Reviewerly/exHarmony huggingface dataset:
Key Agreement Protocol Using Conjugacy Classes of Finitely Generated Group

This research presents a novel key agreement protocol leveraging the rich mathematical structure of conjugacy classes within groups. We propose a key agreement protocol based on finitely generated group drawing inspiration from algebraic cryptography, specifically group theory, to establish a secure and efficient means of key exchange. Through the utilization of conjugacy classes, the protocol aims to enhance the security of cryptographic systems while addressing computational efficiency concerns. This study contributes to the intersection of mathematics and cryptography by providing a robust foundation for secure communication protocols.
MOBİL KİBER KRİMİNALİSTİKAYA HAZIRLIQ METODOLOGİYALARININ TӘDQİQİ

Mobile cyber forensics serves to recover potential digital evidence from mobile devices using digital forensics. The development and spread of mobile technologies, the need for mobile-based services and new requirements have led to the development and transformation of mobile cyber forensics into an important field. In this article, a component model is proposed to assess the readiness potential of the relevant organizations in the field of mobile cyber forensics for the detection of cybercrimes, collection of evidence, and investigation of criminal cases.
Emotional computing modeling method combining user preference model

Emotional analysis refers to the analysis and identification of emotional tendencies and emotional expressions in texts throu gh natural language processing and computer technology. In order to improve the accuracy and efficiency of user emotion recognition, t his article proposes an algorithm based on deep learning (DL) and special optimization strategy. In order to verify the effectiveness and superiority of the algorithm, the performance of SVM algorithm and this a lgorithm in terms of accuracy, recall and recognition time under different node numbers and different data scales is compared through experiments. The experimental results show that the algorithm has obvious advantages in user emotion recognition and the s tability of search efficiency. Through the use of DL and special optimization strategies, this algorithm can better capture the emotional inform ation in the text and improve the accuracy of emotional recognition. Moreover, with the increase of data scale, multi node computing can effectively improve the efficiency of emotion recognition. The research results further confirm the advantages and application value of DL in the field of natural language processing, especially in the task of sentiment analysis.
A fault diagnosis online method of railway track based on knowledge transfer learning

In the complex environment of actual train operation, due to the lack of railway track fault sample data, the fault diagnosis online model cannot be directly established. However, under the environment of track inspection vehicle and actual train operation, the railway track has the same health status space and most fault types. Therefore, this paper proposes a railway track fault diagnosis online method based on knowledge transfer learning. First, the railway track fault data under the track inspection vehicle environment is taken as the source domain, and the fault data is processed as knowledge and rules, and the expert knowledge rule base based on Belief Rule Base reasoning (BRB) is established. Then, the noise distribution of actual train operation is simulated by triangular distribution, and the fault space of railway track in complex environment is obtained as the target domain. Finally, the knowledge rules in the source domain are transferred to the target domain, and a three-level iterative optimization transfer weight learning method is designed to realize the accurate diagnosis of railway track online fault. The effectiveness of the proposed method is verified by experiments.
Impact of Competitive Strategy, Decision Making Style, Innovativeness and Risk Management on Knowledge Management Practices

This study fills an urgent gap in need of a complete picture of the complicated relationships between organizational traits and Knowledge Management approaches amid Lahore's traditional financial institutions, Pakistan. This research relates to studying how risk management, innovation strategies or decision-making styles affect the knowledge environment within these firms. This paper adopts a positivist philosophy for research and is combined with the use of quantitative methods. Smart PLS software is used to perform Structural Equation Modeling (SEM) on the data obtained. Responses came from a sample of 380 bank managers, comprising the dataset used for analysis. These results suggest that there are some areas in which organizational interventions aimed at knowledge management could be especially effective (e.g., the strong and reliable positive relationships between insights into how organizations use information, competitive strategy, decision-making styles, innovativeness, and risk management). The following results, through verifying and elaborating upon these existing conceptual frameworks while at the same time providing practical applicability for participants in this specific field of study, also serve to add depth theory side of knowledge management. This will provide us with a better understanding of Knowledge Management processes in many kinds of organizations. Because this research makes use of cross-sectional data, the capacity to establish causal relationships between variables is limited. It is a key study for organizations, employees, executives, and policy makers.
Klasifikasi Sentimen Masyarakat Di Twitter Terhadap Prabowo Subianto Sebagai Bakal Calon Presiden 2024 Menggunakan M-KNN

