Reviewerly/exHarmony huggingface dataset:
Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids

In this paper, we introduce a model representing the key characteristics of high frequency variations of solar irradiance and photovoltaic (PV) power production based on Clear Sky Index (CSI) data. The model is suitable for data-driven decision-making in electrical distribution grids, e.g., descriptive/predictive analyses, optimization, and numerical simulation. We concentrate on solar irradiance data since the power production of a PV system strongly correlates with solar irradiance at the site location. The solar irradiance is not constant due to the Earth’s orbit and irradiance absorption/scattering from the clouds. To simulate the operation of a PV system with one-minute resolution for a specific coordinate, we have to use a model based on the CSI of the solar irradiance data, capturing the uncertainties caused by cloud movements. The proposed model is based on clustering the days of each year into groups of days, e.g., (i) cloudy, (ii) intermittent cloudy, and (iii) clear sky. The CSI data of each group are divided into bins of magnitudes and the transition probabilities among the bins are identified to deliver a Markov Chain (MC) model to track the intraday weather condition variations. The proposed model is tested on the measurements of two PV systems located at two different climatic regions: (a) Yverdon-les-Bains, Switzerland; and (b) Oahu, Hawaii, USA. The model is compared with a previously published N-state MC model and the performance of the proposed model is elaborated.
Linguoecological Assessment of the Readability of Russian Longreads (Statistical Approach)

The study is devoted to the use of a mathematical model for calculating the readability index of a text as one of the tools for linguoecological assessment of a journalistic text. The research material was 20 longreads published in Russian electronic editions. The specificity of the research object lies in the fact that it is both a kind of journalistic discourse and an element of the digital space. In the study formal mathematical and statistical research methods were used, based on the determination of readability values, i.e. ease of human perception of texts. A linguoecological assessment of Russian-language longreads was carried out on the basis of the universal readability indices, adapted to the Russian language, – the Flesch and the Flesch-Kincaid indices. The results obtained showed that longreads designed for a wide mass readership have a readability index ranging from “medium” to “very high”. Texts in which oral speech is actively transmitted have a high readability index. The author of the article concludes that this method should be used in the initial assessment of a journalistic text at the stage of editing the material, as well as in the framework of the linguoecological examination of significant publications. The study of the statistical parameters of journalistic materials made in this genre will help not only to assess the linguoecological situation in the space of modern virtual media, but also to include the results obtained in the overall picture of the analysis of the linguoecological situation in the Russian-speaking virtual space.
New Normal After Education Game Design Pandemic Uses Approaches Agile Scrum Application of Finite State Machine (FSM) Method

Game is an application that infers human behavior into software, and players who play the game feel they are entering the game world. Games can be used as a medium for information and education. The current rise of Covid-19 requires every individual to implement health protocols in their daily life. This term is called new normal. New normal is a change in behavior to continue carrying out normal activities but with the addition of implementing health protocols to prevent Covid-19 transmission. Unfortunately, in everyday life, the application of the new normal is less effective so that in one area, new cases have emerged and the PSBB must be re-applied. The new normal themed educational game can be a fun information medium for the community, with the New Normal After Pandemic educational game, it is hoped that it can educate the public in implementing health protocol rules. This game was created using Construct 2 software because it is based on HTML 5 which is specifically for 2D platforms. 2D designs are created using Corel Draw X7, Easy Paint Tools SAI and Adobe Photoshop. The final result of this study is a game application that applies the FSM (Finite State Machine) control system design method in each level rule and uses the Agile Scrum system development method with a process of 2x sprint iterations
IEEE Access Special Section Editorial: Blockchain Technology: Principles and Applications

Blockchain is a disruptive technology for building consensus and trust in a peer-to-peer network without centralized control. It was first used in bitcoin, the very first cryptocurrency released at the beginning of 2009, to implement a secure ledger of transactions. This secure ledger ensures that once a transaction is placed in the ledger, it cannot be altered without being detected, which is a prerequisite for any digital currency implementation because it must guarantee that no one can double-spend one’s money. The interest in blockchain exploded in recent years. The number of publications indexed by Web of Science on blockchain increased from 2 in 2013 (the first year in which publications on blockchain started to appear), to 4 in 2014, to 21 in 2015, to 118 in 2016, to 521 in 2017, to 1954 in 2018, to 3667 in 2019, and to 3945 in 2020. As of June 29, 1569 articles have been published in 2021.
Elderly Falling Distinguish with Empirical Mode Decomposition-Derived Entropy Features

