Reviewerly/exHarmony huggingface dataset:
Nonconvex resource allocation for inelastic enterprise applications deployment into the cloud via particle swarm optimization

The advantages of cloud computing attract a large number of enterprises to deploy their applications into the cloud, thereby reducing their own operating costs. This paper considers deploying inelastic applications into the cloud and proposes an optimal resource allocation model. The deployment functions for inelastic applications are nonconvex (e.g., sigmoidal), then the resource allocation model becomes a hard nonconvex optimization problem. The traditional gradient-based resource allocation algorithm cannot effectively achieve the global optimum. Therefore, this paper applies particle swarm optimization (PSO) method to design a resource allocation scheme. This scheme can not only effectively solve the resource allocation problem of deploying inelastic enterprise applications into the cloud, but also solve the hard problem of deploying multi-class applications into the cloud when the enterprise can support both elastic and inelastic applications. We also compare the performance of the proposed PSO-based resource allocation scheme with some other methods and illustrate some numerical examples to verify the effectiveness and superiority of the proposed resource allocation scheme.
The Effectiveness of Guided Discovery Strategy in Developing EFL Preparatory School Pupils' Writing Skills

The current study investigated the effectiveness of guided discovery strategy in developing preparatory stage pupils' EFL writing skills. The participants of the study comprised )60( pupils randomized from first-year preparatory pupils at Nasser preparatory school in Tahway, Ashmoun, Menoufia governorate during the second semester of the 2020-2021 scholastic year. They were divided into two groups; experimental (30) and control (30). The guided discovery strategy was used in teaching the experimental group while the control group received regular instruction. The study adopted the quasi-experimental pre-posttest design. A pre-posttest writing skills test was prepared by the researcher and administered before and after the treatment. The findings revealed that using the guided discovery strategy had a positive effect on developing EFL preparatory stage pupils' writing skills. Hence, the result seemed to provide adequate evidence to support the hypotheses. The progress in the pupils' writing skills was due to the guided discovery strategy. It is recommended that teachers and curriculum designers pay attention to the importance of the guided discovery strategy in EFL teaching and learning.
Geometrial: Development of Educational Digital Game for Combined Two-Dimensional Figure Learning

Mathematics is one of the subjects that is often considered difficult and boring for students. This is evidenced by the poor scores obtained by the students. One of the chapters that are considered difficult is the geometry chapter, especially on the topic of combined two-dimensional figures studied by students at the Vocational High School (SMK) level. Spatial skills are needed for students to be able to solve combined two-dimensional figures questions, which to learn will be very difficult without using assisted learning media. While the learning so far is still using the conventional learning approach which is considered boring for students. This is due to the absence of learning media that is fun and can be easily accessed by students. This study tries to present a solution to this problem in the form of a mobile-based educational digital game design that can be accessed by all students. This digital educational game is called Geometry. This study uses Research &amp; Development (R&amp;D) combined with the Agile-Extreme Programming method to develop this educational digital game. Tests were carried out using an expert validation approach to game prototypes. This study uses a questionnaire that adapts the Computer System Usability Questionnaire (CSUQ) to assess the usability aspect of the game system built. Meanwhile, to assess this game from the point of view of educational media, this study used an evaluation questionnaire of material experts and media experts. The development process occurs in 3 iterations of development which includes the ideation, conceptualization, and prototyping stages. The results obtained from the assessment of material experts are 88.9% in the aspect of material suitability, and 85.7% in the suitability aspect of learning evaluation. While the results of the assessment obtained from media experts were 77.1% on the software engineering aspect, 76.3% on the visual design aspect, 80% on the media design aspect, and 77.6% on the system usability aspect (CSUQ).
Nonlinear nano-imaging of few-fs coherent dynamics in 2D graphene-semiconductor heterostructures

Using correlative adiabatic plasmonic nanofocused four-wave mixing and two-photon photoluminescence we image nanoscale heterogeneity, resolve few- to 10’s fs coherent dynamics, and model underlying interlayer coupling in 2D graphene/WSe2 heterostructures.
Digital Image Security: Steganography and Watermarking

