Reviewerly/exHarmony huggingface dataset:
Novel PV Power Hybrid Prediction Model Based on FL Co-Training Method

Existing photovoltaic (PV) power prediction methods suffer from insufficient data samples, poor model generalization ability, and the inability to share power data. In this paper, a hybrid prediction model based on federated learning (FL) is proposed. To improve communication efficiency and model generalization ability, FL is introduced to combine data from multiple locations without sharing to collaboratively train the prediction model. Furthermore, a hybrid LSTM-BPNN prediction model is designed to improve the accuracy of predictions. LSTM is used to extract important features from the time-series data, and BPNN maps the extracted high-dimensional features to the low-dimensional space and outputs the predicted values. Experiments show that the minimum MAPE of the hybrid prediction model constructed in this paper can reach 1.2%, and the prediction effect is improved by 30% compared with the traditional model. Under the FL mode, the trained prediction model not only improves the prediction accuracy by more than 20% but also has excellent generalization ability in multiple scenarios.
Dynamic Access Control in Industry 4.0 Systems

Industry 4.0 enacts ad-hoc cooperation between machines, humans, and organizations in supply and production chains. The cooperation goes beyond rigid hierarchical process structures and increases the levels of efficiency, customization, and individualisation of end-products. Efficient processing and cooperation requires exploiting various sensorand process data and sharing them across various entities including computer systems, machines, mobile devices, humans, and organisations. Access control is a common security mechanism to control data sharing between involved parties. However, access control to virtual resources is not sufficient in presence of Industry 4.0 because physical access has a considerable effect on the protection of information and systems. In addition, access control mechanisms have to become capable of handling dynamically changing situations arising from ad-hoc horizontal cooperation or changes in the environment of Industry 4.0 systems. Established access control mechanisms do not consider dynamic changes and the combination with physical access control yet. Approaches trying to address these shortcomings exist but often do not consider how to get information such as the sensitivity of exchanged information. This chapter proposes a novel approach to control physical and virtual access tied to the dynamics of custom product engineering, hence, establishing confidentiality in ad-hoc horizontal processes. The approach combines static design-time analyses to discover data properties with a dynamic runtime access control approach that evaluates policies protecting virtual and physical assets. The runtime part uses data properties derived from the static design-time analysis, as well as the environment or system status to decide about access.
Performance Study on CPU-based Machine Learning with PyTorch

Over the past decade we have seen a surge in research in Machine Learning. Deep neural networks represent a subclass of machine learning and are computationally intensive. Traditionally, GPUs have been leveraged to accelerate the training of such deep networks by taking advantage of parallelization and the many core architecture. As the datasets and models grow larger, scaling the training or inference task can help reduce the time to solution for research or production purposes. The Supercomputer Fugaku established state of the art results in multiple benchmarks in machine learning by scaling ARM based CPU technology. To that end, we study and present the performance of machine learning training and inference tasks on 64-bit ARM CPU architecture by exploiting its features namely the Scalable Vector Extensions (SVE) in the ARMv8-A.
User Experience: A Bibliometric Review of the Literature

User experience is a well-discussed topic in the field of human–computer interaction. To describe the status of research on this topic, a bibliometric analysis of publications concerning user experience from 2011 to 2021 was conducted using the Web of Science database. The main functions of Derwent Data Analyzer software include data cleaning, mining, and visualization. The historical trends of the year, leading countries, leading institutions, major contributors, and leading research fields and journals were investigated, and a keyword analysis of the highly cited literature was performed. The key findings are as follows. The United States, China, and Britain are the three most productive countries. Tsinghua University in China is the most productive organization, and Sungkyunkwan University in South Korea has the highest average citations per publication. User experience has attracted the attention of numerous scholars in 204 research fields, and computer science information systems is the main field. “Usability,” “virtual reality,” “human–computer interaction,” and “augmented reality” are the most used keywords. A recent trend is the focus on molecular biology.
Classification of Faults Operation of a Robotic Manipulator Using Symbolic Classifier

