Reviewerly/exHarmony huggingface dataset:
GAN-Based ROI Image Translation Method for Predicting Image after Hair Transplant Surgery

In this paper, we propose a new deep learning-based image translation method to predict and generate images after hair transplant surgery from images before hair transplant surgery. Since existing image translation models use a naive strategy that trains the whole distribution of translation, the image translation models using the original image as the input data result in converting not only the hair transplant surgery region, which is the region of interest (ROI) for image translation, but also the other image regions, which are not the ROI. To solve this problem, we proposed a novel generative adversarial network (GAN)-based ROI image translation method, which converts only the ROI and retains the image for the non-ROI. Specifically, by performing image translation and image segmentation independently, the proposed method generates predictive images from the distribution of images after hair transplant surgery and specifies the ROI to be used for generated images. In addition, by applying the ensemble method to image segmentation, we propose a more robust method through complementing the shortages of various image segmentation models. From the experimental results using a real medical image dataset, e.g., 1394 images before hair transplantation and 896 images after hair transplantation, to train the GAN model, we show that the proposed GAN-based ROI image translation method performed better than the other GAN-based image translation methods, e.g., by 23% in SSIM (Structural Similarity Index Measure), 452% in IoU (Intersection over Union), and 42% in FID (Frechet Inception Distance), on average. Furthermore, the ensemble method that we propose not only improves ROI detection performance but also shows consistent performances in generating better predictive images from preoperative images taken from diverse angles.
Clustering: Finding Patterns in the Darkness

Machine learning is changing the world and fuelling Industry 4.0. These statistical methods focused on identifying patterns in data to provide an intelligent response to specific requests. Although understanding data tends to require expert knowledge to supervise the decision-making process, some techniques need no supervision. These unsupervised techniques can work blindly but they are based on data similarity. One of the most popular areas in this field is clustering. Clustering groups data to guarantee that the clusters’ elements have a strong similarity while the clusters are distinct among them. This field started with the K-means algorithm, one of the most popular algorithms in machine learning with extensive applications. Currently, there are multiple strategies to deal with the clustering problem. This review introduces some of the classical algorithms, focusing significantly on algorithms based on evolutionary computation, and explains some current applications of clustering to large datasets.
Optimal Image Based Information Hiding with One-dimensional Chaotic Systems and Dynamic Programming

Information hiding is a technology aimed at the secure hiding of important information into digital documents or media. In this paper, a new approach is proposed for the secure hiding of information into gray scale images. The hiding is performed in two stages. In the first stage, the binary bits in the sequence of information are shuffled and encoded with a set of integer keys and a system of one-dimensional logistic mappings. In the second stage, the resulting sequence is embedded into the gray values of selected pixels in the given image. A dynamic programming method is utilized to select the pixels that minimize the difference between a cover image and the corresponding stego image. Experiments show that this approach outperforms other information hiding methods by 13.1% in Peak Signal to Noise Ratio (PSNR) on average and reduces the difference between a stego image and its cover image to 0 in some cases.
A Research on CALL-Based POA in English Writing Teaching

The combination of production-oriented approach (POA) and modern computer technology makes English learning and teaching more convenient and efficient. This research combines the computer-assisted language learning (CALL) mode with POA to form the online and offline hybrid teaching mode, aiming to provide a reference for English teachers.
A Topography of Data Commons: From Regulation to Private Dynamism

Abstract Knowledge is subject to enclosure through digital technology and legal rules. Data collected, stored and pooled by the Internet of Things (IoT) or Artificial Intelligence (AI) are no exception to this. Operators acting in the markets related to the algorithmic society may have a quite diversified range of intellectual property rights (IPRs) to protect the information they produce and manage. This is exploited through algorithmic processing techniques, aggregating collected data for the generation of new ones, thus creating additional information and knowledge. This paper studies whether and when data, information and knowledge, presented within the Big Data, IoT and AI structures, may be considered and exploited as commons. The analysis is not aimed at stating that commons should be the general solution for the algorithmic society. Nor does it endorse legal interpretations unilaterally favoring openness and limiting IPR protection and privacy rules (though this could be the case under certain circumstances). The question is to establish whether a certain level of commons should be provided by regulation or left to spontaneous private initiatives. In this regard, two different meanings of data commons are used in this work. The first one refers to the open access systems provided by regulation, equivalent to a public domain protection, and opposed to exclusivity mechanisms. The second refers to data commons which are privately ‘constructed’ on top of background regulation and manage resources for a limited set of claimants.
Conceptual Design of Python IDE with Embedded Turkish Spoken Chatbot that Analyzes and Corrects the Syntax Errors