Digital image is one of the best media to store data. It provides large capacity for hiding secret information which results into stego-image imperceptible to human vision. Digital images are widely communicated over the internet. The security of digital images is an essential and challenging task on shared communication channel. Various techniques are used to secure the digital image, such as encryption, steganography and watermarking. Steganography is the process of art and science in such a way that no one apart from sender and intended recipient even realizes that the communication is going on. It is also used to authenticate the digital images. This paper presents a security technique using encryption, steganography and watermarking. It comprises of three key components: (1) the original image has been encrypted using large secret key by rotating pixel bits to right through XOR operation, (2) for steganography, encrypted image has been altered by least significant bits (LSBs) of the cover image and obtained stego image, then (3) stego image has been watermarked in the time domain and frequency domain to ensure the ownership.The proposed approach is efficient, simpler and secured; it provides significant security against threats and attacks.
The Effect of Active Learning Education Program on Self-directed Learning Readiness and Learning Satisfaction - Coaching Foundation for Adult Learners -

This study applied the coaching-based learning method to adult learners(college students) to contribute to the growing demand for learning in the rapidly changing society, and examined the effects of coaching-based active learning education programs on self-directed learning readiness and learning satisfaction.The effects of the study were analyzed by pre-post-control group design and the coaching effect was verified by comparing the coaching group with the control group without any treatment.The satisfaction of the experimental group with a coaching-based active learning program was statistically higher than that of the control group who participated in the general lecture class.The findings of the study are intended to promote career guidance and learning achievement of adult college students through coaching.Therefore, it is necessary to establish a career coaching model for adult college students, establish a dedicated department, reflect the budget, and change the perception of school authorities from a mid-to long-term perspective.
VALUE ORIENTATIONS IN THE PROFESSIONAL ACTIVITY OF A PRESCHOOL EDUCATION TEACHER

The article discusses the main directions of the formation of value orientations at the stage of preschool childhood. The approaches to modeling the value-oriented educational process in a preschool educational organization are characterized, the issues of introducing preschool children to the values in the practice of teachers’ activities are analyzed.
Development of 3D Simulation Application for Production Multimedia Subject

The development of 3D simulation applications for Production of Multimedia Subjects is intended to provide exposure and convenience to teachers, especially prospective teachers of Universiti Pendidikan Sultan Idris in exploring the 3D virtual world of the classroom and school environment in the context of learning and facilitate teachers to obtain daily information in finding relevant information. 3D. The objective of this study is to identify the 3D elements needed to build a school environment, design and develop a virtual reality application simulation application program that can be developed and evaluate the function of this 3D simulation application can help teachers teach and learn in schools. In addition, the use of applications such as simulators is basically for research and educational purposes. This study uses the ADDIE model used in the development of a 3D simulation application for this Multimedia Production subject. Simulation is capable of producing a virtual world of the surrounding atmosphere and a real environment. This Multimedia Production subject learning simulation can give students an idea of the learning environment in school. There is no denying that the use of simulation applications is not widespread as most of the available applications mostly focus on skills such as aeroplane piloting. With this, applications such as simulators are developed to give students an experience of the virtual learning environment.
Learning Impact of Non-personalized Approaches for Point of Interest Recommendation

Location-based social networks (LBSNs) have impacted our lives recently to a great deal. Typical location-based social networking sites have provision of declaring a check-in at a venue for users. Users can communicate this information with their friends, thus, generating huge dataset. This large geographical information has paved the way to build location-based recommender systems. Location recommendation service is an integral feature of location-based social networks. More and more users are using this service to explore new places and take timely and effective decisions. These systems provide a rich knowledge about a new place that a user has never visited before and also recommend interesting locations to the user after mining socio-spatial check-in data. In this paper, the authors present non-personalized techniques to utilize the check-in information for recommending popular and interesting locations to users. Background of location-based social networks and various techniques to develop location recommender system is discussed initially, followed by existing work and research issues of location-based recommender system. Authors have presented illustrative examples to mine the available spatial information of real-world location-based social network to suggest best interesting locations.
CORRECTION NOTICE

This article refers to:Cadomian crust of Eastern Iran: evidence from the Tapeh Tagh granitic gneissesGeochronology and geochemistry of exotic blocks of Cadomian crust from the salt diapirs of SE Zagros: the Chah-Banu exampleGeochronology and petrogenesis of the Late Neoproterozoic granitic gneisses of Golpayegan metamorphic complex: a new respect for Cadomian crust in the Sanandaj-Sirjan zone, Iran
Reliable Scene Recognition Approach for Mobile Robots with Limited Resources Based on Deep Learning and Neuro-Fuzzy Inference

