Reviewerly/exHarmony huggingface dataset:
Manajemen Bandwidth dalam Mengoptimalkan Penggunaan Router Mikrotik terhadap Pelayanan Koneksi Jaringan

The internet with unlimited reach can connect all computers in a connection that makes it easier for users to get information, communicate and establish partnerships. The need for technology is very important to support all existing activities, such as the process of teaching and learning activities, data processing and transactions, administrative needs, exchanging information even in terms of social needs and so on. The problem that often occurs when using the internet is that bandwidth management on the internet is irregular and even wasted, it will even make internet network users experience bandwidth struggles, delays, and lagging. There is a need for bandwidth usage management so that unwanted things don't happen. The purpose of this research will be to apply a Queue Tree method to control clients in providing internet services so that they remain stable and efficient to get test results with QoS (Quality Of Service) parameters in optimizing router use. proxy for network connection services. Where the results obtained from 5 clients and 10 clients by limiting upload and download bandwidth of 50 Mbps on Throughput are 32.08 (%) medium category. The 0.0024 (ms) delay category is very good. Packet Loss 7.4 (%) good category. Jitter of 0.0024 (ms) is very good. And the total result of 10 Client Throughput 17.23 (%) is bad. The delay is 0.0044 (ms) which is great. Packet Loss 4.2 (%) good category. Jitter 0.0044 (ms) is very good by using bandwidth management can optimize internet usage well.
Design of Digital Education Systems

An analysis of the main approaches to the design of information education systems is presented. The basics of designing information education systems (IES) are considered, taking into account the modern requirements of digital transformation. The principles of designing the IES are formulated, the basic requirements for the IES are developed, the types of activities that are planned to be carried out in electronic (digital) form are described. The authors show that the expected results from the introduction of the IES will affect all participants in the educational process, however, for each of these participants, these effects will be different. Models for designing the IES in the form of an infological model, a datalogical model, a physical model are proposed. Additionally, a model for designing the IES when teaching a particular discipline, as well as a model for constructing ER-diagrams, were considered. At the same time, the IES is a complex task and it should solve the problems of managing the educational process, automate, and optimize it.
Toc

The Career Development QuarterlyVolume 70, Issue 3 p. 173-173 ISSUE INFORMATIONFree Access Toc First published: 05 September 2022 https://doi.org/10.1002/cdq.12227AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Volume70, Issue3September 2022Pages 173-173 RelatedInformation
DEVELOPMENT OF DIGITAL MEDIA AND LUDO EKSPONEN TO IMPROVE LEARNING OUTCOMES OF GRADE IX JUNIOR HIGH SCHOOL STUDENTS

This study aims to analyze the use of digital media and Ludo Eksponen which has been developed using the ADDIE model in Team Games Tournament (TGT) type cooperative learning. Furthermore, at the stage of implementation and evaluation of the media, it was seen its effectiveness on mathematics learning outcomes, for junior high school students in class IX on the square number and square root material. The subjects of this study were 30 students at one of The Al-Amien Junior High Schools in Sumenep Regency. The data collection technique used direct observation and giving pretests and pot-tests to research subjects. As a result, there was an increase in the average value between the pretest and post-test results, which was 7.54 and 53.65, respectively. By using a Paired Test sample t-test. Based on the results of the study, it can be concluded that the use of digital media and Ludo Eksponen as manual media in cooperative learning type Team Games Tournament (TGT) was quite effective in improving student learning outcomes on the square number and square root.
Analysis of the relation between academic performance and learning strategies of undergraduate students in Distance Education

This research aimed to identify the relations between the learning strategies adopted by distance education accounting students and their academic performances. In the context of the pandemic caused by Covid-19, several Higher Education Institutions (HEIs) needed to invest in technologies that facilitate the introduction and dissemination of the distance education. Despite the similarities in content between face-to-face and distance education, the learning process is different between the modalities, making it necessary to understand the strategies that help explain the performance of these students. To achieve the objective proposed by the research, a survey was carried out with 174 students of accounting undergraduate course in the distance education modality of a private HEI based in the city of São Paulo. The data used in this research was based in quantitative techniques, such as test of means, analysis of principal components and simple and multiple linear regressions. From the research results, it was possible to identify a positive correlation between the main strategies used by the students and the performance in the course, corroborating the literature consulted. Cognitive strategies, specially, proved to be more important for students in the research sample. The study results help to understand how accounting students use strategies that help their academic performance, which can be useful for course managers, students in the area, professors, and for the conduction of educational polices by the accounting association.
P07.02.B Neuro-oncological augmented reality planning (NOA-p)