In autonomous manufacturing lines, it is very important to detect the faulty operation of robot manipulators to prevent potential damage. In this paper, the application of a genetic programming algorithm (symbolic classifier) with a random selection of hyperparameter values and trained using a 5-fold cross-validation process is proposed to determine expressions for fault detection during robotic manipulator operation, using a dataset that was made publicly available by the original researchers. The original dataset was reduced to a binary dataset (fault vs. normal operation); however, due to the class imbalance random oversampling, and SMOTE methods were applied. The quality of best symbolic expressions (SEs) was based on the highest mean values of accuracy (ACC¯), area under receiving operating characteristics curve (AUC¯), Precision¯, Recall¯, and F1−Score¯. The best results were obtained on the SMOTE dataset with ACC¯, AUC¯, Precision¯, Recall¯, and F1−Score¯ equal to 0.99, 0.99, 0.992, 0.9893, and 0.99, respectively. Finally, the best set of mathematical equations obtained using the GPSC algorithm was evaluated on the initial dataset where the mean values of ACC¯, AUC¯, Precision¯, Recall¯, and F1−Score¯ are equal to 0.9978, 0.998, 1.0, 0.997, and 0.998, respectively. The investigation showed that using the described procedure, symbolically expressed models of a high classification performance are obtained for the purpose of detecting faults in the operation of robotic manipulators.
STEM-APPROACH IN EDUCATION AND PREPARATION OF THE TEACHER FOR ITS IMPLEMENTATION

The article considers the essence and potential of STEM-education, the implementation of which is important for the training of specialists of the new generation and socio-economic development of our country. The core of the basic concepts of STEM-education as a new direction in education is revealed, the appearance of the acronym "STEM" is covered, its meaning is discussed, and the conceptual field of its synonymous categories is analyzed. The main advantages of this area and the main issues of STEM-approach implementation in the domestic education system are clarified. The experience of the world's leading countries (including the USA, Great Britain, and Canada) in the implementation and development of STEM-education is analyzed. The conclusion about efficiency of activity of the establishments of the general secondary education focused on STEM-technologies is made. The necessity of improving the education system of Ukraine focusing on the experience of STEM programs implementation in the above-mentioned countries is substantiated. Possibilities of application of STEM-technologies in the New Ukrainian School (NUS) are defined (at an initial stage of training in particular). The conclusion is made about the need to restructure the whole learning process, which presupposes moving away from the teacher-centered model of learning, giving students initiative, stimulating their activity, activating development of critical thinking and creativity. It is noted that the role of teachers in STEM-education is changing, which, in turn, raises the issue of training and retraining of teachers who could work in this direction and transfer the process of STEM-education from individual to mass. The need to provide educational institutions with appropriate material resources (LEGO designing sets, computers, new generation of textbooks, etc.) has been confirmed.
Analysis on the Reform of Higher Vocational Teaching Methods Under the Cultivation of Innovation Ability

With the continuous improvement of our country's modern education system, the role and status of higher vocational colleges are constantly improving. As an important educational base for cultivating technical and application-oriented talents, higher vocational colleges must do well in teaching reform. In the process of reform and development of higher vocational colleges, it is necessary to do well in cultivating students' innovative ability, adopting a scientific teaching model, strengthening the innovation of teaching methods, and continuously strengthening students' core literacy and innovation ability, which is an inevitable way to cultivate high-quality talents. Therefore, this paper will conduct in-depth research and analysis on the reform of higher vocational teaching methods under the cultivation of innovative ability, and summarize some measures based on practical experience, in order to help relevant personnel.
Application of Artificial Intelligence Chi-Square Model and Classification Of KNN in Heart Disease Detection

Cardiovascular disease is a problem in the blood vessels that do not run smoothly into the heart. This is fatal in patients with a history of heart disease. This problem often occurs in the flow of blood pumps into the heart. The problem examined in this study is how to complete the level of accuracy of each data set and the reduction of each attribute in heart disease. The purpose of this study is to analyze heart disease and classify heart disease using the chi-square and K-Nearest Neighbor algorithms. The results of the study with patient age 57, gender LK, cp 3, trestbps 200, chol 564, fbs 1, restecg 2, thalach 202, oldpeak 6.2, slope 2, ca 4, and the value of thal 3 for the target is there is disease heart 0 or 1 is detected without heart disease when the max-min data is normalized. while to measure the performance of the algorithm with the value of the confusion matrix with the actual class value of 1, prediction class 1 value 44, actual class 0 and prediction class value 6. while the actual class value 0-1, prediction class 1 value 5 and 0-0 value 36. the final stage value of the accuracy measure is 0.87912, the recall value is 0.89797 and the precision value is 0.85714. The implication of the application of the test has an optimal test, the accuracy value with data K = 303 then it can be concluded that based on the test the calculation of the KNN model obtained an accuracy of 91%
Multi-scale Contrastive Learning for Complex Scene Generation