Fall risk prediction is an important issue for the elderly.A center of pressure signal, derived from a force plate, is useful for the estimation of body calibration.However, it is still difficult to distinguish elderly people's fall history by using a force plate signal.In this study, older adults with and without a history of falls were recruited to stand still for 60 s on a force plate.Forces in the x, y and z directions (Fx, Fy, and Fz) and center of original signals and EMD-derived signals were extracted.Results showed that features extracted from the raw COP data did not differ significantly between the fall and non-fall groups.There were 10 features extracted using EMD, with significant differences observed among fall and non-fall groups.These included four features from COPx and two features from COPy, Fx and Fz.
IMPLEMENTASI PROJECT BASED LEARNING UNTUK MENGEKSPLORASI KEMAMPUAN COMPUTATIONAL THINKING MAHASISWA

Computational Thinking (CT) or Computational Thinking is indispensable in mathematics. Programming Language courses are expected to facilitate and develop students' CT skills. The Project Base Learning (PjBL) learning model is used to see students' CT abilities. The project carried out in this PjBL learning is the making of learning media using Matlab. The media produced by the students has met the aspects of fluency in use, script accuracy, and flowchart logic. Students' CT abilities are in good category from the aspects of abstraction, logarithmic thinking, debugging / evaluation, and generalization because they have met the percentage of achievement of 82%.
Deep Learning for Handwritten Text Recognition (ConvNet & RNN)

In computing, Handwritten Text Recognition refers to the ability and method of a computer to recognise and understand legible handwriting acquired from a variety of sources, including photos, scanned documents, and other sources. Machine learning techniques like handwritten text recognition are used to create patterns. To achieve pattern realisation, input or objects are organised or categorised into a small number of groups or categories from a vast number of possible options.Historically, handwriting identification methods depended on handmade characteristics and a significant quantity of preexisting knowledge. Training an Handwritten Text Recognition system utilising the standard methods and a varied collection of rules and criteria is a difficult job. Numerous research on handwriting recognition have been conducted in recent years, with a special focus on deep learning methods that have produced breakthrough results. Despite this, the increasing amount of handwritten data and the new strides in computing power are very much need  to increase the actual accuracy achieved in properly recognising written text, which necessitates further study. Consonant with the fact that convolutional neural networks (CONVNETs) algorithms are extremely efficient at extracting structure from images, they are also extremely capable of identifying handwritten textual characters/words in ways that facilitate automatic recognition of unique characteristics. As a result, CONVNETs algorithms are the most appropriate method for addressing handwritten text recognition challenges. This technique will be used to recognise text in a number of different forms. Handwriting has developed into a more sophisticated art form, as shown by the existence of a wide variety of handwritten textual characters, including numbers, characters, scripts, and symbols, in both English and other languages.
Defining the Semantics of Abstract Argumentation Frameworks through Logic Programs and Partial Stable Models (Extended Abstract)

Extensions of Dung’s Argumentation Framework (AF) include the class of Recursive Bipolar AFs (Rec-BAFs), i.e. AFs with recursive attacks and supports. We show that a Rec-BAF \Delta can be translated into a logic program P_\Delta so that the extensions of \Delta under different semantics coincide with subsets of the partial stable models of P_\Delta.
CRSA: A Cryptocurrency Recovery Scheme Based on Hidden Assistance Relationships

As cryptocurrency and blockchain-related assets become more common in our digital society, there is a corresponding need to secure our digital assets, including the private keys used to secure access to such assets (e.g., due to loss or corruption of the data storage medium). However, there are limitations in existing blockchain-related asset management and recovery methods. Therefore, we use zero-knowledge proof to design a cryptocurrency recovery scheme based on hidden assisting relationships (hereafter referred to as the CRSA scheme) to facilitate the recovery of blockchain assets. Specifically, when the user's private key is lost, and access to the assets cannot be obtained, the user leverages information such as the pre-defined list of assistants to authenticate himself/herself on the blockchain. Once the assistants have confirmed the legitimacy of the user's authentication request, the asset will be transferred from the old address to the new address. During the (identity) proof process, the zero-knowledge proof is used to ensure that the identification of assistants is not leaked to other nodes, assistants, and the adversary. We provide the formal definition of the above scheme and the security proof of the construction. We also implement a prototype of the system and evaluate its performance. Evaluations indicate that the time required for the zero-knowledge proof is less than 10s, and the block verification time is less than 100ms.
Encryption on Multimodal Biometric using Hyper Chaotic Method and Inherent Binding Technique