Abstract Background When preparing for the resection of an intracranial lesion, neuronavigation with a tracked pointer is most often used to determine lesion borders and the optimal approach. This can sometimes prove challenging, especially for deep-seated lesions. Augmented Reality (AR), directly displaying the lesion on the patient’s skin, can simplify and improve this step. Material and Methods We developed a system for inside-out infrared tracking that does not require an external tracking camera or external computer and allows for heads-up displaying an AR scene on the Microsoft HoloLens II. Twenty patients planned for the resection of an intracerebral lesion were included in our study. After patient registration, the lesion outlines were marked on the patient’s skin by different participants, consecutively using the Brainlab neuronavigation system and the HoloLens. Each registration on both systems provided a registration transform that was compared for accuracy and consistency. The performance of the participants was measured in terms of duration and accuracy and compared to expert registration and delineation. Results Both registration and delineation were significantly faster with AR (p=0.02 and p&amp;lt;0.001, respectively, and p&amp;lt;0.001 for the total duration), taking 79.23±17.48 and 39.58±39.10 seconds while neuronavigation required 96.61±24.54 and 90.80±44.09 seconds. AR had a registration offset of 3.3mm and 3.4°, and was more consistent compared to neuronavigation. AR facilitated more accurate and detailed lesion delineation, while neuronavigation often overestimated lesion size. Conclusion Augmented reality provides a faster and more accurate alternative for resection planning. Lesion delineation is more intuitive while remaining high in accuracy. Future research should focus on further intraoperative implementations.
Novel Oversampling Algorithm for Handling Imbalanced Data Classification

In the current age, the attention of researchers is immersed by numerous imbalanced data applications.These application areas are intrusion detection in security, fraud recognition in finance, medical applications dealing with disease diagnosis pilfering in electricity, and many more.Imbalanced data applications are categorized into two types: binary and multiclass data imbalance.Unequal data distribution among data diverts classification performance metrics towards the majority data instance class and ignores the minority data, instance class.Data imbalance leads to an increase in the classification error rate.Random Forest Classification (RFC) is best suitable technique to deal with imbalanced datasets.This paper proposes the novel oversampling rate calculation algorithm as Improvised Dynamic Binary-Multiclass Imbalanced Oversampling Rate (IDBMORate).Experimentation analysis of the proposed novel approach IDBMORate on Page-block (Binary) dataset shows that instances of positive class is increased from 559 to 1118 whereas negative instance class remains same as 4913.In case of referred multiclass dataset (Ecoli), IDBMORate produces the consistent result as minority classes (om, omL, imS, imL) instances are oversampled majority class instances remains unchanged.IDBMORate algorithm reduces the ignorance of minority class and oversamples its data without disturbing the size of the majority instance class.Thus, it reduces the overall computation cost and leads towards the improvisation of classification performance.
Parameter Estimation in Computational Systems Biology Models: A Comparative Study of Initialization Methods in Global Optimization

This paper compares different initialization methods and investigates their performance and effects on estimating kinetic parameters' value in models of biological systems.Estimating parameters values is difficult and timeconsuming process due to their highly nonlinear and huge number of kinetic parameters involved.Global optimization method based on an enhanced scatter search (ESS) algorithm is a suitable choice to address this issue.However, despite its resounding success, the performance of ESS may decrease in solving high dimension problem.In this work, several choices of initialization methods are compared and experimental results indicated that the algorithm is sensitive to the initial value of kinetic parameters.Statistical results revealed that uniformly distributed random number generator (RNG) and controlled randomization (CR) that being used in ESS may lead to poor algorithm performance.In addition, the different initialization methods also influenced model accuracy.Our proposed methodology shows that initialization based on opposition-based learning scheme have shown 10% better accuracy in term of cost function.
Encryption Algorithms Modeling in Detecting Man in the Middle Attack in Medical Organizations