Presidential elections are held every five years and each presidential candidate will get support from several political parties to run for candidacy in the election. In a multi-party system, the number of parties participating in the election is very large, so that the perspectives of voters on political actors, including presidential candidates who will advance in the 2024 elections, are varied. The survey results from Polling Indonesia (SPIN) conducted from 7 to 16 October 2022 show that Prabowo Subianto has the highest electability with a score of 31.6%, based on a national leadership survey. In this study, a test was carried out by classifying tweet data from the public collected on the Twitter application from January to December 2022 using the Modified k-Nearest Neighbor method to analyze public sentiment regarding the upcoming election. Data collected as many as 2,100 data with positive and negative categories related to "Presidential Candidate" and "Prabowo Subianto" and the implementation of the Modified k-Nearest Neighbor classification was carried out using Google Colab. Based on the results of the confusion matrix test from the Modified k-Nearest Neighbor classification with three comparisons made (ie comparisons 70%:30%, 80%:20% dan 90%:10%) and using K=3, 5, 7, 9, 11 when testing a comparison of 90:10 at K=3 the highest accuracy results were obtained with a value of 93,3%.
Book Recommender System Using Matrix Factorization with Alternating Least Square Method

In this digital age, we are faced with countless choices of books. Finding books that match our interests and desires becomes a complex challenge. However, the existence of a book recommender system is useful to help provide the best decision-making experience that users can have. This research develops a book recommender system using Collaborative Filtering (CF) Matrix Factorization with Alternating Least Squares method which is compared with Singular Value Decomposition method to see an accurate recommender system. This research uses datasets from Goodreads in the form of book data and rating data. This research uses several evaluation metrics, namely RMSE and MAE for regression metrics and F1-Score and Precision for classification metrics. Based on the research that has been done, SVD gets a better accuracy value with an RMSE value of around 0.86822, for MAE values around 0.6903, for F1-Score values around 0.827923 and for Precision values around 0.568347. Meanwhile, the ALS algorithm gets an RMSE value of around 1.09320, for MAE value of around 0.86479, for F1-Score value of around 0.000304 and for Precision value of around 0.000596.
Ontology-Based Physical Exercise Recommender System for Underweight Using Ontology and Semantic Web Rule Language

Inactive lifestyles and unhealthy diets are often the result of people's busy lives. because of these bad habits, many people are underweight. diet and lack of physical activity are factors that cause underweight. Due to lack of information, people prefer to live lazily and not exercise. To solve this problem, we propose a physical exercise recommendation system that is explicitly designed for Indonesian people who are struggling with underweight. Despite the existence of various research studies advocating for physical activities tailored to individual preferences, there is currently no recommendation system available within a chatbot framework that includes a comprehensive session to be completed, along with specific sets and repetitions for each activity. This research proposes the utilization of ontology and Semantic Rule Web Language (SWRL) to represent and process the knowledge presented, enabling the development of rules for generating physical activity recommendations based on user preferences. By integrating the user profile, ontology, and the rules created, our system recommends physical exercise based on gender, weight, height, activity level, difficulty of movements, and the type of muscle to be trained. From the sample user data obtained, 408 physical exercises menu are recommended. The performance of the system is quite good, together with the validation results from personal trainers, obtained a precision value of 0.8, recall of 1, and f-score of 0.888. Concluding that the system we designed can provide physical activity recommendations in accordance with user preferences.
Sistem Informasi Lembar Kerja Siswa Berbasis Mobile Menerapkan Metode Rapid Application Development