Recent advances in Generative Adversarial Networks (GANs) have enabled photo-realistic synthesis of single object images. Yet, modeling more complex distributions, such as scenes with multiple objects, remains challenging. The difficulty stems from the incalculable variety of scene configurations which contain multiple objects of different categories placed at various locations. In this paper, we aim to alleviate the difficulty by enhancing the discriminative ability of the discriminator through a locally defined self-supervised pretext task. To this end, we design a discriminator to leverage multi-scale local feedback that guides the generator to better model local semantic structures in the scene. Then, we require the discriminator to carry out pixel-level contrastive learning at multiple scales to enhance discriminative capability on local regions. Experimental results on several challenging scene datasets show that our method improves the synthesis quality by a substantial margin compared to state-of-the-art baselines.
Image-Consistent Detection of Road Anomalies as Unpredictable Patches

We propose a novel method for anomaly detection primarily aiming at autonomous driving. The design of the method, called DaCUP (Detection of anomalies as Consistent Unpredictable Patches), is based on two general properties of anomalous objects: an anomaly is (i) not from a class that could be modelled and (ii) it is not similar (in appearance) to non-anomalous objects in the image. To this end, we propose a novel embedding bottleneck in an auto-encoder like architecture that enables modelling of a diverse, multi-modal known class appearance (e.g. road). Secondly, we introduce novel image-conditioned distance features that allow known class identification in a nearest-neighbour manner on-the-fly, greatly increasing its ability to distinguish true and false positives. Lastly, an inpainting module is utilized to model the uniqueness of detected anomalies and significantly reduce false positives by filtering regions that are similar, thus reconstructable from their neighbourhood. We demonstrate that filtering of regions based on their similarity to neighbour regions, using e.g. an inpainting module, is general and can be used with other methods for reduction of false positives. The proposed method is evaluated on several publicly available datasets for road anomaly detection and on a maritime benchmark for obstacle avoidance. The method achieves state-of-the-art performance in both tasks with the same hyper-parameters with no domain specific design.
Penerapan Model Pembelajaran Number Heads Together (NHT) Untuk Meningkatkan Hasil Belajar Siswa Vi SD Negeri Bantarjati 8 Kota Bogor Pada Materi Taksiran Keliling Dan Luas Lingkaran

This study aims to find out the application of the Numbered Heads Together Cooperative learning model can improve student learning outcomes in mathematics subjects about estimating the circumference and area of a circle in class VI SD Negeri Bantarjati 8 Bogor City Semester 1 of the 2019/2020 Academic Year. Also to describe the process of improving student learning outcomes in mathematics subjects about estimating the circumference and area of a circle after using the Numbered Heads Together learning model. As well as to measure the magnitude of the increase in student learning outcomes in mathematics subjects about estimating the circumference and area of a circle after using the Numbered Heads Together Type Cooperative learning model. The results of this study indicate that using the Numbered Heads Together Cooperative learning model can be a fun variation of learning for students so that it is proven to improve student learning outcomes. Before using the Numbered Heads Together Cooperative learning model, student learning outcomes only reached an average value of 65.56 then there was an increase after using the Numbered Heads Together Cooperative learning model to 71.11 in cycle 1 and 79.31 in cycle 2. concluded that the use of the Numbered Heads Together Cooperative learning model is fun so that there is an increase in student learning outcomes. Therefore, the researcher suggests that the use of the Numbered Heads Together Cooperative learning model be socialized and used as an alternative in learning mathematics in schools within the Bogor City Education Office.
Robust Method for Embedding an Image inside Cover Image based on Least Significant Bit Steganography

According to the enormous evolution of the internet and communications, the security of private or sensitive information has become the important issue. The private information can be passed between a sender and a receiver with high security using Cryptography and Steganography. By the cryptography, as the information can be secured with the cipher key for encryption. By the Steganography, the secret information can hide within usual data such as images, videos, audios. In this paper we proposed the robust method that includes two phases. The first phase consists of two stages. In the first stage we encrypt a secret image using encryption algorithm in order of increasing security of information. In the second stage, we complete the first stage for embedding the most significant bits (MSB) of the encrypted image into the least significant bits(LSB) of the cover image for instituting the stego image. The second phase is the obverse of the first phase, where, the encrypted image is retrieved from the stego image and then, decrypted process construct the secret image. The proposed method is efficient in terms of security compare with the other methods. Where, both encryption and embedding algorithms have an efficient role for embedding the secret image and preserving the good visual quality of stego images. Furthermore, extraction algorithms obtained better and faster results to a restoration of the secret image.
Performance Evaluation of Fusion Based Efficient Algorithm for Facial Expression Recognition