A cyberattack is a serious crime that could affect medical organizations.These attacks could affect medical organization sensitive data disclosure, loss of organization data, or the business's continuity.The Man-in-The-Middle (MITM) attack is one of the threats that could impact medical organizations.It happens when unapproved outsiders break into the traffic between two parties that think they are conversing directly.At the same time, the adversary can access, read, and change secret information.Because of that, medical organizations lose confidentiality, integrity, and availability.Data encryption is a solution that changes vital information to unreadable by unauthorized and unintended parties.It could involve protecting data with cryptography, usually by leveraging a scrambled code.Only the individuals with the decoding key can read the information.There is no full protection due to the variety of MITM attacks.Each encryption algorithm has its advantages and disadvantages, like the speed of encryption and decryption, strength of the algorithm, and the cipher type.This research investigates the MITM attacks and comprehensively compares the Rivest Shamir Adleman algorithm and the Elliptic Curve Cryptography algorithm.
Watchdog Monitoring for Detecting and Handling of Control Flow Hijack on RISC-V-based Binaries

Control flow hijacking has been a major challenge in software security.Several means of protections have been developed but insecurities persist.This is because existing protections have sometimes been circumvented while some resilient protections do not cover all applications.Studies have revealed that a holistic way of tackling software insecurity could involve watchdog monitoring and detection via Control Flow Integrity (CFI).The CFI concept has shown a good measure of reliability to mitigate control flow hijacking.However, sophisticated attack techniques in the form of Return Oriented Programming (ROP) have persisted.A flexible protection is desirable, which not only covers as many architecture structures as possible but also mitigates known resilient attacks like ROP.The solution proffered here is a hybrid of CFI and watchdog timing via inter-process signaling (IP-CFI).It is a software-based protection that involves recompilation of the target program.The implementation here is on vulnerable RISC-V-based process but is flexible and could be adapted on other architectures.We present a proof of concept in IP-CFI which when applied to a vulnerable program, ROP is mitigated.The target program incurs a run-time overhead of 1.5%.The code is available.
Towards Flexible Transparent Authentication System for Mobile Application Security

Undoubtedly, Mobile Application Security (MAS) has made tremendous progress in implementing enhanced security protocols in the past decade.With the recent increase in the usage of mobile applications, concerns of privacy and security are increasing rapidly.Thus, the security measurement must be applied to satisfy security and privacy needs.On the one hand, the developer community works feverishly to develop mobile applications with innovative and usable layers and userfriendly for multigenerational customers.However, the security community, in particular, strives to make those layers secure.Therefore, the main objective of this research is to build a transparent authentication system in a mobile application.There are potentially many ways to implement an authentication mechanism such as the biometrics approach.It has features, which can be used to heightened security for the end-user.In these articles, we experimentally investigate the multigenerational customer base's factors such as age, convenience, easiness, memorizing new passwords, and understanding the precept of frequently changing passwords to enhance security.Additionally, we propose a system that will solve the common problems users face when starting the password resetting process.At the same time, in the MAS sector, we orchestrate the applications for better security encryption for the stored biometrics to ensure it, which makes it even more challenging for an adversary to bypass the system and reset the password.We conclude our research with a comprehensive security solution for MAS that considers user friendliness and data safeguarding.
A New Learning to Rank Approach for Software Defect Prediction

Software defect prediction is one of the most active research fields in software development.The outcome of defect prediction models provides a list of the most likely defect-prone modules that need a huge effort from quality assurance teams.It can also help project managers to effectively allocate limited resources to validating software products and invest more effort in defect-prone modules.As the size of software projects grows, error prediction models can play an important role in assisting developers and shortening the time it takes to create more reliable software products by ranking software modules based on their defects.Therefore, there is need a learning-to-rank approach that can prioritize and rank defective modules to reduce testing effort, cost, and time.In this paper, a new learning to rank approach was developed to help the QA team rank the most defectprone modules using different regression models.The proposed approach was evaluated on a set of standardized datasets using well-known evaluation measures such as Fault-Percentile Average (FPA), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the Cumulative Lift Chart (CLC).Also, our proposed approach was compared with some other regression models that are used for software defect prediction, such as Random Forest (RF), Logistic Regression (LR), Support Vector Regression (SVR), Zero Inflated Regression (ZIR), Zero Inflated Poisson (ZIP), and Negative Polynomial Regression (NPR).Based on the results, the measurement criteria were different than each other as there was a gap in the accuracy obtained for defects prediction due to the nature of the random data, and thus was higher for RF and SVR, as well as FPA achieved better results than MAE and RMSE in this research paper.
Theoretical foundations of the psychology of the possible

