpose of this mixture is to increase the performance of the used PV system and to conserve with an adorable percentage the water resources that exist in the dam. The simulation results confirm the increase of the obtained energy with a percentage of 6% in summer season and also the rise of the conservation of the water resources against evaporation in the dam with annual percentage of almost 3%.
Micro-expression Recognition Based on Residual Network

Due to the small movement amplitude of micro-expressions, the paper views micro-expression recognition as an image classification task and aims to identify micro-expressions by classifying images. Firstly, micro-expression videos are extracted into frames, each frame representing a standalone image. Secondly, each micro-expression image is fed into a neural network for feature learning and classifier training. Finally, the model is deployed for inference and recognition. The advantage of this method is that it allows the model to focus more on the location information of each micro-expression relative to the overall facial area, distinguish the shape of each AU on the human face, and identify the corresponding micro-expression category based on different AU combinations. As a result, micro-expression recognition is achieved. The test set accuracy of the model with pre-trained parameters is 3.48% higher than that of the model without pre-trained parameters, indicating that the transfer learning method used in this method can help reduce training time and improve model accuracy.
Multi-Type Anomaly Detection Using One Deep Neural Model for QAR Data

QAR data serves as a critical data foundation for flight quality assessment, accident investigation and maintenance management. However, raw QAR data contain various types of anomalies caused by sensor faults or abnormal flight operations. Therefore, anomaly detection from QAR data plays important roles in mining the cause of abnormality and providing a guarantee for flight safety. Although, there are several related works focusing on anomaly detection for sensor data of different industries, they cannot get the same high performance on different type of anomalies. Further more, due to the peculiar features of QAR data, the existing methods cannot performs as well as on common sensor data. Therefore, there is a need to propose a generic anomaly detection algorithm for QAR data to detect different type of anomalies efficiently in one time. In this work, we propose a general anomaly detection method, which can effectively capture complex spatial-temporal information in QAR data. The proposed method can be applied to detect multiple types of anomalies using one deep neural model. Experiments are conducted on different types of anomaly datasets. The experimental results show that the proposed new method can effectively detect multiple types of anomalies in QAR data with higher performance compared with existing methods.
Cross-modal Augmentation: A Data Augmentation Scheme for RGB-Thermal Semantic Segmentation

RGB-Thermal semantic segmentation is crucial for applications like robotic inception, autonomous driving, and video surveillance. However, small and imbalanced datasets in this field create training bias. To address this, we propose a new data augmentation scheme. We mix up the cross-modal information to smooth the decision boundaries from class to class, modal to modal using CutMix and copy-paste. Using the two method, RGB data and Thermal data are exchanged. Applying the RandAugment method to augment RGB and Thermal images, we select 2 transformations from a pool of 15, including a new transformation tailored for RGB-Thermal datasets. This innovative transformation mimics modal failure by fading a randomly selected region in either RGB or thermal images. Our experiments confirm the effectiveness of the proposed augmentation scheme, leading to a observable performance boost. Specifically, we observe improvements of 4.7% in mAcc and 6.1% in mIoU compared to the baseline.
Influences of Internal Control on Enterprise Performance: Does an Information System Make a Difference?

It is generally perceived that the effective implementation of an adequate internal control system prevents and controls an entity’s risks and improves its procedures and performance. This study empirically investigates the relationship between the internal control system and firms’ performance, with particular emphasis on the moderation role of an integrated information system. For this purpose, a survey was developed and sent to 215 Saudi firms that had implemented an integrated information system. A hundred and two valid responses were received. Partial least squares structural equation modeling was utilized for the data analysis and hypothesis testing. The findings confirmed that organizational structure, prospectors’ strategy, information system quality, and management support significantly influence the internal control system for the study sample. The finding also supports the role of an information system as a moderator variable in the relationship between internal control and organizational performance. Additionally, the study elucidates the importance of information system maturity for information system quality.
An Analysis of the Features of Requirements Engineering Tools

Many requirements engineering tools have been developed for gathering, documenting, and tracing requirements that can even be further processed for such purposes as analysis and transformation. In this study, we analysed 56 different requirements engineering tools for a comprehensive set of features that are categorised into multiple viewpoints (i.e., project management, specification, collaboration, customisation, interoperability, methodology, and user-support). The analysis results led to many interesting findings. Some of them are as follows: (i) the project planning and execution activities are rarely supported, (ii) multi-user access and versioning are highly supported, (iii) the most popular specification technique is natural languages, while precise specification via modeling languages is rarely supported, (iv) requirements analysis is rarely supported, (v) requirements transformation is considered for generating documents only, (vi) tool customisation via the tool integration and API support is highly popular, while customising the notation set is rarely supported, (vii) exchanging requirements is popular in such standards as ReqIF and Excel/CSV, while no single standard is accepted by all the tools, (viii) agile development is very common, while other methodologies (e.g., MDE and SPLE) are rarely supported, and (ix) user-guides, telephone, e-mail, and videos are the most preferred methods for user-support. The analysis results will be useful for different stakeholders including practitioners, tool vendors, and researchers.
A Truthful Reverse Auction Mechanism for Federated Learning Utility Maximization Resource Allocation in Edge–Cloud Collaboration