Indoor scene recognition is complex due to the commonality shared between different spaces. Still, when it comes to robotics applications, the uncertainty increases due to illumination change, motion blur, interruption due to external light sources, and cluttered environments. Most existing fusion approaches do not consider the uncertainty, and others have a high computational cost that may not suit robots with limited resources. To mitigate these issues, this paper proposes a reliable indoor scene recognition approach for mobile robots with limited resources based on robust deep convolutional neural networks (CNNs) feature extractors and neuro-fuzzy inference to consider the uncertainty of the data. All CNN feature extractors are pre-trained on the Imagenet dataset and used in the manner of transfer learning. The performance of our fusion method has been assessed on a customized MIT-67 dataset and for real-time processing on a Locobot robot. We also compare the proposed method with two standard fusion methods---Early Feature Fusion (EFF) and Weighted Average Late Fusion (WALF). The experimental results demonstrate that our method achieves competitive results with a precision of 94%, and it performs well on the Locobot robot with a speed of 3.1 frames per second.
Development of Network Security Based on the Neural Network PSD Algorithm

The more frequent occurrence of network security incidents has an impact on network security. Through the research on network security situational awareness, this paper constructs a multilevel network security situation evaluation index system from various aspects and uses the Elman neural network optimized by the genetic algorithm to evaluate network security situation. Aiming at the disadvantage of subjective dependence in the traditional assignment method of basic probability assignment function, Elman neural network is used to obtain the basic probability assignment function to increase its objectivity, and it is optimized with the PSD algorithm. In addition, the neural network is further improved by the genetic algorithm. In the traditional D-S evidence theory, an evidence correction step is added to optimize the situation that the final judgment result is incorrect due to evidence conflict. Finally, the fusion rules of the D-S evidence theory are used to fuse the support degrees of the four first-level situations to different security levels to obtain the final network security situation assessment result. The results show that the prediction accuracy of the GA-Elman neural network model is as high as 80%, which is significantly higher than that of the traditional D-S model, indicating that the model proposed in this paper has improved the accuracy of the assessment and prediction results. In conclusion, this study provides feasible theoretical prediction guidance for the accurate assessment of network security posture, reveals the improvement ideas for network security development, and is of great significance for the maintenance of network environment security.
A Convolutional Neural Networks Model for Breast Tissue Classification

Diagnosis of breast cancer and the determination of cancer type are essential information for cancer research in monitoring and managing the disease. Artificial intelligence techniques developed in recent years have led to many developments in medicine, as any information about the patient has become more valuable. Especially, artificial intelligence methods used in the detection and classification of cancer tissues directly assist physicians and contribute to the management of the treatment process. This study aims to classify breast tissues with ten different tissue characteristics utilizing the breast tissue data set, which has 106 electrical impedance spectroscopies taken from 64 patients in the UCI Machine Learning Repository database. Various machine learning algorithms such as k-nearest neighbors, support vector machine, decision tree, self-organizing fuzzy logic, and convolutional neural networks are used to classify these tissues with the accuracy of 81%, 78%, 82%, 92%, and 96%, respectively. This study demonstrated the benefit of the usage of convolutional neural networks in cancer detection and tissue classification. Compared to traditional methods, convolutional neural networks provided a more reliable and better results.
Topluluk Öğrenme ile Google Uygulamalarının İçerik Derecelendirmelerini Analiz Etme

Google Play was launched under the name of Android Market and made its reputation known all over the world. The mobile application market, which is a package manager developed by Google for Android users, contains applications that appeal to many areas and age ranges. The wide area in which applications spread and the data flow, which has reached the level of being called “big data”, has started to attract the attention of researchers. The excessive increase in the number of applications makes it difficult for parents to follow up on the content. In order to provide content rating of applications on Google Play, it is needed to be classified by machine learning methods. In this study, content rating classification was made by analyzing “Category, Rating, Reviews, Size, Installs, Type, Genres, Last Updated, Current Version, Android Version” features of 10757 applications on Google Play, Ensemble Learning methods (Adaboost, Bagging, Random Forest, Stacking), Logistic Regression, Artificial Neural Network, K-Nearest Neighbors algorithms.
SOSIALISASI PELESTARIAN DAN PENGELOLAAN CAGAR BUDAYA DI KOTA PEMATANG SIANTAR

