Reviewerly/exHarmony huggingface dataset:
Prediction of hourly solar radiation in Indonesia using LSTM

The value of solar radiation has many advantages to support optimal utilization of solar energy, from building design, solar power plant and agricultural system. However, solar radiation is an element that will affect to other weather parameters. Forecasting systems that have been carried out by previous researchers are predictions for data per-hour, per-day, per-month and even per-year. All forecasting processes are carried out using different multivariate meteorological parameters. Fluctuations in solar radiation can occur due to data errors or moving fast of clouds. In this study, we are using meteorological parameter: solar radiation, sun duration, relative humidity, dew point, air temperature, and sky cover. All these parameters will be used to predict hourly solar radiation. Long short-term memory (LSTM) is proposed in our works to discover best activation: ReLU, sigmoid, tanh. As a result, RELU is best activation which has the smallest Mean Square Error (MSE) value is 0.1885, compared with other activation value: sigmoid MSE value is 0.2359 and tanh MSE value is 0.3270. By using ReLU, the wall time is 1min 51s and calculation process stops in 18 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> epoch. Optimum learning rate become 0.00249 and wall time 1min 38s. The output prediction is hourly solar radiation one-day ahead that we can used as a data solar radiation information.
Hybrid Method of Selection Features to Improve Performance of Covid-19 Classification

In this study, to improve the performance of the classification algorithm using the Hybrid feature selection method on covid-19 data, this study utilizes the SBS Filtering and Wrapper Technique, aiming to reduce the initial feature sub-space dimensions N (total features) to K (features). best). From the whole process of testing the Hybrid method by combining filtering and wrapper techniques, it can be concluded that from a total of 8974 features, after entering the filtering process, 184, then after applying the wrapper technique to 170 selected features, performance evaluation was carried out and obtained SVM performance results with data The big one is with an accuracy of 83.8% and testing on KNN by testing the parameter value K = 5, getting an accuracy result of 79.5%, the classification of the K value is determined by the researcher. The overall precision comparison is KNN with a precision value of 32.6% and SVM with a precision value of 87.6%, recall with a KNN result of 14.2%, and SVM of 20.1%, a comparison of F1-Score KNN of 17.3% and SVM of 27.5%.
Solar Power Prediction Based on Weather Forecast

Prediction of an evolving system behavior based on a sparse historic data set is hardly achievable with current artificial intelligence concepts, even if using massive computing power. This paper presents a novel prediction technique for such difficult cases. The technique is based on local regression and dimensional splitting of the parameter space. As an application, the power generated by a generic solar cell is predicted, based on the weather forecast obtained from public sources. The algorithm maintains a good balance between generating reasonably good predictions and keeping the computation needs very low. Several common prediction strategies are compared, proving the superiority of an adaptive approach. Measurements on an experimental device are used to validate the prediction technique.
Principles of building personalized intelligent human assistants

The article describes the principles of building intelligent human assistants based on the concept of co-evolutionary hybrid intelligence. The idea of the concept is the mutual development of human and artificial intelligences as a single indivisible organism. From this concept follows the necessity of continuous mutual construction and improvement of models of the state and behavior of humans and artificial intelligence. The latter is possible under the condition of personalizing a system of co-evolutionary hybrid intelligence. The principles of building such systems are illustrated by the example of intelligent doctor-patient assistants.
Approximate Model Checking based on Deep Forest

Some classical Machine Learning (ML) algorithms have been applied to predict model checking results, while a data set contains several thousands of samples. However, the power of prediction will reduce sharply when the scale of dataset is bigger. To this end, some Deep Learning (DL) algorithms are employed in this study. First, a part of samples are inputted to a DL algorithm. Second, the obtained DL model can be used to predict model checking results. Our experiments demonstrate that Deep Forest (DF) has the better performance when one million samples are used, compared with the classical ML algorithms and the deep learning based on deep neural network.
Automatic Term Extraction with Joint Multilingual Learning

Automatic term extraction using deep learning achieves promising results if sufficient training data exists. Unfortunately, some languages may lack these resources in some domains causing poor performance due to under-fitting. In this study, we propose a joint multilingual deep learning model with sequence labeling to extract terms, trained on multilingual data and aligned word embeddings to tackle this problem. Our evaluation results demonstrate that the multilingual model provides an improvement for automatic term extraction task when it is compared with a monolingual model trained with limited training data. Although the improvement rate varies according to domain and the size of the data, our evaluation shows that the highest improvement in F1-score is 10.1 % in the domain of Computer Science, the least improvement is 7.6 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> in the domain of Electronic Engineering. Our multilingual model also achieves competitive results when it is compared with a monolingual model trained with sufficient training data.
Stabilised Inverse Flowline Evolution for Anisotropic Image Sharpening