Chaotic maps are non-convergent and highly sensitive to initial values. The applications include secure digital identity in distributed systems. Face and fingerprint biometric templates are subjected to hyper-chaotic map leading to encrypted image. Encrypted image is fed as an input to deoxyribonucleic acid (DNA) sequence. Dimensionality of generated DNA sequence is reduced by hashing. The intra-variation for a subject is measured with inter-quartile range. Image set with minimal variation value is identified for selecting the consistent image of a subject. 256 bit Key is generated from the consistent image. Generated key is reduced to 128 bits by eliminating subject specific outliers and redundant values. User specific features are extracted for both the traits using ResNet 50 convolutional neural network and are fused by addition. Final key is bound to feature vector by permutation function and time taken towards key binding is estimated with benchmark database SDUMLA-HMT. Outcome reveals that time taken for key binding varies between 45ms and 58ms for an image of size 80 MB.
Tool- and Domain-Agnostic Parameterization of Style Transfer Effects Leveraging Pretrained Perceptual Metrics

Current deep learning techniques for style transfer would not be optimal for design support since their "one-shot" transfer does not fit exploratory design processes. To overcome this gap, we propose parametric transcription, which transcribes an end-to-end style transfer effect into parameter values of specific transformations available in an existing content editing tool. With this approach, users can imitate the style of a reference sample in the tool that they are familiar with and thus can easily continue further exploration by manipulating the parameters. To enable this, we introduce a framework that utilizes an existing pretrained model for style transfer to calculate a perceptual style distance to the reference sample and uses black-box optimization to find the parameters that minimize this distance. Our experiments with various third-party tools, such as Instagram and Blender, show that our framework can effectively leverage deep learning techniques for computational design support.
High Resolution Information Reserved Anchor-Free Detection Algorithm

<p indent=0mm>Recently, object detection has gradually become a critical domain in the visual research community, while it’s also challenging to locate objects of different scales accurately for detectors. For the application of detecting objects in images, a high resolution anchor free (HOAR) detection strategy is proposed to meet the challenge of variable object scales. HOAR first inputs the image into a parallel high-resolution network with multiple paths (which is corresponding to different scales). Then the output feature map is extracted from each path as the deep feature representation of the image at each scale. Next, the dense feature pyramid (DenseFPN) is adopted to fuse the information of these feature maps to obtain the re-combined feature maps of multiple scales. Finally, the anchor-free detection sub-network is used to determine the object’s category and rectangle box position of each point on these feature maps. To verify the effectiveness of HOAR, comparative experiments are carried out on COCO dataset. First, the results on ablation study show the necessity of each module in the HOAR strategy. Second, the evaluation metric of detection, namely mAP, of HOAR reaches 40.5 on the validation set, which is significantly higher than mAP of all baseline models and some SOTA methods. In addition, the size of model parameters of HOAR strategy is significantly less than that of baseline models.
Deep Residual Reinforcement Learning (Extended Abstract)

We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algorithms, yielding a residual version of DDPG that significantly outperforms vanilla DDPG in commonly used benchmarks. Moreover, we find the residual algorithm an effective approach to the distribution mismatch problem in model-based planning. Compared with the existing TD(k) method, our residual-based method makes weaker assumptions about the model and yields a greater performance boost.
Research on the Innovative Application of Interactive Teaching Mode in the Post-PC Era

With the continuous development of society and technology, portable computer and intelligent communication device through cable or wireless Internet service have integrated into people's life under the post-personal computer era.In this new type of social intelligent environment, the traditional interactive teaching mode and methods have been incompatible with present needs.According to the preparation before class, teaching in class, assignments and interaction as well as common intelligent communication equipment and network application methods in the postpersonal computer era, this paper makes analysis and research on the improvement and innovation of teaching method in the "interactive" teaching of distance education method under the influence of post-personal computer era.
Internet of things based vital signs monitoring system: A prototype validity test

The advent of the internet of things (IoT) has resulted in an upsurge in the deployment of digital health care systems enabling patients’ health conditions to be remotely monitored. This article presents an intelligent and automated IoT-based vital signs monitoring system to aid in patient care. A the oretical framework was established to guide the development of a prototype. It encompasses the patient, IoT sensors, input and storage unit and data processing, analysis and data transmission. The prototype is equipped with the capability of sensing a patient’s body temperature, heart rate, and respiration rate in real time and transmits the data to a cloud data repository for storage and analysis. Alerts are sent to caregivers using SMS, email and voice calls where urgent attention is required for the patient. The voice call isto ensure a caregiver does not miss the alert since SMS and email may not be checked on time. To ensure privacy of patients, a caregiver has to be biometrically verified by either fingerprint or facial pattern. The experimental results confirmed the accuracy of the data gathered by the prototype, privacy of patients is also guaranteed compared to other benchmark systems.
RANCANG BANGUN APLIKASI DETEKSI TEPI CITRA DIGITAL MENGGUNAKAN ALGORITMA PREWITT

