Reviewerly/exHarmony huggingface dataset:
Methodology for Determining Optimal Model and Training Data in Deep Learning

Deep Learning has numerous applications in various market segments including consumer, industrial, energy & utilities, oil & gas, surveillance, autonomous vehicles and medical and so on. And within each dataset, the deep learning inference precision achieved with a trained model may be meeting the precision goals. But that does not mean the same trained model may perform equally well on some other test dataset. In practical applications, such drop in precision on test data variations is highly undesired. Hence, it is critical to train the deep learning model with adequate and augmented training data. Also, it is important to deploy an optimal deep learning model for a given application. This is to utilize optimum compute resources, when such deep learning trained model is deployed in inferencing. This becomes even more important for resource constrained and battery-operated embedded edge applications. Hence, determining the amount of training data needed and deep learning model to be used should not be on trial-and-error basis. There are no known structured methodologies available, for optimal model selection and training data. In this paper, a methodology has been proposed for determining optimal deep learning model and training data to be used, for achieving target precision levels.
Klasifikasi Jurnal menggunakan Metode KNN dengan Mengimplementasikan Perbandingan Seleksi Fitur

Classification is a process that automatically places text documents into a text based on the content of the text. Classification can help us classify many text documents that have been published, with the classification, these text documents can be reached easily and quickly. Feature selection can be used to improve the performance of text classification in terms of learning speed and effectiveness. In the Chi-Square feature selection experiment, a 1% threshold combination with a parameter value of k=6 is the combination chosen to be the best model. In testing the new data, the K-Nearest Neighbor model by selecting the Chi-Square feature produces precision performance, recall, F1-Score, and accuracy respectively, namely 85%, 83.3%, 88.2%, and 92.3%. In the Gini Index feature selection experiment,1% threshold combination with a parameter value of k=4 is the combination chosen to be the best model. This threshold selects about 31 features with the highest Gini Index value. In testing the new data, the K-Nearest Neighbor model by selecting the Gini Index feature produces precision performance, recall, F1-Score, and accuracy respectively, namely 81.2%, 80.3%, 81.6%, and 86.6%.
Systematic Review on Photogrammetry, Streaming, Virtual and Augmented Reality for Virtual Tourism

Photogrammetry, streaming, virtual reality, and augmented reality are technologies that are having a great impact in fields such as education, medicine, video games, and lately in tourism, especially in recent years due to the COVID19 pandemic; however, the area of tourism had limitations in terms of face-to-face tourism, but thanks to the use of the aforementioned technologies, it has been possible to virtualize the interactions of the users. In this work, we have carried out a review of applications of photogrammetry, streaming, virtual and augmented reality focused on the area of tourism. Which were found through a systematic review of the literature focused on the use of these technologies as tools for the generation of immersive environments for tourist sites. The objective of this work is to serve as a basis for future research focused on virtualizing tourist sites; That is why we focused on the following topics (1) the application of these technologies to tourism in related works, (2) the advantages and disadvantages in the use of these technologies, (3) the problems that these technologies present and how they would be solved and (4) future work. After conducting the research review, we have been able to confirm that photogrammetry, streaming, virtual reality, and augmented reality can be used effectively to generate an inmersive interaction between the tourist and the tourist site in a virtual way, but it is still necessary to take considerations about its use.
Emoticons Elicit Similar Patterns of Brain Activity to Those Elicited by Faces: An EEG Study

The present study investigated whether the patterns of brain activity elicited by emoticons are similar to those elicited by faces. In order to test this, participants were subliminally primed using either human faces, emoticons, or non-face control stimuli. Each prime group contained three levels of valence—positive, negative, or neutral. Brain activation was recorded via electroencephalography. Subsequently, three event related potential components of interest were identified, which are closely associated with the processing of faces, i.e., the P100, the N170, and the late positive potential. These ERPs were tested at two electrode sites. For each ERP component, peak amplitudes were calculated and used in repeated-measures ANOVAs. There were significant main effects of prime type across several ERP components, and several interaction effects prime type*prime valence on four out of six components. There was no statistically significant main effect of prime valence on any of the ERP components. While our results uncovered diverse patterns of brain reactivity to the different kinds of primes, there is sufficient evidence to suggest that, at some electrode locations, emoticons elicit similar brain reactions as faces do.
Evaluating the Effect of Optimized Voting Using Hybrid Particle Swarm and Grey Wolf Algorithm on the Classification of the Zoo Dataset

