Reviewerly/exHarmony huggingface dataset:
Exact deep learning machines

Incorporating actual intelligence into the machines and making them think and perform like humans is impossible. In this chapter, a new kind of machine called the EDLM (exact deep learning machine) is introduced. Such EDLMs can achieve the target with probability one and could be the best alternative for originally designed artificial intelligence models of the mid-20th century by Alan Turing and others that have so far not seen reality. In the current context, achieving a target is defined as detecting a given object accurately.
cARica: Enhancing Travelling Experiences in Wonosobo Through Location-Based Mobile Augmented Reality

Wonosobo, as a Regency in Central Java Province, Indonesia, has attractions including the Dieng Plateau Theater Kalianget, and Menjer Lake. The research is intended to provide more experience for tourists who visit the tour through Location-Based Mobile Augmented Reality (MAR), an application we developed, cARica. This application includes experience travelling in Wonosobo and is aware of other information displayed through AR content. It was an alternative medium for tourism promotion to be easy, attractive, and inexpensive. It is a practical guide to attract tourists to visit tourist sites. In its development, we use the prototyping method so that each stage is carried out under the procedures that have been prepared. To get the point of Interest (PoI) points of tourist sites, use Global Positioning System (GPS) data taken through Google Maps to get the Latitude and Longitude of each object. The results of this study present that cARica is a Location-Based Mobile Augmented Reality service platform that can be accessed using an Android smartphone and has three-dimensional animated character content with the Wonosobo regency icon. cARica is a form of innovation in providing exceptional services and experiences for tourists and has the potential to be continuously developed.
Analysis of the Effect of Information Quality, System Quality, and Support Service Quality on User Satisfaction Levels and Its Implications for Blended E-Learning Continuance Intention to Use in the New Normal Era

Technology encourages e-learning to improve student learning and performance. The Covid-19 pandemic accelerated e-learning in Indonesian higher education. The learning method has transitioned from complete online learning to blended e-learning in new normal era. This research was conducted to determine the factors that influence students' intention to continue using blended e-learning using the IS Success Model. This research was conducted using a quantitative approach. Sampling was carried out by convenience sampling of 232 active students using blended e-learning in Indonesia. Data analysis was carried out with PLS-SEM. The hypothesis test shows that information, system, and support service quality affect blended e-learning student satisfaction. This research demonstrates that user happiness affects the intention to continue using blended e-learning
THE SEARCH WARRANT PROVISIONS OF THE CYBERCRIMES ACT AND THEIR RELATIONSHIP WITH THE CRIMINAL PROCEDURE ACT

The recently enacted Cybercrimes Act 19 of 2020 regulates the powers of the police and investigators to investigate cybercrimes. Chapter 4 of the Act provides for the powers of the police and others in respect of search, access or seizure in the investigation of cybercrimes and other offences committed by means of cybertechnology. The provisions of the Criminal Procedure Act 51 of 1977 will continue to operate in addition to the provisions of the Cybercrimes Act, to the extent that the Criminal Procedure Act is not inconsistent with the Cybercrimes Act. The search and seizure provisions of the Criminal Procedure Act are object-based, as they do not deal explicitly with the specialised procedures that are required to investigate cybercrimes or other offences that involve the use of digital devices. The Cybercrimes Act attempts to address this shortcoming. The coexistence of the search and seizure provisions in these two Acts may cause difficulties in the fight against crime. In addition to the validity requirements of search warrants, as set out in the Acts, additional intelligibility requirements for the validity of search warrants have been developed by the courts.
Fuzzy type two self-tuning technique of single neuron PID controller for brushless DC motor based on a COVID-19 optimization

&lt;span lang="EN-US"&gt;This paper presents an efficient interval type 2 fuzzy (IT2F) based on a single neuron proportional–integral–derivative (PID), also known as IT2FSNPID controller. The main purpose of the proposed control technique is to track the motion profile of the brushless DC (BLDC) motor. Also, a comparative study was investigated fuzzy type 1 (FT1) and IT2F. IT2F can treat the uncertainty and nonlinearity of the BLDC motor drive electric system in contrast to FT1. The parameters of each control technique were obtained using a new COVID-19 optimization algorithm according to an objective function. Moreover, several tests had been performed to ensure the ability of fuzzy type to absorb the system uncertainty and nonlinearity. All controllers were utilized to operate the BLDC motor sudden change in load and continuous load. The simulation results show that the IT2FSNPID can improve the dynamic response of linear and nonlinear of the same BLDC motor and accommodate the system uncertainty significantly.&lt;/span&gt;
Cloud Environment Task Scheduling Optimization of Modified Genetic Algorithm