The Pematang Siantar city government has issued Regional Regulation number 1 of 2021 concerning the Preservation and Management of Cultural Conservation in the city of Pematang Siantar. Cultural heritage is a cultural heritage that is material in the form of Cultural Conservation Objects, Cultural Conservation Buildings, Cultural Conservation Structures, Cultural Conservation Sites, and Cultural Conservation Areas on land and or in water that need to be preserved because they have important values for history, science, education, religion, and or culture through the determination process.&#x0D; The purpose of this community service is to explain to participants, namely government employees in the sub-districts of Pematang Siantar city, which are eight sub-districts so that they can know and get provisions to carry out the preservation and management of cultural heritage or suspected cultural heritage in their respective regions.The method used is the lecture method, followed by a question and answer session between the lecturer and the participants. The lecture was conducted by displaying a slide that could show an image of a suspected cultural heritage at the Office of Education and Culture of Pematang Siantar City Jl. Merdeka, Pematang Siantar.&#x0D; The result achieved in this socialization is that participants consisting of government employees in the sub-district can recognize a number of objects that are suspected of being cultural heritage in their working areas. Sustainability of socialization means that sub-district employees are equipped with abilities, and are able to take action in the effort to preserve and manage cultural heritage, or those suspected of cultural heritage by the community to sub-district employees to be followed up. The mandate of the Pematang Siantar Mayor's Regional Regulation number 1 of 2021 can be applied.
Joint Consultation as a Determinant of Job Satisfaction in the Insurance Sector

The urge for employees to have a voice in decision making process at workplace has made joint consultation imperative. This study examined joint consultation as a determinant of job satisfaction in the insurance sector. The study adopted the survey research method using Krejie and Morgan sample size determination formular to selects a sample size of eighty-six (86) from a population of one hundred and ten employees. Seventy-three copies of the questionnaire were duly completed and thirteen were not usable. Two hypotheses were tested using partial least square structural equation model, SMARTPLS 3.2.2. SPSS version 25. The result revealed that joint consultation has an effect on employee job satisfaction. The P values for the two hypotheses tested are less than 0.05 level of significance. Therefore, for the two hypotheses, the alternative hypotheses were accepted. The study recommended among others that organizations should see joint consultation as a means for enhancing job satisfaction also further studies should be conducted on joint consultation using other indicators of job satisfaction.
Evaluating the Stability of Deep Image Quality Assessment with Respect to Image Scaling

Image quality assessment (IQA) is a fundamental metric for image processing tasks (e.g., compression). With full-reference IQAs, traditional IQAs, such as PSNR and SSIM, have been used. Recently, IQAs based on deep neural networks (deep IQAs), such as LPIPS and DISTS, have also been used. It is known that image scaling is inconsistent among deep IQAs, as some perform down-scaling as pre-processing, whereas others instead use the original image size. In this paper, we show that the image scale is an influential factor that affects deep IQA performance. We comprehensively evaluate four deep IQAs on the same five datasets, and the experimental results show that image scale significantly influences IQA performance. We found that the most appropriate image scale is often neither the default nor the original size, and the choice differs depending on the methods and datasets used. We visualized the stability and found that PieAPP is the most stable among the four deep IQAs.
Teaching introductory applied statistics with R

This chapter discusses the benefits of teaching R coding simultaneously with introductory applied statistics. Despite a reputation for difficulty, R has a number of advantages for students. It is free, benefits from plentiful, freely accessible tutorials and support online, and provides students with highly marketable skills amid a challenging job market. This chapter discusses strategies for integrating R coding tutorials with course material on data management and visualization, probability, and inference for categorical and numerical data. The chapter also emphasizes best practices for managing workflow that allow students to create comprehensive replication archives increasingly required for publication in academic journals. The fundamental goal of this chapter is to provide instructors with ideas on how to teach students the fundamentals they need to continue learning on their own.
A Cloud Pub/Sub Architecture to Integrate Google Big Query with Elasticsearch using Cloud Functions

In recent years, the need for analytics on large volumes of data has become increasingly important. It turns out to be extremely useful in making strategic decisions about different applications. In this way, appropriate mechanisms must be designed to carry out data processing and integration with different platforms to take advantage of their best features. In this work, an architecture that works on cloud services is shown to migrate data stored in Big Query to an analytics engine such as Elasticsearch and take advantage of its potential in query, insert and display operations. This is accomplished through the use of Cloud Functions and Pub / Sub. The integration of these platforms through the proposed architecture showed 100% effectiveness when transferring data to another, maintaining an insertion rate of 4,138.30 documents per second, demonstrating its robustness, efficiency, and versatility when performing a data migration. This pretends to establish an architecture solution when it comes about handling a large amount of data as in the real world.
An Introduction to Federated and Transfer Learning

In today’s world, we have access to a tremendous amount of data. However, there is not enough high-quality data to obtain the desired results. More importantly, many industries have separate databases, with restricted sharing policies. This prevents the use of centralized storage of all relevant data in many applications for various reasons, such as privacy concerns and the need for fast computational results at the frontier. These problems can be addressed through transfer learning and federated learning. This book contains some chapters to provide background knowledge of transfer learning and federated learning, as well as, novel contributions to improve the performance of distributed learning systems.