When there are numerous possible solutions for a given class in a given problem, majority voting or plurality voting is typically employed. One common technique for improving classification accuracy is bagging, which involves training many classifiers on slightly different datasets and then voting on the combined results. In this research, we examine how alternative voting procedures affect the efficiency of two distinct classification algorithms applied to datasets of varying complexity. Despite the increased computing cost associated with determining preference order, the results show that the single transferable vote can be a suitable alternative to plurality voting.
Feature Extraction and Application of Chinese Sentence Patterns Based on Massive Data Analysis

Today, in the era of big data, data shows the characteristics of large scale, modal diversity and rapid growth. The value of language data also increases explosively, and the value of Chinese corpus information analysis also increases correspondingly. The crawler obtains a large number of Chinese sentences from different styles, establishes a personal corpus, designs and optimizes the database, analyzes the sentence patterns of each corpus in the corpus, and finally establishes a Chinese sentence pattern database, makes full use of a large number of data in the sentence pattern database, and makes multi-directional statistics and analysis of sentence patterns. Taking the corpus in the sentence pattern corpus as a sample, it uses kmeans + + for clustering, The sentence space representation model of the article is constructed, and the feature vector of each article is obtained, so as to carry out the application research of sentence pattern.
Exploring the information security culture within industrial control systems organisations: Expert reviews

The security of computer-automated systems governing the Critical Infrastructures sectors: Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) systems represent a significant challenge in the present time. These systems have seen numerous examples of malicious attacks including Stuxnet and Night Dragon attacks. Unknown cyber-security threat vectors towards the critical industrial processes and systems seem to be unavoidable due to the new technology trends such as Mobility, BYO devices, cloud storage and big data. Recognising and rapidly responding to these attacks could be possible by strengthening the information security implementations within organisations. This research paper proposes a holistic approach to cultivate awareness within the organisations by instilling an information security culture. This includes assessing the information security compliance, awareness, measures, policies and government as adapted from the Information Security Culture Model. The information is gathered through semi-structured interviews of eight IDS and SCADA systems experts and were then analysed deductively. This paper presents the results of this research study analysis to evaluate the current information security culture within SCADA organisations.
Suspicious Customer Detection on the Blockchain Network for Cryptocurrency Exchanges

Blockchain has rapidly become one of the hottest Internet technologies as a decentralized and distributed data management solution. Cryptocurrency, one of the most successful applications of blockchain, quickly attracted attention from investors because of its high anonymity, flexibility and rapidity. However, cryptocurrencies are also used by some criminals to commit crimes secretly and they integrate illicit funds into real economy through cryptocurrency exchanges, causing serious impact to economies and societies. Traditional methods for exchanges to prevent financial crimes like Know Your Customer (KYC) have limit effect in the peer-to-peer and decentralized blockchain system. In this work, we propose a system aiming to help cryptocurrency exchanges to detect suspicious customers on the blockchain network. Besides the traditional KYC procedures, several machine learning models are used to check the account addresses submitted by the customers and to detect suspicious address in daily transaction. The detection models use the open-source data from websites and forums instead of private data from governments or institutions, and can detect the top five financial crimes in cryptocurrencies simultaneously. Finally, a visualization of transactions is proposed to better demonstrate the fund flows of suspicious customers for further analysis.
Stability Improvement of Crawler-type Ceiling Mobile Robot in high-speed movement

Ceiling is considered to be suitable location for robots to transport cargoes because Automated Guided Vehicle(AGV) and people do not interfere with them. In our previous study, we have developed a ceiling mobile robot called HanGrawler 2. HanGrawler 2 can travel at a high speed of 1.0 m/s to compete with ground vehicles as its champion data. However, HanGrawler 2 sometimes fell at the beginning of the traveling. So, the purpose of this study is to improve the traveling stability of HanGrawler 2. There are two technical issues to be solved: 1) HanGrawler 2 cannot start traveling until hanging mechanism is set to specific position, and 2) HanGrawler 2 tends to fail to hang to the ceiling during acceleration. Automatic adjustment of an initial position and new control method of hanging considering in acceleration is added to solve these issues.
Boosted Self–evolving Neural Networks for Pattern Recognition