From the availability of resources to the accomplishment of tasks, cloud computing is a development of supercomputing. One of the most trustworthy paradigms in computing technology is built on internet-based parallel and distributed computing models. Optimization algorithms can be used to distribute user workloads to provided logical resources termed 'Virtual Machines' in the cloud computing system, which is a major aspect of resource management (VM). A fundamental challenge in cloud computing is the dynamic heterogeneity of resources and workloads, which necessitates efficient task scheduling and distribution. It is possible that task scheduling in distributed environments will improve our understanding of workflow scheduling, independent task scheduling that takes into account security and execution time for applications, trust between various system entities, and improved system utilisation and energy efficiency, among other things. The goal of this research is to contribute to these advancements in these areas: An independent task scheduling system based on genetics is presented to obtain the best outcomes in terms of time and resource consumption while allocating tasks to resources in accordance with the task's security needs. Various meta-heuristic algorithms, such as Genetic Algorithm, are currently being used to solve task scheduling difficulties.
Overview of Multiple User Encryption for Exchange of Private Data via Blockchains

In order to integrate the use of blockchain technologies into the daily routines of companies and institutions, it is essential to securely transmit the data stored and exchanged in these systems. Not only the security and reliability of the underlying system is important here, but also the encryption of the transferred information. Since there are usually more than two parties involved in transferring data over the blockchain in everyday applications, multi-party encryption is required. This paper introduces various encryption methods which can be used in the context of blockchain usage.
Topic Segmentation Model Focusing on Local Context

Topic segmentation is important in understanding scientific documents since it can not only provide better readability but also facilitate downstream tasks such as information retrieval and question answering by creating appropriate sections or paragraphs. In the topic segmentation task, topic coherence is critical in predicting segmentation boundaries. Most of the existing models have tried to exploit as many contexts as possible to extract useful topic-related information. However, additional context does not always bring promising results, because the local context between sentences becomes incoherent despite more sentences being supplemented. To alleviate this issue, we propose siamese sentence embedding layers which process two input sentences independently to get appropriate amount of information without being hampered by excessive information. Also, we adopt multi-task learning techniques including Same Topic Prediction (STP), Topic Classification (TC) and Next Sentence Prediction (NSP). When these three classification layers are combined in a multi-task manner, they can make up for each other's limitations, improving performance in all three tasks. We experiment different combinations of the three layers and report how each layer affects other layers in the same combination as well as the overall segmentation performance. The model we proposed achieves the state-of-the-art result in the WikiSection dataset.
Machine learning and external auditor perception: An analysis for UAE external auditors using technology acceptance model

Research Question: Do external auditors in the United Arab Emirates (UAE) perceive the ease of use and usefulness of Machine Learning (ML)? Motivation: This study aims to investigate external auditors' perceptions of the ease of use and usefulness of Machine Learning in auditing in the UAE. In addition, the study intends to examine the difference in perceived ease of use of Machine Learning between local and international audit companies in the UAE. Data: Data for this study were gathered from 63 external auditors working for local and global audit firms in the UAE. The study's population comprises external auditors from national and international audit companies in UAE. Tool: The questionnaire was deployed through an online survey tool. Findings: The results have shown that the findings do not support the idea that there is a different perception of the Perceived Ease of Use of Machine Learning in auditing between local and international audit firms. According to the conclusions of this study, external auditors have a restricted perception of the simplicity of use and utility of Machine Learning. Practical implications: The importance of the findings of such research stems from the lack of research evidence on the perceived ease of use and usefulness of Machine Learning in external auditing in the UAE. As a result, this paper provides new empirical evidence by assessing external auditors' assessments of the usage of Machine Learning in the UAE.
Comparative Study and Utilization of Best Deep Learning Algorithms for the Image Processing

Deep learning has gained immense popularity in scientific computing, and its algorithms are widely used in complex problem-solving industries. Every deep learning algorithm use different types of neural networks to perform indented tasks. Deep learning (DL) algorithms have emerged from different machine learning and soft computing methodologies. Since then, a number of deep learning (DL) algorithms have been recently introduced in the scientific community and applied in various application fields. Today, the use of DLs has become indispensable due to their intelligence, effective learning, accuracy and reliability in model creation. However, a comprehensive list of DL algorithms has not yet been presented in the scientific literature. This article lists the most popular DL algorithms and their application areas. Deep learning uses ANN artificial neural networks to perform convoluted calculations on huge amounts of data. It is a type of machine learning based on the structure and function of the human brain. Deep learning algorithms train machines by learning from examples. Industries such as healthcare, e-commerce, entertainment and advertising often use deep learning.
Advantages and limitations of the use of e-textbooks in the Serbian language teaching from the perspective of teachers and future teachers