The central limit theorem suggests Gaussian convolution as a generic blur model for images. Since Gaussian convolution is equivalent to homogeneous diffusion filtering, one way to deblur such images is to diffuse them backwards in time. However, backward diffusion is highly ill-posed. Thus, it requires stabilisation in the model as well as highly sophisticated numerical algorithms. Moreover, sharpening is often only desired across image edges but not along them, since it may cause very irregular contours. This creates the need to model a stabilised anisotropic backward evolution and to devise an appropriate numerical algorithm for this ill-posed process.We address both challenges. First we introduce stabilised inverse flowline evolution (SIFE) as an anisotropic image sharpening flow. Outside extrema, its partial differential equation (PDE) is backward parabolic in gradient direction. Interestingly, it is sufficient to stabilise it in extrema by imposing a zero flow there. We show that morphological derivatives – which are not common in the numerics of PDEs – are ideal for the numerical approximation of SIFE: They effortlessly approximate directional derivatives in gradient direction. Our scheme adapts one-sided morphological derivatives to the underlying image structure. It allows to progress in subpixel accuracy and enables us to prove stability properties. Our experiments show that SIFE allows nonflat steady states and outperforms other sharpening flows.
Design of civil aviation security check passenger identification system based on residual convolution network

INTRODUCTION: A civil aviation security check passenger identification system based on residual convolution network is designed to improve the efficiency of airport passenger security check service. OBJECTIVES: The system uses the basic resource layer to provide communication and configuration services, collects the basic information of passengers, the images of passengers' faces and whole body, and the images of baggage security X-ray machine through the data layer, and stores the collected results in the unstructured database; METHODS: The image processing module of the business service layer calls the data in the database, and takes the STM32F103VBT6 microprocessor as the image processing control chip to complete the image data processing. The person, baggage, X-ray machine image and passenger basic information are associated through the person, baggage and X-ray machine information binding service module, and the association results are uploaded to the person and certificates integration unit of the client application layer. RESULTS: The face recognition module identifies the passenger identity through the residual convolution network with the attention mechanism, and realizes the ReID identification of passengers and baggage and the association of people and baggage through the transmission control unit. CONCLUSION: The experimental results show that the system can accurately identify the identity of civil aviation security passengers, and the identification efficiency of security passengers can reach more than 27 frames per second.
Secured Storage Mechanism for the Cloud-Based Banking Applications Using ECC

The security of data becomes more vulnerable despite the various safety measures. The significant contribution of the chapter is to describe the design of a secured Cloud storage mechanism using ECC. Many banks are using cloud technology. However, it is not free from hacks and threats. Here comes the focus on Cloud Security and a lot of research work is happening around it. ECC-based secured cloud framework with a logging mechanism provides a high-level design of the SSL VPN and secured cloud mobile banking. It helps to secure data transfer for users. Software applications and web services are handled by maintaining the log files securely, and this security scheme shields the aligning of different kinds of data elements to each provider using an ECC algorithm. It is implementing a two-layered security system, which includes both private and public cloud storage environments, also providing a customized secure logging mechanism that will be encrypted by ECC. The TRA (banking) customers will get lots of benefits from this cloud storage mechanism. The proposed design acts like a buffer for end users, Internet service providers, and Banks, so it is more effective and secure in the Cloud environment.
Review on Data Confidentiality Issues of User’s Information on Online Social Networks

Online social networking has become an integral component of human life. Many individuals, on the other hand, user are unaware of the security and privacy concerns that come along with its use. As a result of social media sites like Facebook and Twitter, people’s personal information, such as their date of birth and phone number, profile photos, etc. might be dangerously exposed. To get access to private information, such as a user’s banking credentials or to launch a security attack, hackers first obtain the user’s public information from their social media posts. Assaults or leaks of personal information might have a significant impact on their daily life. In this day and age of cutting-edge technology, internet users must understand the risks of using social media websites like Facebook and Twitter. The current status of online social networks, their hazards, and possible solutions are examined in depth in this paper. In this review paper, we deliberately focus on the privacy and security challenges accompanying with OSNs illustrate some models of attack by the attackers and investigate some techniques used to secure a user’s private information and prevent it from attackers while leaving the privacy destruction mostly unresolved. In the end, we are proposing a blockchain-based framework for decentralised that provides advanced security and privacy to OSN users.
A Study on the Relationship between Mindset, Grit, and Perceived Performance of Taekwondo Trainees

