Reviewerly/exHarmony huggingface dataset:
A Method for Expanding the Training Set of White Blood Cell Images

In medicine, the count of different types of white blood cells can be used as the basis for diagnosing certain diseases or evaluating the treatment effects of diseases. The recognition and counting of white blood cells have important clinical significance. But the effect of recognition based on machine learning is affected by the size of the training set. At present, researchers mainly rely on image rotation and cropping to expand the dataset. These methods either add features to the white blood cell image or require manual intervention and are inefficient. In this paper, a method for expanding the training set of white blood cell images is proposed. After rotating the image at any angle, Canny is used to extract the edge of the black area caused by the rotation and then fill the black area to achieve the purpose of expanding the training set. The experimental results show that after using the method proposed in this paper to expand the training set to train the three models of ResNet, MobileNet, and ShuffleNet, and comparing the original dataset and the method trained by the simple rotated image expanded dataset, the recognition accuracy of the three models is obviously improved without manual intervention.
Construction of wisdom classroom for English majors based on back propagation

With the continuous development and progress of the times, college English courses should adapt to the requirements of the development of the information age in terms of teaching concepts, teaching contents, and teaching methods, so as to adapt to the changes of learners' personal needs and social needs. Because the traditional teaching mode ignores students' experience and perception, the development of students' language literacy is limited, which restricts the training efficiency of foreign language talents. The introduction of smart classroom can change this situation. The system can develop one-to-one learning methods based on students' mastery of foreign language knowledge and students' own strengths, so as to help students improve their English learning efficiency. This paper mainly analyzes the overall design of ICAI system on Massively Open Online Courses platform: the system design takes SSH structure as the framework and Back Propagation, and focuses on the basic principle, specific steps of training and heuristic rules of evaluation and application of BP algorithm. Smart classroom can truly realize personalized learning and teach students according to their aptitude, so that learners can get wisdom development. The Particle Swarm algorithm-Back Propagation prediction model is designed for student behavior data mining. The representative data are selected as the input of the neural network, and the course grade is selected as the output of the neural network. The grade prediction error is 12%, which provides a new idea for improving the teaching quality.
Overview of traditional denoising and deep learning-based denoising

Image denoising is a classical problem in the current field of computer vision. The goal of the task of image denoising is to use techniques to preserve as much clear detail of the original image as possible when the image has external noise. The essence of the image denoising process is to reduce the noise in the digital image and to recover and reconstruct the original clear image. The reason for image noise is that during image transmission and acquisition, the integrity of the image cannot be guaranteed due to environmental, acquisition equipment, human and other factors, so the image will inevitably be damaged by different degrees of noise. In medical, military, and optoelectronics fields, there is an extremely high demand for image realism, so the task of image denoising becomes very important. In this paper, we will discuss a history of image denoising techniques and analyze the denoising methods into traditional denoising and deep learning based denoising.
An improved small target detection algorithm for SSD

In order to improve the accuracy and robustness of the small target detection for SSD (Single Shot Multi-Box Detector) algorithm, this paper proposes an improved SSD algorithm. Firstly, the Mish activation function is introduced in the backbone network to provide more linear relationships. Secondly, a copy-reduce-paste data enhancement method is proposed to ensure a more balanced train. In the multi-scale detection stage, feature enhancement module and feature fusion strategy guided by channel attention mechanism are used to improve the feature information. At the same time, modify the loss function to improve the model training effect. Experiments on the VOC2007+2012 dataset show that the detection performance of the proposed algorithm is better than that of the SSD algorithm.
Online Application of Hotel Point of Sales: System Development Life Cycle of the Wing Ed Hotel of the Bali State Polytechnic

Purpose: This research aims to design and develop a web-based application, which will make it easier, faster and accurate for the hotel management both in operations and hotel supervision. The application also has the advantage of being able to be accessed all the time from all points in the world with various media. Method: The development of this application is carried out using the Waterfall method, which is a sequential and systematic software development method consisting of analysis, design, coding, and testing. The analysis is the process of gathering software requirements. Design is a software design process. Encoding is the process of translating designs into a form that can be read and done by a computer. Testing is the process of testing the results of coding to ensure results are free from error and according to what is needed. Result: Online Point of Sales Application at The Wing Ed Hotel Bali State Polytechnic provides facilities in accordance with business fields and user authority as follows: Manager, Front Office Officers, Restaurant Officer, and Laundry Officer. All transactions carried out on each business field have been integrated and processed automatically to produce fast, easy and accurate information at the hotel point of sales. This has fulfilled the needs of the hotel revenue information system as planned previously. Novelty: Produce online applications that simplify and speed up hotel operations because the entire process has been done automatically.
The Impact of Relatedness Support on Motivation and Depression