Federated learning is a promising technique in cloud computing and edge computing environments, and designing a reasonable resource allocation scheme for federated learning is particularly important. In this paper, we propose an auction mechanism for federated learning resource allocation in the edge–cloud collaborative environment, which can motivate data owners to participate in federated learning and effectively utilize the resources and computing power of edge servers, thereby reducing the pressure on cloud services. Specifically, we formulate the federated learning platform data value maximization problem as an integer programming model with multiple constraints, develop a resource allocation algorithm based on the monotone submodular value function, devise a payment algorithm based on critical price theory and demonstrate that the mechanism satisfies truthfulness and individual rationality.
Improving Table Tennis Training and Technique Analysis: Accurate Classification of Actions with Informer Encoder

This paper introduces a binary classification network that utilizes the Informer Encoder to classify ping pong actions as either correct or incorrect. The dataset used in this study comprises 949 action videos capturing two fundamental ping pong stroke actions performed by athletes, including both correct and incorrect actions. The average frame count for each action is 38.62. Temporal skeletal data is extracted from the videos using a 2D pose estimation model, and a fully connected layer is employed to perform binary classification on the temporal skeletal data. During training and testing, the extracted skeletal data is segmented into temporal sequences of 39 frames for training and evaluation. On the test set, the Informer Encoder-based model achieves 100% accuracy, while the MLP-based model reaches 94%.
Decision Tree Algorithm and Deep Learning Technology Based Motion Decision Algorithm for Autonomous Robots

The current motion decision of autonomous robots is characterised by rigid motion commands, which leads to unsatisfactory motion decision results. To this end, an autonomous robot motion decision algorithm based on decision tree algorithm and deep learning technology is proposed. A robust combinatorial controller is set up for the autonomous robot. The motion control commands are modified based on the decision tree algorithm, and a disturbance estimation compensation term is added.Based on deep learning technology, autonomous robot motion decision-making is trained, obstacle avoidance correction is performed on motion control commands, and ultimately an autonomous robot motion decision-making algorithm is generated. Experiments show that the results of the algorithm has overlap with the optimal motion path by 96.49%, and the decision results are of high quality and have high practical application value.
An Information Extraction Method for Industrial and Commercial Rooftop Photovoltaics Based on GaoFen-7 Remote Sensing Images

With the increasing global focus on renewable energy, distributed rooftop photovoltaics (PVs) are gradually becoming an important form of energy generation. Effective monitoring of rooftop PV information can obtain their spatial distribution and installed capacity, which is the basis used by management departments to formulate regulatory policies. Due to the time-consuming and labor-intensive problems involved in manual monitoring, remote-sensing-based monitoring methods are getting more attention. Currently, remote-sensing-based distributed rooftop PV monitoring methods are mainly used as household rooftop PVs, and most of them use aerial or satellite images with a resolution higher than 0.3 m; there is no research on industrial and commercial rooftop PVs. This study focuses on the distributed industrial and commercial rooftop PV information extraction method based on the Gaofen-7 satellite with a resolution of 0.65 m. First, the distributed industrial and commercial rooftop PV dataset based on Gaofen-7 satellite and the optimized public PV datasets were constructed. Second, an advanced MANet model was proposed. Compared to MANet, the proposed model removed the downsample operation in the first stage of the encoder and added an auxiliary branch containing the Atrous Spatial Pyramid Pooling (ASPP) module in the decoder. Comparative experiments were conducted between the advanced MANet and state-of-the-art semantic segmentation models. In the Gaofen-7 satellite PV dataset, the Intersection over Union (IoU) of the advanced MANet in the test set was improved by 13.5%, 8.96%, 2.67%, 0.63%, and 0.75% over Deeplabv3+, U2net-lite, U2net-full, Unet, and MANet. In order to further verify the performance of the proposed model, experiments were conducted on optimized public PV datasets. The IoU was improved by 3.18%, 3.78%, 3.29%, 4.98%, and 0.42%, demonstrating that it outperformed the other models.
Enhanced E-Commerce Sales Analysis with Deep learning Approach

Nowadays, in e-commerce market, Consumer's purchase decision is dependent on many different factors and it is an uncertain process. Using computer vision techniques and e-commerce marketing data, deep learning and Artificial Intelligent can be introduced for E-commerce content detection. Using Computer vision techniques, many valuable features can be extracted out from the e-commerce images, such as HSV color features, face detection, item detection, text detection, and image quality analysis. The relationship between variables provided valuable data and the relation between e-commerce images and sales are correlated in the e-commerce market. Furthermore, deep learning method and regression techniques were also used to determine consumers' preferences, and used to analyze the influencing factors. In this work, an end-to-end system was designed to extract e-commerce image information into quantitative data in consumer models, and proposed a comprehensive deep learning models which can improve consumers' buying willingness.
A Binocular Vision Stereo Matching Algorithm for Depth Map Estimation

A new binocular stereo matching algorithm is proposed to solve the problem that the existing stereo matching algorithm is susceptible to noise and pixel mismatch and wrong matching in weak texture areas. First, set the search window and the neighborhood window, the neighborhood window slides inside the search window, compare the similarity between the neighborhood window and the Census transformation window, and determine the degree of influence of the center pixel of the neighborhood window relative to the center pixel of the transformation window according to the similarity, and assign the weight to process the gray value of the pixel. Secondly, the Sobel operator is introduced to calculate the gradient value of the image, and the texture level of the image is divided according to the gradient value. Three thresholds are set according to the texture level, and the parallax is aggregated with the cross-domain algorithm. Finally, the disparity map is optimized by the least squares fitting plane method. Experiments show that the average mismatch rate of the disparity map processed by the algorithm is 5.39% in the test of randomly selected images. Compared with other algorithms, it is more robust to noise, smoother in low-texture areas, has higher matching accuracy of disparity maps, and better optimization of disparity maps.