This study is to explore the effect of Taekwondo practitioners" growth mindset on perceived performance and the mediating effect of grit in this relationship. Methods, The questionnaire response data of 237 taekwondo practitioners were collected, and the collected data were used in SPSS Ver. 22.0 Using the program, frequency analysis, exploratory factor analysis, reliability analysis, multiple regression analysis, and step-by-step regression analysis were performed. Result, Presenting the derived research results are as follows. First, the growth mindset of taekwondo practitioners improves perceived performance. Second, the passion among the grit of Taekwondo practitioners completely mediated the relationship between growth mindset and perceived performance improvement. Conclusion, Taekwondo trainees" efforts and instructor"s efforts to develop a growth mindset training program for Taekwondo trainees should be made, and self-reflection and empowerment efforts should be made to foster passion and pride in Taekwondo.
The relationship between Major Satisfaction and Career Exploration Behavior of Dance major Student

The purpose of this research was to identify a relationship between major satisfaction and career exploration behavior of dance major student. To achieve the goal, samples were collected from Universities from Seoul metropolitan area with convenience sampling method. Collected data were analyzed by SPSS 24.0 statistical program using frequency analysis, correlation analysis, and multiple linear regression analysis. The results derived from this research are as follows, First, it was confirmed that subject satisfaction and relationship satisfaction had a significant effect on self-exploration behavior among dance major college students. Lectures that meet students
Bottom Sediments as an Indicator of the Geoecological State of Natural Water Currents

The parameters of the natural geochemical background of ecosystems of the Lunnoye deposit area were studied and the geoecological conditions of large and small watercourses of the Yukhukhta — Yukungra interfluve were assessed according to the state of the total pollution index of bottom sediments. 27 elements of the gross content were determined by the method of emission spectral analysis. The elements that determine the geochemical specificity of the territory were revealed: Bi, Li, Sn, Y, Be, Sc, K, Th, Cs and Ra. At the time of research, the state of the surface watercourses of the territory is assessed as moderately polluted. With further monitoring, special attention should be paid to the state of the river basin Yukhukhta and especially the dynamics of changes in the chemical composition of bottom sediments of the brook. Lunny and Mramorny. The elements K, Th, Cs, Ra, Be, Zn, Pb, As, Cr, and Ni are proposed as parameters for monitoring the geoecological state of bottom sediments.
Computer vision in Autonomous Intelligent Vehicles (AIV)

Autonomous intelligent vehicle (AIV) is no longer a science fiction as it has been realized in today&apos;s era of the computing world. The research and development on autonomous vehicles have been going on for decades with constructive outcomes. Autonomous vehicles have expanded their areas of applications apart from the transportation sector. The internal structure and major components of the vehicle play a crucial role in making it autonomous. After the sophisticated hardware and sensors used in them, they are required to be learned and trained to mimic the driving style of a normal human driver. For this purpose, artificial intelligence and its subsets have played a pivotal role, without which the idea of self-driving vehicles would have just remained abstract. Software tools provided by machine learning can help assist an AIV in scene comprehension, decision-making, and planning. Deep learning algorithms like long short-term memory can be implemented to make such vehicles more intelligent and take decisions just like a human brain does. Besides, if not applicable for transporting people, AIVs have been used as robotic vehicles to transport goods and carry out tasks in industries. Computer vision can immensely help AIVs and could potentially help in reaching higher levels of autonomy. Nonetheless, with all the advantages, autonomous vehicles also suffer from various issues and challenges which still remain to be improvized to achieve the far-fetched dream of fully automated driverless vehicles.
Joint Classification and out-of-Distribution Detection Based on Structured Latent Space of Variational Auto-Encoders

Deep neural networks (DNNs) are very efficient at object recognition but sometimes have an overestimated reliability for erroneous decisions. It is therefore very important to check if the distribution of images to be processed matches that of the training set. This has been studied as out-of-distribution (OoD) detection. This paper proposes an algorithm allowing classification as well as OoD detection, based on the generative classifier (GC) paradigm.While a discriminative classifier (DC) learns p(y|x) to achieve classification and a generative network learns p(x) to achieve OoD detection, the GC learns p(x|y) to achieve classification as well as OoD detection. Since generative models such as variational auto-encoders (VAEs) show good performance for unsupervised OoD detection, we propose to structure the latent variable of a VAE with a gaussian mixture model (GMM) while building a GC. The proposed model <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> is shown to outperform classical approaches based on DCs for far OoD and generative adversarial network (GAN) based models for near OoD without losing any performance for the classification task.
Image Compression Based on Importance Using Optimal Mass Transportation Map