목적: 본 연구는 고등학생의 체육수업 상황에서 친구의 관계성지지와 체육수업 참여 동기의 관계, 친구의 관계성지지와 우울의 관계에서 회복탄력성의 매개역할을 검증하기 위해 진행되었다.방법: 본 연구를 위해 광주광역시 소재의 고등학생 183명을 대상으로 친구의 관계성지지, 회복탄력성, 체육수업 참여 동기, 우울을 자기보고식 설문지를 활용하여 측정하였다. 연구목적을 달성하기 위해 본 연구에서는 SPSS 27.0을 활용하여 기술분석, 상관분석, 모형검증을 위한 매개분석 등을 실시하였다. 또한, 척도의 신뢰도와 타당도 확보를 위해 AMOS 22.0을 활용하여 확인적 요인분석을 실시하여 모형적합도를 확인하였다. 모형검증을 위해 Hayes의 Process Macro 4.1을 추가로 활용하였다.결과: 체육수업 상황에서 친구의 관계성지지와 체육수업 참여 동기의 관계에서 회복탄력성은 유의한 부분매개효과가 있는 것으로 나타났다. 친구의 관계성지지와 우울의 관계에서 회복탄력성은 유의한 부분매개효과가 있는 것으로 나타났다.결론: 본 연구의 연구결과는 긍정적 신체활동 경험을 위한 체육수업 참여 동기 증진과 다양한 원인으로 우울을 경험할 수 있는 고등학생의 정신건강 개선을 위한 방안으로 친구의 관계성지지 및 회복탄력성의 역할에 대한 근거기반이 될 수 있을 것이다. 향후 이를 검증하기 위한 학교 체육수업 현장에서 활용할 수 있는 친구와의 관계성과 회복탄력성을 모두 고려한 상호보완적인 체육수업 프로그램의 개발과 연구가 필요할 것이다. Purpose: The purpose of this study was to examine mediating effect of resilience on the relationship between peer relatedness support and motivation, and between peer relatedness support and depression in physical education settings.Methods: In total, 183 high school students (female: 101) were recruited for this study. Participants" characteristics, peer relatedness support, resilience, motivation, and depression were measured using questionnaires. Descriptive statistics, correlation were performed using SPSS. For mediation analysis, Hayes"s Process Macro model 4 were employed. Confirmatory factor analysis was conducted using AMOS.Results: The results showed that resilience mediated the relationship between peer relatedness support and motivation and the relationship between peer relatedness support and depression on physical education.Conclusion: This study revealed that peer relatedness support and resilience may play a role in promoting motivation to participate in physical education and reduce depression symptom. This implies that physical education teachers may improve resilience-based teaching style when they intend to improve participation in physical education. Further studies are warranted to develop resilience programs, specialized in physical education settings.
Special issue on fuzzy systems for biomedical science in healthcare

General fuzzy min–max neural network (GFMMNN) is one of the efficient neuro-fuzzy systems for data classification. However, one of the downsides of its original learning algorithms is the inability to handle and learn directly from mixed-attribute data without using encoding techniques. While categorical feature encoding methods can be used with the GFMMNN learning algorithms, they exhibit many shortcomings. Other improved approaches proposed in the literature are not suitable for online learning algorithms working in the dynamically changing environments without ability to retrain or access full historical data, which are usually required for many real world applications. This paper proposes an extended online learning algorithm for the GFMMNN. The proposed method can handle the datasets with both continuous and categorical features. It uses the change in the entropy values of categorical features of the samples contained in a hyperbox to determine if the current hyperbox can be expanded to include the categorical values of a new training instance. An extended architecture of the original GFMMNN and its new membership function are introduced for mixed-attribute data. Important mathematical properties of the proposed learning algorithms are also presented and proved in this paper. The extensive experiments confirmed superior and stable classification performance of the proposed approach in comparison to other relevant learning algorithms for the GFMM model.
Semi-Supervised Instance-Segmentation Model for Feature Transfer Based on Category Attention

In the task of image instance segmentation, semi-supervised instance segmentation algorithms have received constant research attention over recent years. Among these algorithms, algorithms based on transfer learning are better than algorithms based on pseudo-label generation in terms of segmentation performance, but they can not make full use of the relevant characteristics of source tasks. To improve the accuracy of these algorithms, this work proposes a semi-supervised instance segmentation model AFT-Mask (attention-based feature transfer Mask R-CNN) based on category attention. The AFT-Mask model takes the result of object-classification prediction as "attention" to improve the performance of the feature-transfer module. In detail, we designed a migration-optimization module for connecting feature migration and classification prediction to enhance segmentation-prediction accuracy. To verify the validity of the AFT-Mask model, experiments were conducted on two types of datasets. Experimental results show that the AFT-Mask model can achieve effective knowledge transfer and improve the performance of the benchmark model on semi-supervised instance segmentation.
FeedMaster: A least-cost feed formulation App for minimizing the cost and maximizing milk yield