Electronic textbook as a product of digital technology has a potentially important role in the learning and teaching process. The authors examine the opinions of teachers (N = 266) and future teachers (N = 176) regarding (a) the advantages and (b) limitations of the use of e-textbooks in the Serbian language teaching in younger grades of elementary school. The research findings show there is an agreement in the assessment of positive and negative aspects of the use of this textbook format in teaching. Multimedia, interactivity and hypertextuality are recognized as the most important advantages, and as the most important limitations - the weakening of direct communication between pupils and teachers, screen reading and the impossibility of its application to certain teaching contents. Effectiveness and efficiency of the use of e-textbooks in teaching and learning is determined both by the characteristics of its structure and content and by the way they are used. In that sense, a significant role in achieving the potential developmental and formative impact of electronic textbooks on students is played by a teacher, who is responsible for creating an optimal learning environment.
TATA GUNA LAHAN DAN KEJADIAN BANJIR DI KAWASAN PERKOTAAN KABUPATEN TABALONG

The Regional Spatial Plan (RTRW) is a direction in space utilization and control of space utilization, but the RTRW still uses a small scale of 1: 50,000, which means that information on topographical conditions is not described in detail, so the housing planning and licensing are not informed in detail as a result, low-lying areas Initially functioned as a temporary water reservoir turned into a residential area, it was unavoidable. The conventional drainage concept currently applied in Tanjung urban area is an effort to dispose of or drain excess water as soon as possible to the nearest river, namely the Mangkusip River. Along with the development which is not balanced with good drainage planning causes water to flow improperly, and high-intensity rainfall may occur flooding. The purpose of the research is to examine land use in urban areas with three-dimensional spatial data related to flood events, analyze flood events in urban areas, and determine locations as protected areas for water conservation. Three-dimensional spatial data using DEM from DEMNAS, the digital image in 2020. Determining protected location areas (jungle cities) using several parameters, where each parameter has its score value, both analyzes utilize GIS. Counting of a flood using Rational and Manning method. The results showed: 1) Topographic conditions and the slope of the research site, 2) Natural formation network of the Mangkusip river and tributary, 3) Length (L) Length of air traffic from the farthest point to the point at the review point, 4) The catchment area (A) from the modeling results, 5) The average slope of the water path from the farthest point to the observation point. 6) Land use (C), 7) The runoff flood discharge is greater than the flood discharge from the Mangkusip river reservoir, resulting in flooding, 8) From the results of the assessment with overlay scoring, the result is an area of 59,318 hectares for water conservation areas to prevent flooding.
A New Mechanism for Detecting Shilling Attacks in Recommender Systems Based on Social Network Analysis and Gaussian Rough Neural Network with Emotional Learning

A recommender system is an integral part of any e-commerce site. Shilling attacks are among essential challenges in recommender systems, which use the creation of fake profiles in the system and biased rating of items, causing the accuracy to decrease and the correct performance of the recommender system in providing recommendations to users. The target of attackers is to change the rank of content or items corresponded to their interests. Shilling attacks are a threat to the credibility of recommender systems. Therefore, detecting shilling attacks it necessary to in recommender systems to maintain their fairness and validity. Appropriate algorithms and methods have been so far presented to detect shilling attacks. However, some of these methods either examine the rating matrix from a single point of view or use low-order interactions or high-order interactions. This study aimed to propose a mechanism using users' rating matrix, rating time, and social network analysis output of users' profiles by Gaussian-Rough neural network to simultaneously use low-order and high-order interactions to detect shilling attacks. Finally, several experiments were conducted with three models: mean attack, random attack, and bandwagon attack, and compared with PCA, Semi, BAY, and XGB methods using precision, recall, and F1-Measure. The results indicated that the proposed method is more effective than the comparison methods regarding attack detection and overall detection, which proves the effectiveness of the proposed method.
STB Sentiment Analysis Classification Multiclass Modeling Using Calibrated Classifier With SGDC Tuning As Basis and Sigmoid Method

A set-top box (STB) is a device that converts digital signals into images and sounds that we can see on a regular analog television (TV). More and more people are looking for STB these days because the ordinary use of analog television (TV) will end in December 2022. The discontinuation of the normal use of analog television (TV) has generated a lot of positive, neutral, and negative sentiments. This sentiment data was obtained from social media Twitter using crawling data techniques. The feature extraction process in this study uses the TF-IDF method. In this study Stochastic Gradient Descent Classifier (SGDC) is used as a basis for determining the optimal method and comparing the SGDC tuning method with the Calibrated Classifier method. The test results show that the best optimization for this model is the Calibrated Classifier method with SGDC as its basis with an accuracy value of 80% on the test data and 100% on the training data. It shows that the Calibrated Classifier method can slightly improve the performance of testing and training on the SGDC Classifier has an accuracy value of 78.4% on the test data and 100% on the training data.
Smart contract disputes and public policy in the ASEAN+6 region