Intelligent agents, in addition to act as a cognitive tool, they can also to be designed as learning agents. In this way, they can learn the user's behavior from users' past behaviors. Learning agents can analyze the user's / student's behavior to a task, build a database of past activities, and suggest better strategies. This study provides a detailed review of interactive guidance using intelligent agents, and then introduces a concept model for a Conversation-based Turkish Python Integrated development environment (IDE) that can analyze user syntax errors and help them to correct errors. The model proposed in this study consists of three layers. These are user interface layer, middle layer, and Python interpreter layers. User interface layer consists of code editor and chatbot components. Middle layer includes code structural control subsystem, code error manager, and intelligent agent subsystems. The code structural control module analyzes conditions, loops, branching, and other types of program flow controls in the user's code. The code error manager analyzes the error outputs of the user code which is generated by the Python interpreter. The intelligent learner, on the other hand, uses these inferences to understand the reason for the student's error and convey the necessary actions to the user by the help of chatbot and suggest possible corrections. The proposed Integrated development environment (IDE) has a well-designed UI that can be easily adapted by newcomers. The coding editor can be used as a stand-alone desktop software, or it can also connect to a cloud storage to store user codes in the cloud. A web application is also planned for our proposed IDE. The teacher will be able to assign homework to the student over the web. The student will be able to view these assignments on the web, do the assignments in the desktop editor and send them back to the teacher over the web. In addition, each user’s error characteristics will be analyzed, the success of the learning will be measured, and the deficiencies of the students will be determined using the Intelligent Agent Subsystem.
Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Numerous challenges are usually faced during the design and development of an autonomous mobile robot. Path planning and navigation are two significant areas in the control of autonomous mobile robots. The computation of odometry plays a major role in developing navigation systems. This research aims to develop an effective method for the computation of odometry using low-cost sensors, in the differential drive mobile robot. The controller acquires the localization of the robot and guides the path to reach the required target position using the calculated odometry and its created new two-dimensional mapping. The proposed method enables the determination of the global position of the robot through odometry calibration within the indoor and outdoor environment using Graphical Simulation software.
An all-optical perceptron for binary classification

We present an all-optical perceptron that utilizes an injection-locked Fabry-Perot laser diode as an adaptive activation unit. The evaluation of the proposed neuron was performed theoretically on the problem of binary classification of pairs of hand-written digits. Results for the MNIST dataset reveal an average accuracy of over 97% for activation function profile tailored by adjusting the laser diode bias current.
"Designing an Adjustable Head Frame for Surgery Using Mixed Reality Technology Hololens 2"

To position three-dimensional holograms to a strictly defined point in space, it is necessary to use special markers, which can be represented in the form of images [1], QR codes [2] or geometric objects..
Trajectories of Idea Emergence in Dialogic Collaborative Problem Solving: Toward a Complex Dynamic Systems Perspective

According to the complex dynamic systems (CDS) perspective, learning emerges at various system levels. This study built a coherent theoretical framework based on CDS and Bakhtinian dialogic theory and further employed the concept of attractor (i.e., certain stable states that recur over time) in CDS theory to investigate the trajectories of idea emergence and how they diversified group outcomes in dialogic collaborative problem solving (D-CPS). Two contrasting groups were compared using visual and qualitative analysis approaches. The analysis based on idea tree diagrams showed that new ideas emergent in group discussion tended to attract local utterances and performed features of attractors in CDS in both high-performing and low-performing groups. The analysis based on idea hierarchy diagrams revealed how ideas emerged at various system levels. It was also found that status problems were likely to affect the functioning of regulative feedback loops, which might give rise to different structures of idea evolution. This study proposed CDS theory as an alternative perspective, augmented by the ethical considerations of Bakhtinian dialogism, for examining the dynamics of D-CPS.
Impact of Organisational Climate on Nurses’ Commitment at Public Hospitals in Saudi Arabia