It has been well documented that both boosting and bagging algorithms improve ensemble performance. However, these types of algorithms have only infrequently been applied to ensembles of constructivist learners which are based on neural networks. Although there have been previous attempts at developing similar ensemble learning algorithms for constructivist learners, our proposed approach also addresses the issue of ensuring more diversity of the learners in the ensemble and offers a different approach for handling imbalanced data sets. More specifically, this paper investigates how a modified version of the AdaBoost algorithm can be applied to generate an ensemble of simple incremental learning neural network-based constructivist learners known as the Self-Evolving Connectionist System (SECoS). We develop this boosting algorithm to leverage the accurate learning of the SECoS and to promote diversity in these SECoS learners in order to create an optimal model for classification tasks. Moreover, we adopt a similar minority class sampling method inspired by RUSBoost which addresses the class imbalance problem when learning from data. Our proposed AdaBoostedSECoS (ABSECoS) learning framework is compared with other ensemble-based methods using four benchmark data sets, three of which have class imbalance. The results of these experiments suggest ABSECoS performs comparably well against similar ensemble methods using boosting techniques.
Secure Shell (SSH)

SSH is used to administer servers, so their security depends on this cryptographic protocol. The goals and structure of SSH are similar to TLS. A handshake protocol negotiates keys and algorithms, and a binary packet layer protocol encrypts and authenticates application data. But there are also differences: Client authentication is far more important in SSH, the length of data records may be encrypted, and the ecosystem is far less standardized than TLS.
Efficient Multi-Party Contact Tracing

Since the beginning of the Covid-19 Pandemic, Contact Tracing Apps have been implemented in many countries as a way to detect if someone has been in contact with a patient within a minimum amount of time. However, most existing solutions only consider users in pairs. Since many people meet at the same time in real-life scenarios, those applications aren't able to accurately reflect the situation. Moreover, extending current schemes to a multi-party setting could cause scaling problems and place a heavier load on the device. In this paper, we propose a new Contact Tracing protocol that works in a multi-party setting. We evaluate our scheme to show its efficiency.
The role of educational video in the development of future lawyers’ english professional communicative competence

Since modern requirements for law specialists’ training postulate English as an imminent part of professional education, there is a need for constant revision of methodological tools aimed at the development of professional communicative competence. Several characteristics of video make it an efficient tool of reconstructing an authentic English environment even in the circumstances of distance learning. Thus the objective of the paper is to determine the role of the educational video didactic reserves in building a professional (legal) English competence. The indicated objective required the review of specialized scientific resources. The article focuses on: defining such notions as “the educational authentic video”, “professional communicative competence”, “learning environment”; representing the criteria of professional authentic films / video selection; investigating the peculiarities of the basic types of authentic educational video; distinguishing the stages in the work on the educational film / video. The paper also discusses judicial video-based assignments targeted at developing legal culture and providing a personalized approach to professional English competence enhancement. The correct selection of video and the usage of adequate guidelines permit to reconstruct a realistic professional environment within a classroom. It will become an extra source for a competitive specialist’s training.
Discipline, Impact, And Challenges Of Virtual Reality In Higher Education: A Systematic Literature Review

Virtual Reality (VR) has received attention since the trend of the Metaverse came after the pandemic era. Several studies look into how Virtual Reality can be used in higher education. because all the research comes from the education field. However, previous research is too narrow in using technology, but not the area, impact, and challenges of Virtual Reality itself. Therefore, the systematic literature review (SLR) from 2016 to 2022 is discussed in this work to research how discipline, impact, and challenges of Virtual Reality in Higher Education. From 702 papers retrieved, and 214 candidates, to determine the applicability of the study question, 62 papers were chosen. The result shows that most discipline areas are still in computer science, followed by education and public health. Most reports said Virtual Reality is still highly impacted in solving the problem, especially in public health and education. However, there are still some pitfalls: the challenge of using The Head Mounted Device makes Virtual Reality-Induced symptoms and effects (VRISEs) such as Cyber sickness, simulator sickness, Motion sickness, dizziness, and physical discomfort. The implication of the SLR will be considered for academic purposes in preparing for the Metaverse era, especially in higher education.
INDIVIDUALIZATION OF THE PROCESS OF FORMATION OF INFORMATION AND ANALYTICAL SKILLS OF STUDENTS IN THE VIRTUAL EDUCATIONAL ENVIRONMENT OF THE UNIVERSITY

The article deals with the problem of applying an individualized approach in the process of teaching students. The purpose of the study was to identify the impact of individualized learning in the virtual educational environment of the university on the effectiveness of the formation of information and analytical skills of students. With the help of the developed diagnostic tools, a primary assessment of the levels of formation of the studied skills of two groups of students was carried out. The components of the virtual educational environment are described, which make it possible to individualize the learning process: training modules, a monitoring module, an analytical module. Based on the results of the control diagnostics of the formation of information and analytical skills, a conclusion was made about the effectiveness of the individualization of the process of forming information and analytical skills of students in the virtual educational environment of the university.
An Empirical Analysis of Reverse Greedy for K-Center

