Reviewerly/exHarmony huggingface dataset:
Agilidad Organizativa, Resiliencia del Proyecto y Prácticas Laborales de Alto Rendimiento: Una Receta para el Éxito del Proyecto

A dynamic business environment, characterized by fiercely competitive and fast-changing markets, has made the ability to respond quickly to change and manage uncertainty essential for organizational survival. Consequently, organisational agility has become imperative for achieving project success. Traditionally, project success was determined by the achievement of project goals within the constraints of time, budget, and scope. However, the perception of project success has evolved to encompass a multidimensional approach, considering the project's impact on the team, customer, business success, and capacity-building for the future. These dimensions should be used to measure project success in volatile environments. Project resilience, which refers to a project's ability to withstand and recover from unexpected events and disruptions, could act as a mediator between organisational agility and project success. Project resilience involves taking a holistic view of the organization and its ability to adapt to different scenarios, including various disturbances in the system. However, the concept of resilience still needs to be explored further in project studies. In this paper, the authors propose a research model for testing the relationship between organisational agility and project success, with project resilience acting as a mediating factor and high-performance work practices as a moderating factor. Keywords: Project Success; Project Management; Research Model; Organizational Agility; Project Resilience
Performance of the Solar PV Module of the Dual Solar Axis Tracker of a Smart Home Monitoring System

Nowadays, solar PV power plays a significant role in sustainable electrification. However, because of the positions of PV modules, the electricity generated by the solar system is drastically reduced. This study aims to design, build, and deploy a dual-axis solar PV tracking system. This study describes a module control tracking system based on artificial intelligence for more effective solar energy harvesting. The passive closed-loop system often employs two opposing solar-powered actuators to receive equal solar radiation only when the mirrors point directly toward the sun to provide feedback from its location. Without using feedback to verify the intended result, the controller enters a mathematical equation into the tracking system using only the current state of the system and the algorithm. The solar tracker system rotates the solar panel toward the sun or another light source using an Arduino board, two DC motors, four light dependent resistors, four resistors, and a monocrystalline panel. In conclusion, the project’s performance after testing satisfied the design criteria and is likely to boost the efficiency of solar panels. Keywords: photovoltaic module, Arduino board, smart home monitoring system, solar panel tracking. https://doi.org/10.55463/issn.1674-2974.50.9.3
YOGA: Adaptive Layer-Wise Model Aggregation for Decentralized Federated Learning

Traditional Federated Learning (FL) is a promising paradigm that enables massive edge clients to collaboratively train deep neural network (DNN) models without exposing raw data to the parameter server (PS). To avoid the bottleneck on the PS, Decentralized Federated Learning (DFL), which utilizes peer-to-peer (P2P) communication without maintaining a global model, has been proposed. Nevertheless, DFL still faces two critical challenges, i.e., limited communication bandwidth and not independent and identically distributed (non-IID) local data, thus hindering efficient model training. Existing works commonly assume <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full</i> model aggregation at periodic intervals, i.e., clients periodically collect models from peers. To reduce the communication cost, these methods allow clients to collect model(s) from selected peers, but often result in a significant degradation of model accuracy when dealing with non-IID data. Alternatively, the layer-wise aggregation mechanism has been proposed to alleviate communication overhead under the PS architecture, but its potential in DFL remains rarely explored yet. To this end, we propose an efficient DFL framework YOGA that adaptively performs layer-wise model aggregation and training. Specifically, YOGA first generates the ranking of layers in the model according to the learning speed and layer-wise divergence. Combining with the layer ranking and peers’ status information (i.e., data distribution and communication capability), we propose the max-match (MM) algorithm to generate the proper layer-wise model aggregation policy for the clients. Extensive experiments on DNN models and datasets show that YOGA saves communication cost by about 45% without sacrificing the model performance compared with the baselines, and provides 1.53-3.5 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times$</tex-math> </inline-formula> speedup on the physical platform.
Privacy preserving in health data collection using LDP and IOTA

In hospitals there is a huge amount of data that can be used in research. Unfortunately, the transmission of this data through an insecure link can lead to privacy issues. Anonymisation techniques are commonly used to achieve the privacy of patients. However, existing methodologies are not enough to protect data from attacks, while dependency on third parties and for achieving privacy could compromise utility. Data collectors are dependent on leaders (who could be adversary) for data storage and publication. Internal and external privacy breaches are also possible. To minimise the effects of these attacks, we have proposed a protocol that uses anonymisation techniques ie local differential privacy during the data transmission and IOTA for data storage. The results of an experiment revealed that the IOTA method performed better than the blockchain when it comes to the computation overhead. The main advantage of using local differential privacy is that it provides a trade-off between privacy and utility.
A Gynocritical Reading of Laut Bercerita in Contemporary Indonesian Children's Literature (Pembacaan Kritis Laut Bercerita Sebagai Karya Sastra Anak Indonesia Masa Kini dari Perspektif Ginokritik)