This article analyzes the field of science that studies the entire spectrum of phenomena in the psychology of the possible. Why only today did we start talking about a new field of psychology, the psychology of the possible? Its novelty lies in the new emphasis on the possible as unexpected, incredible, and improbable. In the psychology of the possible, at one pole of the understanding of psychologists is the adaptive possible, based on past experience, and on the opposite is the possible as a preadaptive phenomenon (understanding of events that are not causally related to the ontogenesis of the subject). The connecting link between these poles is the idea of the sought-for in human thinking, the prediction of the initially unknown when solving the problem. In the psychological analysis of personality, on the left pole of the “conscious — unconscious” continuum, there are alternative possible selves conscious of the subject and therefore enumerable, on the right — personality as an existential mystery. The theoretical and methodological foundations of the psychology of the possible were analyzed in detail — the philosophy of the possible M. N. Epstein, the historical and evolutionary concept of preadaptation to uncertainty by A. G. Asmolov with colleagues, scientific ideas about the uncertainty of the human world. The concept of possible thinking, which analyzes different options and alternatives of the possible, is disclosed and substantiated in detail. At the end of the report three large groups of phenomena in the psychology of the possible are analyzed: the phenomena traditionally studied in psychology that relate to the possible (affordances, anticipation, and others), the phenomena of the sought-for, and the understanding of the possible as a preadaptive phenomenon that characterizes unstable situations, the occurrence of which cannot be predicted.
Summarizing Event Sequence Database into Compact Big Sequence

Detecting the core structure of a database is one of the most objective of data mining.Many methods do so, in pattern set mining, by mining a small set of patterns that together summarize the dataset in efficient way.The better of these patterns, the more effective summarization of the database.Most of these methods are based on the Minimum Description Length principle.Here, we focus on the event sequence database.In this paper, rather than mining a small set of significant patterns, we propose a novel method to summarize the event sequence dataset by constructing compact big sequence namely, BigSeq.BigSeq conserves all characteristics of the original event sequences.It is constructed in efficient way via the longest common subsequence and the novel definition of the compatible event set.The experimental results show that BigSeq method outperforms the state-of-the-art methods such as Gokrimp with respect to compression ratio, total response time, and number of detected patterns.
Enhanced Security: Implementation of Hybrid Image Steganography Technique using Low-Contrast LSB and AES-CBC Cryptography

Now-a-days, sensitive and confidential information needs to be exchanged over open, public, and not secure networks such as the Internet.For this purpose, some information security techniques combine cryptographic and steganographic algorithms and image processing techniques to exchange information securely.Therefore, this research presents the implementation of an algorithm that combines the AES-CBC cryptographic technique with the LSB steganographic technique, which is statistically enhanced by image processing by looking for low-contrast areas where the encrypted information will be stored.This hybrid algorithm was developed to send a plaintext file hidden in an image in BMP format, so the changes in the image are invisible to the human eye and undetectable in possible steganographic analysis.The implementation was performed using Python and its libraries PyCryptodome for encryption and CV2 for image processing.As a result, it was found that the hybrid algorithm implemented has three layers of security over a plaintext encrypted and hidden in a digital image, which makes it difficult to break the secrecy of the information exchanged in a stego-image file.Additionally, the execution times of the hybrid algorithm were evaluated for different sizes of plaintext and digital image files.
Humanization of Logo as a Representation of Social Values in Halodoc

In utilizing the Halodoc application, there is a communication gap between direct speech communication and communication through the application. Through Roland Barthes' semiotic method, this research attempts to examine this problem and propose an alternative solution in the form of emoji humanization. Heidy's emoji structure is indeed synonymous with the Halodoc logo. Meanwhile, according to the connotative analysis, you can see the emoji visualization whose characters are identical to today's human characters. This character is characterized by expressions and facial expressions that look cheerful and friendly. This study concludes that there are a number of indicators of success in composing humanist emojis and still referencing the main logo identity. In the principle of emoji design, structurally it must be dominated by a shape similar to the original logo. The addition of mimics, expressions, limb movements, and body movements itself must represent the true expressions of humans who provide health ordering services. Thus, emojis are more attractive, interactive, and communicative. As confirmed by various studies, this intimacy between users and emojis that looks humanist has proven to strengthen customer trust in services.