Mobile technology has been widely applied in education, one of them is as a learning media. The learning process at MTs Nurul Khairiyah is currently still conventional using media in the form of print worksheets. The obstacles faced were that students were less interested and felt bored in participating in learning, teacher absence makes learning stop so that students are not effective in getting lessons, students who were unable to attend also did not get the learning and assignments given, limited hours of study so that the delivery of material is less effective, wasted time in correcting questions and teacher mistakes in calculating student grades. This research aims to build student worksheet applications as digital learning media with the Android platform so that learning can be more interesting and can be done anytime and anywhere. The population of this study were students at MTs Nurul Khairiyah Sei Tuan using the simple random sampling technique to determine a sample of 57 students. The research method applied is the R&amp;D (Research and Deveopment) method so that the data collection used is observation, interviews, questionnaires, and literature study. System development that uses the RAD (Rapid Application Development) method to create a system that can help facilitate the implementation of learning at MTs Nurul Khairiyah Sei Tuan. For testing, researchers used SUS (System Usability Scale) which resulted in a score of 76.14 or good. Therefore it can be concluded that the application is running well and is feasible to be applied as a learning medium at MTs Nurul Khairiyah Sei Tuan.
Chatbot-Based Book Recommender System Using Singular Value Decomposition

In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.
Sistem Informasi Geografis Pemetaan dan Prediksi Pertumbuhan Penduduk Menggunakan Regresi Linear

Geographic Information System (GIS) is a computer-based information system that is used to store and manipulate geographic information. One of the potential applications of GIS is the potential of certain government areas, such as mapping the potential of villages and sub-districts. Tubbitaramanu Subdistrict (TUTAR) Polewali Mandar Regency, most of its territory has not been accommodated on the current digital map platform, so the application of GIS. This is to make it easier for the local government and the general public to obtain information. The purpose of GIS is to find out predictions of population growth, recommend recommendations for the construction of health facilities based on standards, and implement a Geographic Information System for Predicting Population Growth. This study uses the Linear Regression method using population data collected from 2014 to 2020 as data used in the prediction system and can provide recommendations. According to the results, the Tutar District shows that from 2022 to 2027 it requires the construction of pustu health facilities.
The Role of Assertive Leadership in Reducing Organizational Distance An Analytical Study of The Opinions of a Sample of Employees in A Number of Al-Furat Al-Awsat Technical University Formations

The current research aims to explore the relationship between assertive leadership through its dimensions (management, knowledge, values, action, capabilities), and the organizational distance with its dimensions (legal, social, geographical). A number of formations of Al-Furat Al-Awsat Technical University were selected together to conduct the research, numbering 4 formations. (69) employees were selected as a sample for the research, and after testing the data obtained from the questionnaire using statistical programs (spssv.26, smart-plsv.4 ), the research found that there is a significant correlation between assertive leadership and organizational distance.
Acoustic Signature Analysis for Distinguishing Human vs. Synthetic Voices in Vishing Attacks

Cybercrimes targeting mobile devices are on the rise, with vishing and smishing attacks being particularly prevalent. These attacks exploit social engineering techniques to manipulate individuals into divulging personal information or engaging in unintended actions. To counter this evolving threat landscape, this research proposes a pioneering methodology rooted in voice feature analysis. By distinguishing between human and robotic voices, this approach aims to discern legitimate calls from potential scams, thereby mitigating the associated financial losses and reputational damage. The research delves into the intricacies of voice feature analysis, leveraging natural language processing (NLP) and machine learning (ML) techniques to extract and analyze audio attributes such as pitch, volume, and temporal patterns. The ultimate objective is to create a binary classification model that accurately differentiates between human voice calls and robocalls, incorporating a comprehensive dataset comprising actual call recordings and synthesized scenarios. This research advances beyond conventional practices by championing a holistic analysis of both human and robocalls, contrary to the prevalent assumption of robocalls exclusively constituting scams. The application of various audio features, coupled with nuanced weightage allocation, enhances the model’s discernment capabilities. The resultant binary classifier is an exemplar of the innovative fusion of technology and human expertise. In conclusion, this research introduces a novel dimension to the combat against vishing and smishing attacks, with a robust voice feature analysis methodology capable of accurately identifying human and robotic voices. By effectively distinguishing legitimate calls from potential threats, this approach presents a promising avenue for safeguarding individuals and organizations against the far-reaching consequences of cybercrimes. The comprehensive analysis, validation, and insights presented in this paper contribute significantly to the field of cybersecurity and voicebased communication analysis.
ResBot: A Bi-Lingual Restaurant Booking Conversational Artificial Intelligence