Objective: The study aimed to develop and assess an Android app designed for farmers with a low educational status that can formulate a least-cost ration. Materials and Methods: First, a computer-android-based app named BLRI FeedMaster was developed to guide users in formulating a balanced ration at the least cost. A survey was conducted on 30 livestock officers and 18 farmers with 50 cattle to evaluate its efficacy at the field level. The study outcomes were milk yield, feeding cost, milk composition, time, and cost for management before and after using the BLRI FeedMaster app. Descriptive statistics and paired sample t-tests were used to analyze the data. Results: After adopting the BLRI FeedMaster app, a significant increase was observed in daily average milk yield (9.39 ± 0.32 l from 8.37 ± 0.36 l), while a considerable decrease was observed in daily average feed quantity (4.88 ± 0.15 kg from 5.60 ± 0.17 kg) and feed cost (BDT 28.00 ± 0.50 from BDT 29.75 ± 0.49). Besides, the number of visits, time, and cost for seeking professional services regarding feed, health care, and other information was significantly minimized. The number of visits decreased to 0.36 ± 013 from 3.07 ± 0.38, and the consumed time was reduced from 270 ± 34.30 to 235.71 ± 59.42 min (p &lt; 0.05) after adopting the app. Conclusion: Hence, this app was very beneficial for farmers with a low economic and educational background and may ultimately help farmers with profitable animal farming and sustainable pro¬duction in the least developed countries like Bangladesh.
HELOT–Hunting Evil Life in Operational Technology

Operational technology (OT) refers to the industrial counterpart of information technology (IT). OT encompasses technology, systems, and protocols used in industrial operations for controlling, monitoring, and operating of industrial systems. Unlike standard IT systems, OT systems, such as those used in the industry, usually cannot be taken off-line in the event of postmortem forensics investigations. To remedy this situation we present a software architecture and prototype realization that allows the continuous capture of events within OT systems, IT systems, and the interconnected network(s). Our architecture can be realized with existing technologies while also allowing for extension and customization in functionality as well as application to diverse domains. We outline two application cases: capturing of forensics artifacts from a live OT system and possible paths for automation to reduce the cognitive load for cybersecurity operators in combined IT/OT environments.
Online Tracking of Maneuvering Target Trajectory Based on Chaotic Time Series Prediction

Online prediction of maneuvering target trajectory is one of the most popular research directions at present. Specifically, the primary factors balancing, between prediction accuracy and response time, will give the research substance. This paper presents an online trajectory prediction algorithm based on small sample chaotic time series (OTP-SSCT). First, we optimize in terms of data breadth. The dynamic split window is built according to the motion characteristics of the maneuvering target, thus realizing trajectory segmentation and constructing a small sample chaotic time series prediction set. Second, since fully considering the motion patterns of maneuvering targets, we introduce the spatiotemporal features into the particle swarm optimization (PSO) model identification algorithm, which improves the identification sensitivity of key trajectory data points. Furthermore, we propose a feedback optimization strategy of residual compensation to correct the trajectory prediction values to improve the prediction accuracy. For the initial value sensitivity problem of the PSO model identification algorithm, we propose a new initial population strategy, which improves the effectiveness of initial parameters on model identification. Through simulation experiment analysis, it is verified that the proposed OTP-SSCT algorithm achieves better prediction accuracy and faster response time.
DIGITAL CURRENCY IN INTERNATIONAL TRADE

What led to the meteoric growth and popularity of cryptocurrencies, primarily Bitcoin? What concepts preceded the development of modern cryptocurrencies? What is blockchain and what is the architecture of different distributed public ledgers? What is the use value of digital money in everyday transactions and what is the use value in international trade exchange? This paper tries to answer the above questions and decipher the highly complex technical jargon that usually accompanies the topic of digital money.
Prediction of Optimal Operation Point Existence and Its Parameters in BPG-Based Automatic Lossy Compression of Noisy Images