Background: Nurses represent most healthcare professionals. They work in different healthcare organisations and play a vital role in improving the quality of healthcare. Consequently, it is essential to study the factors that influence nurses' organisational commitments. The purpose of this study is to explore the impact of organisational climate and nurses' commitment in Saudi Arabian public hospitals and to explore the correlation between hospital climate and nurses' commitment.&#x0D; Methods: a quantitative cross-sectional method conducted at the Al-Zulfi General Hospital, Riyadh; King Abdul-Aziz Hospital, Makkah; and King Fahd Central Hospital, Jazan. The Data was collected from 343 participants by using electronic questionnaire in English distributed for the period between 01/03/2021 to 01/04/2021. The questionnaire is composed of three parts: demographic data, a three-component model (TCM) of employee commitment, and an organisational climate questionnaire (OCQ). The sample size was calculated based on the Steven K. Thompson equation. The data were analysed using analysis of variance (ANOVA) and t-tests to identify the differences in organisational commitment and organisational climate among participants’ characteristics. Correlation analysis was used to identify the relationship between organisational commitment and organisational climate.&#x0D; Results: The results demonstrated a significant positive moderate correlation between nurses’ commitment and hospital climate, where r = 0.580 and P &lt; 0.001. We found that organisational commitment was observed as significantly correlated with gender, nationalities and hospital; also, organisational climate was perceived as significantly correlated with gender, nationalities, marital status, years of experience and hospitals with a p-value &lt; 0.05. It is evident that improving organisational climate leads to an increase in nurses’ commitment.&#x0D; Conclusion: Improving the organisational climate could help to promote nurse retention and enhance nursing commitment. The current study recommends that nurse managers pay careful attention to the organisational environment to increase organisational commitment, which can contribute to positive attitudes and appreciation for the organisation.
Continuous Viewpoint Planning in Conjunction with Dynamic Exploration for Active Object Recognition

Active object recognition (AOR) aims at collecting additional information to improve recognition performance by purposefully adjusting the viewpoint of an agent. How to determine the next best viewpoint of the agent, i.e., viewpoint planning (VP), is a research focus. Most existing VP methods perform viewpoint exploration in the discrete viewpoint space, which have to sample viewpoint space and may bring in significant quantization error. To address this challenge, a continuous VP approach for AOR based on reinforcement learning is proposed. Specifically, we use two separate neural networks to model the VP policy as a parameterized Gaussian distribution and resort the proximal policy optimization framework to learn the policy. Furthermore, an adaptive entropy regularization based dynamic exploration scheme is presented to automatically adjust the viewpoint exploration ability in the learning process. To the end, experimental results on the public dataset GERMS well demonstrate the superiority of our proposed VP method.
A Novel LSB Matching Algorithm Based on Information Pre-Processing

This paper analyzes random bits and scanned documents, two forms of secret data. The secret data were pre-processed by halftone, quadtree, and S-Box transformations, and the size of the scanned document was reduced by 8.11 times. A novel LSB matching algorithm with low distortion was proposed for the embedding step. The golden ratio was firstly applied to find the optimal embedding position and was used to design the matching function. Both theory and experiment have demonstrated that our study presented a good trade-off between high capacity and low distortion and is superior to other related schemes.
Impact of Network Topology on the Convergence of Decentralized Federated Learning Systems

Federated learning is a popular framework that enables harvesting edge resources' computational power to train a machine learning model distributively.However, it is not always feasible or profitable to have a centralized server that controls and synchronizes the training process.In this paper, we consider the problem of training a machine learning model over a network of nodes in a fully decentralized fashion.In particular, we look for empirical evidence on how sensitive is the training process for various network characteristics and communication parameters.We present the outcome of several simulations conducted with different network topologies, datasets, and machine learning models.
Factors Influencing the Adoption of E-Health Management among Saudi Citizens with Moderating Role of E-Health Literacy