Demand for efficient image transmission and storage is in-creasing rapidly because of the continuing growth of multi-media technology and VR and AR applications. In this paper, we proposed an image compression method based on the recognition of importance of regions in images. As not all the information in an image is equally useful, we can identify important regions in an image for high fidelity compression and accept a comparatively more lossy compression about less important regions of the image. First, we segment images to two parts, namely, foreground and background, where the foreground represents the more important component and the background is of less importance. Second, we apply optimal mass transportation mapping in a GAN (generative adversarial network) framework to both the foreground and back-ground to magnify the foreground and shrink the background while keeping the shape and total image area unchanged. As a result, in the processed image, the ratio of foreground to background is larger than the corrresponding ratio in the original image. This ratio is controllable in our process, giving users the ability to control the degree of compression. The GAN-processed image is then used for compression. To restore the image, we apply a GAN model to the compressed image and recover the ratio of foreground and background using an optimal mass transportation map. Test results show that our method is highly effective in reconstructing detail of important components in compressed images while achieving a high compression ratio.
Revisiting Click-Based Interactive Video Object Segmentation

While current methods for interactive Video Object Segmentation (iVOS) rely on scribble-based interactions to generate precise object masks, we propose a Click-based interactive Video Object Segmentation (CiVOS) framework to simplify the required user workload as much as possible. CiVOS builds on de-coupled modules reflecting user interaction and mask propagation. The interaction module converts click-based interactions into an object mask, which is then inferred to the remaining frames by the propagation module. Additional user interactions allow for a refinement of the object mask. The approach is extensively evaluated on the popular interactive DAVIS dataset, but with an inevitable adaptation of scribble-based interactions with click-based counterparts. We consider several strategies for generating clicks during our evaluation to reflect various user inputs and adjust the DAVIS performance metric to perform a hardware-independent comparison. The presented CiVOS pipeline achieves competitive results, although requiring a lower user workload.
Deep Learning Approach for Blur Detection of Digital Breast Tomosynthesis Images

Image quality is critical in domains such as computer vision, image processing, and pattern recognition.One of the areas of image processing where image quality is critical is image restoration.In the field of medical imaging, blur detection is used in the pre-processing stage of medical image restoration.It was noted that blurring has the potential to obscure small cancers and microcalcifications.As a result, some abnormalities were undiscovered until they have grown significantly.The quality of an image can be determined whether it is blurry using various blur detection algorithms.This paper presents a comparative study of various pre-trained convolutional neural networks (CNNs) models as feature extraction for blur detection.The CNNs models are ResNet18, ResNet50, AlexNet, VGG16 and InceptionV3.These CNNs were then connected to a classifier known as support vector machine (SVM) to classify DBT images into blurry or not blurry.To evaluate the performance of the pre-trained CNN as features extractor, statistical performance measures namely the accuracy, receiveroperating characteristics (ROC), area under the curve (AUC), and execution time were employed.According to the evaluation results, InceptionV3 has the best accuracy rate at 0.9961 with AUC of 0.9961.Most of the output of Pre-trained CNN with SVM lies closest to the ideal ROC curve near the top left corner.AlexNet has the shortest processing time of any of the CNNs model.The findings of this study might be used as reference before performing image restoration.
Developing RDEVS Simulation Models from Textual Specifications

The Routed DEVS (RDEVS) formalism provides a formalization for routing process simulation. This paper presents the mapping between constrained network models obtained from textual specifications of routing processes and RDEVS simulation models implemented in Java. The proposal is part of a work-in-progress intended to develop M&amp;S software tools for the RDEVS formalism using well-known abstractions to get the computational models attached to such abstraction models through conceptual mapping. Then, modelers can have simulation models without needing to codify any routing implementation. Benefits are i) reduction of implementation times and ii) simulation model correctness regarding the RDEVS formalism.
Enhancing Network Security using Digital Image Processing, Cryptography &amp; Steganography

Quite often, we communicate exceedingly confidential information over a communication network that we want only the recipient to know. How do we protect such information from malicious attacks? Techniques like steganography, encryption and cryptography can be used to keep our data confidential.But, a combination of these techniques would help us keep our data highly confidential while nullifying the short comings of each of them when used individually. In this paper, we address the problem of security breaches using digital image processing. We know that email service providers sell our data to third party companies for their usage and profits. In today’s scenario, security is a major concern while transmitting any information over the network. Security provided by the network is insufficient with the increasing rates of cybercrimes. Therefore, we need to employ other techniques to carefully send our data over the network. In this paper, we explore a number of security techniques that can be combined together to ensure higher levels of security to our data.