Objectives: To develop face expression recognition system using JAFFE database and to evaluate the performance of the face expression recognition models.Methods: This study used the FER model based on modified-HoG (Histogram of oriented gradient), LBP (Local Binary Patterns) and Fast Key point detector and BRIEF descriptor (FKBD) to extract the significant features of JAFFE dataset.The features extracted using HoG, LBP and FKBD techniques form a feature vector.Then, the fusion of all the features is carried out at the feature level.The multiclass SVM and KNN classifiers are used to recognize the facial expressions, effectively.Findings: In this work, an effort is made to develop a robust FER model using JAFFE database.It is recorded that, based on the experimental results, the proposed model suits better with a performance rate of 98.26% for SVM and 96.51% for KNN, when compared with the different state-of-the-art methods.Novelty: Many FER models have been developed and adopted for enhancing their quality and to extract the facial features using transform and frequency domains.It is observed that, maximum approaches are based on generating the texture features.The fusion at the feature level using modified HoG, LBP and FKBD is performed and the SVM model is more compatible when compared with other classifiers and it supports one-to-one and one-to-many comparisons' technique.
FASFLNet: feature adaptive selection and fusion lightweight network for RGB-D indoor scene parsing

RGB-D indoor scene parsing is a challenging task in computer vision. Conventional scene-parsing approaches based on manual feature extraction have proved inadequate in this area because indoor scenes are both unordered and complex. This study proposes a feature adaptive selection, and fusion lightweight network (FASFLNet) for RGB-D indoor scene parsing that is both efficient and accurate. The proposed FASFLNet utilizes a lightweight classification network (MobileNetV2), constituting the backbone of the feature extraction. This lightweight backbone model guarantees that FASFLNet is not only highly efficient but also provides good performance in terms of feature extraction. The additional information provided by depth images (specifically, spatial information such as the shape and scale of objects) is used in FASFLNet as supplemental information for feature-level adaptive fusion between the RGB and depth streams. Furthermore, during decoding, the features of different layers are fused from top-bottom and integrated at different layers for final pixel-level classification, resulting in an effect similar to that of pyramid supervision. Experimental results obtained on the NYU V2 and SUN RGB-D datasets indicate that the proposed FASFLNet outperforms existing state-of-the-art models and is both highly efficient and accurate.
RANCANG BANGUN APLIKASI PENCARIAN SLOT PARKIR KOSONG UNTUK KENDARAAN RODA EMPAT DENGAN PENDEKATAN COMPUTER VISION

Parking space is a facility that must be available in public places, both in shopping areas, offices, hospitals and other places. In general, parking lots only provide parking slots to occupy cars without a system that provides information to the driver about the availability of parking slots, often the driver must surround the parking area to determine whether there are still parking slots that can be occupied. Design and build this application using the Computer Vision method, with the help of CCTV cameras. In making this application, the reference object for detection is a four-wheeled vehicle and the detection uses the Structural Similarity Index Measurement (SSIM) method by taking photos of full parking slots and empty parking slots with four-wheeled vehicles. Then two sample photos will be compared with the threshold method. After getting information about the availability of parking slots, the data will be sent to the server which will then be forwarded by the android application in real time. The results of designing this application have several things that are very influential on the level of detection, namely the intensity of light and camera position. Resistance to light changes of at least 50% of the template that has been made, while the distance of the camera to the parking slot is very influential. The farther the camera the smaller the precision produced by the system.
Explainable AI Method for Cyber bullying Detection

People of all ages and genders are using social media platforms to engage themselves in all sorts of activities. People create profiles on online social networks in order to communicate with one another in this virtual environment. Hundreds or thousands of friends and followers are split across many profiles. Along with the virtual communication in this social media life, cyber-crimes also creep in many distinguished forms to grab user’s information and emotionally degrade them with harassment and arrogant behavior. A set of machine learning methods are proposed and used to detect such a bullying behavior. Along with the detection of such an act, the model should also provide the logical reasoning of the evidence extracted. The explain ability of the models classification will give us a view of the way towards portraying a suspect as a bullier. This paper illustrates a machine learning model that works on a twitter data set to suggest the tweets as category bullying or non-bullying. LIME a tool to predict the interpretability of the model is used to depict the performance of model and provides explainability.