This chapter focuses on lossy compression of noisy images. In opposite to lossy compression of noise-free images, there are some peculiarities relating to noise filtering effect and analysis of compression technique performance. As a result of specific noise filtering, the so-called optimal operation point (OOP) may exist. OOP is related to such parameters of a coder that quality of a compressed image is closer to the corresponding noise-free (true) image compared to quality of uncompressed (original, noisy) image where quality is characterized by a certain metric, conventional or visual one. If OOP for a given noisy image exists, it is expedient to automatically compress an image under interest in OOP or, at least, its neighborhood. However, since the true image is not at disposal in practice, it occurs impossible to determine does OOP exist or not. In this chapter, we demonstrate that it is, in general, possible to predict OOP existence (according to different metrics) before image compression. It is also possible to reasonably set the coder parameters for the better portable graphics (BPG) coder, which is paid the main attention. The introductory part concerns a basic case of additive white Gaussian noise in one-component (grayscale) images. We demonstrate that increase (reduction) of the conventional metric PSNR and visual quality metric PSNR-HVS-M can be predicted. If increase is predicted, the decision on OOP existence is undertaken and vice versa. For the case of OOP absence, a way for setting the parameter Q that controls compression in BPG is proposed. Thus, it becomes possible to carry out lossy compression automatically. The extensions of the proposed approach to joint compression of three-component images are proposed. OOP existence in this case is even more probable. Image sub-sampling in color components is studied. The possibility of OOP existence for visual quality metrics PSNR-HA and MDSI is demonstrated.&nbsp
ROS-Based Multirobot System for Collaborative Interaction

This paper presents the design and implementation of a collaborative multi-robot system based on ROS. The goal is to manipulate objects with multiple cobots simultaneously by following commands given by a user via gestures. Two methods have been designed, developed, implemented and experimentally evaluated: The first one is based on the ROS package called MoveIt! and focuses mainly on configuration to allow simultaneous control of different cobots. The second method involves the development of a third-party motion planner that sends commands directly to the controllers responsible for executing the cobots’ movements. The Leap Motion, a device that can be used for gesture recognition, is also integrated into the system to enable user interaction in object manipulation. The system has been tested in simulation using Gazebo and evaluated in a real UR10 robot. The main contribution of the proposed architecture is that it solves the problem of controlling multiple robots simultaneously in ROS. In particular, our approach allows simultaneous execution of tasks with different types of controllers, brands and models, as well as direct control of the robots by using the Leap Motion device.
Influence of data amount, data type and implementation packages in GPU coding

Graphic Processing Units (GPUs) are becoming popular in computational physics. Seeing the increasing trend of using GPUs in the physics community, we provide a comparison of the two major packages Numba and CuPy for GPU coding in Python language. We have discussed the influence of the data amount and data type on the performance of the GPU code. The data transferring time from GPU to CPU and its influence on the total execution time has also been analyzed. We find that when the grid numbers reaches 107, Numba will be faster than CuPy. Meanwhile, there is no noticeable difference in the data transmission speed between Numba and CuPy. Setting the data types as single-precision in Numba programs can improve the computation time by at least 20%.
Impact of microarchitectural state reuse on serverless functions

Serverless computing has seen rapid growth in the past few years due to its seamless scalability and zero resource provisioning overhead for developers. In serverless, applications are composed of a set of very short-running functions which are invoked in response to events such as HTTP requests. For better resource utilization, cloud providers interleave the execution of thousands of serverless functions on a single server.
Non-goal driven eye movement after a visual search task

We investigated the functions and mechanisms of non-goal-driven eye movements, which are defined as eye movements induced when looking at visual stimuli on a display without engaging in a specific task or looking at a display without any visual stimuli or tasks. In our experiment, participants were asked to perform a visual search task on a display, which was followed by a rest period in which stimuli remained on the display or all stimuli were erased. During the rest period, the participants were asked to only look at the displays without engaging in any visual or cognitive tasks. We mainly analyzed the gaze-shift patterns in both task and rest periods, in which eye movements were classified in accordance with the angles of saccade directions in two consecutive saccades. The results indicate a significant difference between goal-driven eye movements, which were observed in the task period, and nongoal- driven eye movements, which were observed in the rest period. Scanning gaze-shift patterns dominated the task period, and backward and corrective-saccade-like gaze-shift patterns dominated the rest period. The gaze-shift pattern was affected by the task-difficulty during the task period. From these findings, we propose a model describing the oculomotor system in terms of goal-driven and non-goal-driven eye movements. In this model, the engagement levels of top-down and bottom-up control change along with task difficulty and are affected by the gaze-shift patterns during a visual search task. Decoupling of top-down control from the oculomotor system during a rest period induces backward saccades, resulting in fixation around the central part of a display. Therefore, we suggest that non-goaldriven eye movements play a crucial role in maintaining the readiness of the oculomotor system for the next search task.
Retracted: A conceptual framework for crowdsourcing requirements engineering in SCRUM‐based environment

Abstract Retraction: [Mohammed Naif Alatawi, A conceptual framework for crowdsourcing requirements engineering in SCRUM‐based environment, IET Software 2022 ( https://doi.org/10.1049/sfw2.12077 )]. The above article from IET Software , published online on 22 November 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Hana Chockler, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal’s peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.