We compare the results of a 2k-approximation reverse greedy algorithm to a 2-approximation greedy algorithm for the k-center problem. We find that in most of our experiments, the reverse greedy algorithm outperforms the greedy algorithm.
Research on the Impact of Different Intensity Task Scaffolding on Students' Learning Effect Under Virtual Simulation Experiment

With the maturity and development of virtual simulation technology, how to use virtual simulation to improve existing education has become a hot research topic in the education industry. However, how to use learning scaffolding to improve the effectiveness of virtual simulation systems still need further research. This paper combines learning scaffolding theory and self-determination theory to investigate the effects of different intensity task scaffolding on the learning effectiveness of students with different cognitive styles, using the virtual simulation experiment platform of material procurement process as the research object. The results show that the intrinsic motivation and learning satisfaction of field-independent students are higher than field-dependent students under different intensity task scaffolding; the autonomy needs, competence needs and relationship needs of two cognitive styles students are satisfied by different intensity task scaffolding, but field-independent students obtain more needs satisfaction.
Design and Research of Distributed Web Crawler Based on Knowledge Graph

As the rapid growth of Internet, the related services and information are also growing rapidly. While this information is widely used by people, people have higher and higher requirements for information, and the web crawler, which is specially responsible for the collection of Internet information, is also facing great challenges. At present, large Internet companies at home and abroad and relevant research institutions have given some relatively mature solutions, but most of these solutions can only provide a search service for the general user that can not be customized and has been unable to meet the majority of users' benefit growth of various requirements. Considering the various needs of users, the distributed web crawler can meet the needs of users with its flexible information collection speed and scale. On the basis of analyzing the joints of the technical solutions of distributed web crawler, this paper designs a distributed web crawler structure based on knowledge map, and proposes a practical algorithm, which solves the key technologies of distributed web crawler and realizes a robust and distributed web crawler system. Finally, some tests are done on the web crawler, including the common crawler test and the web crawler test.
Rayleigh quotient with bolzano booster for faster convergence of dominant eigenvalues

Computation ranking algorithms are widely used in several informatics fields. One of them is the PageRank algorithm, recognized as the most popular search engine globally. Many researchers have improvised the ranking algorithm in order to get better results. Recent research using Rayleigh Quotient to speed up PageRank can guarantee the convergence of the dominant eigenvalues as a key value for stopping computation. Bolzano's method has a convergence character on a linear function by dividing an interval into two intervals for better convergence. This research aims to implant the Bolzano algorithm into Rayleigh for faster computation. This research produces an algorithm that has been tested and validated by mathematicians, which shows an optimization speed of a maximum 7.08% compared to the sole Rayleigh approach. Analysis of computation results using statistics software shows that the degree of the curve of the new algorithm, which is Rayleigh with Bolzano booster (RB), is positive and more significant than the original method. In other words, the linear function will always be faster in the subsequent computation than the previous method.
Research on image to illustration translation method based on CycleGAN

Aiming at the problem that balance between abstract style and original painting content is not enough in traditional image style migration, this paper puts forward an improved method that based on the traditional Cyclic-Consistent Generation Adversarial Networks (CycleGAN), which enables the generator to subsample the feature map of each residual layer, and uses skip link and upsampling to merge the low-level feature and the advanced feature. Then the image averaging operation is used to enhance contrast of the generated images, and weighted mean filter is used to filter out redundant details. Finally, the grey level of the image is reduced in order to improve its abstraction. The experimental results show that with CycleGAN, DualGAN, CartoonGAN compared three methods of migration image style, this model not only improves the image style of abstract degree, also a better retain the original image content, significantly improve the processing and transfer the balance between the content of the original painting abstract style.
Verification of Door Pushing Open and Passage Motion by Mobile Manipulator with Autonomous Locomotion

In this paper, we tested a method of opening and passing through door for a mobile manipulator. Especially, we assumed the mobile manipulator constructed by commercially available robots; the arm robot is position controlled and the mobile robot automatically generates the path. We selected a method by Ott et al. as a promising method and implemented on a mobile manipulator. We revealed that the initial position affected to the force and moment acting on the tip of the arm robot and that it can provoke overload to fail the door opening and passing task.