Edge detection is one of the algorithms used in Digital Image Processing. This algorithm serves to identify the line/edge of the image object to highlight the boundary lines of the image information. Edge is a set of connected pixels (connected pixels) that restricts the objects contained in the image. Senses the eye is one that is used by humans to see.However, the human eye has its limitations in capturing the electromagnetic signals.Therefore, created a computer or imaging machine that can capture almost the entire signal elektromagnetic. Imaging machines can work with imagery from sources that do not fit, do not fit or can not be captured by human vision. This is why digital image processing have very wide usefulness. Image processing technology can fit into a variety of fields such as medicine, geology, marine , industrial and others.Keywords:Image,Prewitt,Edge,Java,Netbeans
A Digital Thesaurus of Ethnic Groups in the Mekong River Basin

This research was aimed at constructing a thesaurus of the ethnic groups in the Mekong River Basin that is a compilation of controlled vocabularies of both Thai and English language, with a digital platform that enables semantic search and linked open data. The research method involved four steps: (1) organization of knowledge content; (2) construction of the thesaurus; (3) development of a digital thesaurus platform; and (4) evaluation. The concepts and theories used in the research comprised knowledge organization, thesaurus construction, digital platform development, and system evaluation. The tool for developing the digital thesaurus was the Tematres web application. The research results are: (1) there are 4273 principle words related to the ethnic groups that have been compiled and classified by the terms for each of the eight deep levels, 2596 were found to have hierarchical relationships, and 6858 had associative relationships; (2) the digital thesaurus platform was able to manage the controlled vocabularies related to the Mekong ethnic groups by storing both Thai and English vocabularies. When retrieved, the vocabulary, details of the broader term, narrow term, related term, cross reference, and scope note are displayed. Thus, semantic search is viable through applications, linked open data technology, and web services.
Siamese Sinir Ağı One-Shot Öğrenmeyi Kullanarak İmza Doğrulama

With the acceleration of digitalization in all areas of our lives, the need for biometric verification methods is increasing. The fact that biometric data is unique and biometric verification is stronger against phishing attacks compared to password-based authentication methods, has increased its preference rate. Signature verification, which is one of the biometric verification types, plays an important role in many areas such as banking systems, administrative and judicial applications. There are 2 types of signature verification, online and offline, for identifying the identity of the person and detecting signature forgery. Online signature verification is carried out during signing and temporal dynamic data are available regarding the person's signature. Offline verification is applied by scanning the image after signing, and this verification is limited to spatial data. Therefore, the offline signature verification process is considered a more challenging task. In this study, offline signature verification, independent of the writer, based on One-Shot Learning, was performed using Siamese Neural Network. Due to the fact that the Deep Convolution Neural Network requires a large amount of labeled data for image classification, real and fake signature distinction has been achieved by using the One-Shot Learning method, which can perform a successful classification by using less numbers of signature images. As a result of the experiments conducted on signature datasets, using the Siamese architecture, the proposed approach achieved percentage accuracy of 93.23, 92.11, 89.78, 91.35 verification in 4NSigComp2012, SigComp2011, 4NSigComp2010 and BHsig260 respectively.
A Survey on Emotion Detection from Text in Social Media Platforms

This paper provides an overview of the evolving field of emotion detection and identifies the current generation of methods of emotion detection from social media platforms as well as the challenges. The challenges in the field of current emotion detection are discussed in detail and potential alternatives are proposed to enhance the ability to detect emotions in real-life systems that emphasize interactions between humans and computers as well as advertisements, recommendation systems, and medical fields such as computer-based therapy. These solutions include the extraction of semantic analysis keywords, and ontology design with the evaluation of emotions. There are multiple models and classifications of emotions such as Ekman’s model (Happy, Anger, Sad, Disgust,Fear, Surprise), and Plutchik’s model (anger-fear, surprise-anticipation, joy-sadness, joy-sadness). Further, a systematic review of publications on textual emotions detection from social media platforms, state-of-the-art methods, and existing challenges presented. Finally, we conclude with some recommendations based on critical analysis of existing techniques and determine future research directions presented at last.