Smart contracts provide some benefits, such as better facilitation for contracting parties to monitor performance of their obligation and reducing the cost spent monitoring the contract. However, it is critical to understand various limitations of this concept as well as many legal and public policy uncertainties around it. Given the non-existence of an “universal rule” that governs smart contracts, the issues vary from jurisdiction to jurisdiction. The article applies comparative legal method to analyse the legal regulation of smart contracts in ASEAN+6 countries as well as the leading jurisdictions in the sphere of implementing digital technologies in the economy. Due to public policy considerations, there are different scenarios of smart contract development. The possible outlook is that ASEAN+6 countries would not accept smart contract as contract, cryptocurrency as property, and/or enforcing foreign awards that relate to smart contract disputes and/or cryptocurrency. Another possible way out of the deadlock is to govern relations under smart contracts by the UNIDROIT Principles. However, if the parties do not opt for such a solution, the determination of the applicable law will be left to the relevant conflict-of-laws rules with all of the uncertainties. Therefore, the author suggests that supranational laws is the better path for avoiding uncertainties in smart-contractual relationships.
Research on Classroom Interaction of International Chinese Online Teaching in Post-Epidemic Era

In the post-epidemic era, international Chinese online teaching still faces four problems in classroom interaction: implement difficulties, single interactive form, insufficient interaction design, and unsatisfactory interaction effects. Interaction, which plays a very important role in second language acquisition, is an inevitable path to realize communication goals and improve the effectiveness of international Chinese online teaching. Therefore, we can improve the classroom interaction of international Chinese online teaching from five aspects: teaching content, teaching process, teaching medium, learners and teachers.
An Intelligent Optimization Control Method for Enterprise Cost Under Blockchain Environment

This paper uses blockchain technology to conduct in-depth research and analysis on enterprise cost optimization control. Based on the analysis of the cost control status in enterprises, the concept of target cost optimization control and specific control ideas are proposed. And the study of optimization control under target cost based on genetic algorithm is carried out in combination with the current three major controls of quality, schedule, and cost. It provides the technical basis for the realization of the target profit of the enterprise. The Optimized Scalable Byzantine Fault Tolerance (OSBFT) algorithm, which is suitable for spectrum sharing, is proposed based on PBFT (Practical Byzantine Fault Tolerance) algorithm. So, in this paper, an improved consensus algorithm OSBFT (Optimized Scalable Byzantine Fault Tolerance) is proposed based on it. The improved genetic algorithm is used to solve the objective function and verify the validity, reasonableness and applicability of the model and algorithm. It is shown that the introduction of delay cost in the multilevel inventory model reduces the total cost of the optimized model by 16.87% compared to previous studies. The algorithm reduces the consensus steps, incorporates a data synchronization mechanism, and enables nodes to join and exit consensus.
Expert System for Diagnosing Disease Symptoms of Rice Pests Using the Dempster Shafer Algorithm and Fuzzy Tsukamoto Algorithm

Agriculture is the largest sector in almost every developing country economy. This sector produces food for most of the population in the country. Some Indonesian people work as farmers who have an important role to ensure the availability of basic ingredients, namely rice from rice. However, the limited number of experts, namely Field Agricultural Extension Officers (PPL) results in limited counseling that will be obtained by farmers, because to overcome all the problems faced by farmers, it is constrained by time and the number of farmers who have problems with their crops. In this case, farmers find it difficult to deal with problems of pests and diseases that attack rice, therefore a tool or an expert application is needed that can help farmers to diagnose pests and diseases of rice in order to provide solutions to overcome them. In connection with that, this study aims to develop an application design of an expert system for diagnosing rice pests using the Fuzzy Tsukamoto algorithm which is a method for classifying objects based on the most similar data, and adding the Dempster Shafer algorithm as a comparison of the methods used to obtain data. maximum result validation. By using the Fuzzy Tsukamoto Algorithm, the author classifies similar objects, in this case the symptoms that often occur during the rice harvest season, then compares them with the Dempster Shafter Algorithm to obtain validation of diseases that occur in rice plants based on the classification of symptoms that have been mapped. . Furthermore, the system will provide the best decision to provide advice related to diseases experienced by rice plants so that farmers can immediately resolve them.