AI-powered chatbots in the service industry enhance customer service and enable data-driven decisions. However, in a conversion, the user's input may not always align closely with the training examples, causing even advanced Natural Language Understanding pipelines to occasionally misinterpret the intent behind user utterances, leading to potential conversational missteps. This paper introduces ResBot, an innovative bi-lingual chatbot (Sinhala and English) with a novel hybrid intent classification mechanism. This approach emphasizes the importance of generalization in intent recognition beyond training data using a language model. Furthermore, by automating reservations through its chat interface, the chatbot transforms customer experience and optimizes restaurant operations in an increasingly digital landscape.
NORB: A Stream-Based and Non-Blocking FPGA Accelerator for ORB Feature Extraction

Oriented FAST and Rotating BRIEF (ORB) is a key technique for visual feature extraction and matching, which forms the foundation of the state-of-the-art ORB-SLAM systems. Due to the computational complexity, FPGA accelerators for ORB computing are usually used when running ORB-SLAM on low-power platforms. Previous implementations of ORB accelerators need to block the input stream when computing the rBRIEF descriptors, and cannot achieve high throughput on all pixels. In this paper, we propose a stream-based accelerator of ORB feature extraction which achieves non-blocking computation by caching the columns of the corresponding window buffer. The proposed accelerator is implemented on a Zynq UltraScale SoC, and the experimental result shows it achieves an average latency of 1.4ms, which is 44% faster than the state-of-the-art approach, with similar output accuracy. The system has a low resource utilization with power consumption being only 1.5W.
A Refined Tracking Filtering Algorithm Based on IMM

Maneuvering targets, distinguished by their pronounced dynamic characteristics, present significant challenges to radar tracking. To address these problems, a refined tracking method based on IMM (IMM-RTF) is proposed in this paper. The key idea of the method is to adaptively correct the recursive residual covariance by using the observation residual based on the consistency of residual and residual covariance to reduce the influence of model mismatch. In addition, to realize refined filtering, this paper analyzes the influence of model mismatch on the filter, and models the consistency problem of residual and residual covariance as a convex optimization problem. By analyzing the unique structure of the problem, the analytical solution of the problem under two assumptions is derived. Meanwhile, the adaptive IMM framework is adopted, thus effectively improving the tracking performance of maneuvering targets and non-maneuvering targets under steady-state conditions. Simulation results demonstrate that compared with the IMM, the IMM-RTF not only accentuates the tracking precision for maneuvering targets in steady states but also significantly fortifies target tracking robustness amidst model mismatch.
Adaptive Confidence Threshold for ByteTrack in Multi-Object Tracking

We investigate the application of ByteTrack in the realm of multiple object tracking. ByteTrack, a simple tracking algorithm, enables the simultaneous tracking of multiple objects by strategically incorporating detections with a low confidence threshold. Conventionally, objects are initially associated with high confidence threshold detections. When the association between objects and detections becomes ambiguous, ByteTrack extends the association to lower confidence threshold detections. One notable drawback of the existing ByteTrack approach is its reliance on a fixed threshold to differentiate between high and low-confidence detections. In response to this limitation, we introduce a novel and adaptive approach. Our proposed method entails a dynamic adjustment of the confidence threshold, leveraging insights derived from overall detections. Through experimentation, we demonstrate the effectiveness of our adaptive confidence threshold technique while maintaining running time compared to ByteTrack.
Penerapan Algoritma K-Means Clustering Untuk Menentukan Kelas Kelompok Bimbingan Belajar Tambahan

Additional learning is a learning program that is carried out outside the school's intracurricular program. This means that additional learning activities are carried out after the regular learning program at school has ended The learning mentor comes from the subject teacher at the school concerned. It is commonly called the afternoon additional learning program. The afternoon additional learning program has its own planning schedule. Additional learning is carried out by students to improve their understanding and deepening of subject material. This goal is related to the preparation of a student to face exams at school, both midterm exams, semester final exams and national final exams. In determining the class of this additional study group using the K-Means Clustering algorithm, the number of data samples will be used is 26 students majoring in science.
NAVIGATING THE CRYPTO JUNGLE: A ROADMAP TO UNDERSTANDING CRYPTO ASSETS AND BLOCKCHAIN

The article provides a comprehensive analysis of the constantly changing and dynamic domain of blockchain technology and cryptocurrency. It explores the foundational principles that support these technologies and the significant consequences they have on the era of digitalization.