This study aimed to examine the role of internet access, social influence, facility access, and cost concerns on E-health adoption in Saudi Arabia with moderating role of E-health literacy. Saudi citizens were the targeted population of this study. To fulfill the objectives of this study, quantitative research methods with a cross-sectional technique were applied. A self-administrative survey was employed with a questionnaire to collect the data. This study used the purposive sampling method for the collection. Structure equation modeling was applied by using Smart PLS 3 to analyze the data. The findings of the current study show that internet access, social influence, and facility access have significant and positive effects on E-health adoption. Cost concerns have a significant and negative influence on the Adoption of E-Health Management services. Results revealed that E-health literacy has a significant and positive moderating role in the association of internet access, facility access, and cost concerns on E-health adoption. Results of this study contribute to the body of literature by finding that internet access, social influence, facility access, and cost concerns have a significant effect on the Adoption of E-Health Management services and E-health literacy is essential to improve the Adoption of E-Health Management services
Standardized Judgment Method of Shooting Training Action Based on Digital Video Technology

Aiming at the difficulty of standardizing the action of basketball shooting training, a new method of standardizing the action of basketball shooting training is proposed based on digital video technology. The digital video signal representation, video sequence coding data structure, and video sequence compression coding method are analyzed, and the pixels of basketball shooting training action position space are sampled to collect basketball shooting training images. The time difference method is used to extract the movement target of basketball shooting training from a digital video sequence. Based on digital video technology, the initial background image is estimated, and the update rate is introduced to update the background estimation image. According to the pixel value sequence of the basketball shooting training image, the pixel model of the basketball shooting training image is defined and modified. By judging whether the defined pixel value matches the background parameter model, the standardization of shooting training can be realized. The experimental results show that the proposed method has good stability, high precision, and short time in determining the standardization of shooting movement, can correct the wrong shooting movement in real time, and can effectively guide basketball shooting training.
A Discriminative Target Equation-Based Face Recognition Method for Teaching Attendance

In this paper, we use discriminative objective equations to conduct an in-depth study and analysis of face recognition methods in teaching attendance and use the model in actual teaching attendance. It focuses on the design and implementation of the attendance module, which uses wireless network technology to record students’ access to classrooms in real time, and relies on face recognition technology to identify students’ sign-in images to achieve attendance records of students’ independent attendance sign-in. Real-time detection of student attendance is achieved by combining face detection and face recognition technology through regular camera photography and automatic attendance check-in by the server. Based on the recognition results of the attendance check-in image, an attendance mechanism is proposed, and the attendance score of the student for the current course can be calculated using the attendance mechanism, which realizes the automatic management of student attendance. For the face recognition process, the system uses the Ad boost algorithm based on Hear features to achieve face detection, preprocesses the face samples with gray normalization, rotation correction, and size correction, and uses the method based on LBP features to achieve face recognition. Firstly, a combination of histogram equalization and wavelet denoising is chosen to preprocess the training sample images to obtain the face image light invariance description, and then, the initial dictionary is constructed using the dimensionality reduction performance of the PCA method; next, the initial dictionary is updated, and a new dictionary with representation and discrimination capabilities is obtained using the LC-KSVD algorithm that makes improvements in the dictionary update stage. The sparse coefficients of the feature matrix of the test sample image under the new dictionary are calculated, and the class correlation reconstruction is performed on the feature matrix of the test sample image, and the corresponding reconstruction error is solved; finally, the discriminative classification of the test sample image is achieved according to the solved class correlation reconstruction error. The relevant experiments on the face database prove that the algorithm can improve the recognition accuracy to a certain extent and better solve the influence of changing lighting conditions on the face recognition accuracy.
Teachers' Strategies in Introducing Literacy to Early Childhood: Lesson from Indonesia

Purpose – This study aims to analyze teachers' strategies in introducing literacy to early childhood.Design/methods/approach –The researchers used the case study method. The research subjects are two teachers who teach in early childhood education institutions in Bandung, Indonesia.Findings –The results of this study indicate that in introducing literacy to children, teachers use multiple strategies, such as optimizing role-play activities, stimulation through active teacher conversations that encourage children to communicate (rich teacher talk), utilizing the use of big book media, and focusing on phonological awareness. The study results show that the teacher's role is significant in optimizing literacy learning for children.Research implications/limitations – This case study focuses on literacy learning in early childhood based on the experiences of two teachers in Bandung, Indonesia.Practical implications –This case study shows that multiple strategies are appropriate to introduce early childhood literacy and prepare children for the next level of education.Originality/value – This study contributes to the understanding of literacy in early childhood. In addition, this study recommends that teachers apply multiple strategies in introducing literacy through play activities.&#x0D; Paper type Case study