Penelitian ini mengeksplorasi representasi karakter perempuan dalam sastra anak Indonesia dengan menggunakan pendekatan ginokritik. Objek penelitian adalah novel Laut Bercerita karya Leila S. Chudori. Penelitian ini menggunakan teori ginokritik yang dikembangkan oleh Elaine Showalter. Penelitian dilakukan melalui analisis kualitatif novel, menggunakan teknik analisis isi dan close reading. Hasil penelitian menunjukkan pentingnya menerapkan pendekatan ginokritik dalam menganalisis sastra anak, karena hal ini mengungkapkan representasi karakter perempuan yang kompleks dan nuansa dalam novel. Penelitian ini mengungkap bagaimana karakter perempuan dalam Laut Bercerita menunjukkan agensi, pemberdayaan, dan perlawanan terhadap norma patriarki, menantang stereotip gender tradisional. Implikasi penelitian ini mendorong penerapan pendekatan ginokritik dalam menganalisis sastra anak, sebagai alat yang kuat untuk mempromosikan kesetaraan gender dan membangun hubungan gender yang lebih adil dalam masyarakat Indonesia. This study explores the representation of female characters in Indonesian children's literature using a gynocritical approach. The object of the study is the novel Laut Bercerita by Leila S. Chudori. The study employs the gynocritical theory developed by Elaine Showalter, which focuses on the experiences and perspectives of women in literature. The research is conducted through a qualitative analysis of the novel, using content analysis and close reading techniques. The results of the study demonstrate the importance of applying a gynocritical approach in analyzing children's literature, as it uncovers the nuanced and complex representation of female characters in the novel. The study reveals how the female characters in Laut Bercerita exhibit agency, empowerment, and resistance to patriarchal norms, challenging traditional gender stereotypes. The study concludes that gynocritical readings can contribute to broader efforts to promote gender equality, challenging traditional gender stereotypes and promoting more equitable gender relations.
A WEB APPLICATION FOR REAL-TIME PHISHING WEBSITE DETECTION

The rapid proliferation of the internet has led to an alarming increase in cyber threats, particularly phishing attacks. Phishing websites impersonate legitimate ones, luring users into divulging sensitive information such as login credentials and financial details. Detecting and preventing such attacks are crucial for safeguarding user privacy and security. This paper presents the development of a web application for real-time phishing website detection. The application employs machine learning techniques, specifically the XGBoost algorithm, to analyze the characteristics of URLs and classify them as either legitimate or phishing. It offers users a simple and effective way to check the authenticity of websites before interacting with them.The web application provides an intuitive user interface, allowing users to enter a URL and receive an instant assessment of its legitimacy. The underlying machine learning model is trained on a diverse dataset of legitimate and phishing URLs, ensuring robust detection capabilities. Additionally, the application provides insights into the features used for classification, enhancing user awareness of potential threats. The proposed web application serves as a valuable tool in the fight against phishing attacks, empowering users to make informed decisions when navigating the web. Its real-time detection capabilities and user-friendly interface make it accessible to a wide audience, contributing to a safer online environment. KEYWORDS Web Application, Phishing Detection, Machine Learning, XGBoost, User Privacy.
Design and Implementation of Tourism News Information Retrieval System using Modified Cosine Similarity

Technological developments cannot be denied a very meaningful impact on human life so that what was previously done traditionally is now completely digital, for example conventional news media which has been transformed into an online news portal so that it can still reach its readers. Online news portals provide a lot of up-to-date information, including tourism news, which is an industry that continues to grow and has the opportunity to create new jobs for the community. Currently, to search for tourism news, people only need to type a keyword (query) in the search engine which will then display the latest news about tourism. However, not all tourism news displayed matches what they are looking for, so readers have to re-check which takes a lot of time. Therefore, a tourism news retrieval system that can display the most relevant tourism news to the query is proposed. The Modified Cosine method shows good results in document clustering to bring the inter-cluster distance closer. This study uses the Modified Cosine method and TF-IDF weighting schema to determine the value of precision, recall, and f-measure in calculating the similarity of the query to tourism news documents. The system has been tested using 3 types of queries, with 5 different words each. The test results show that Modified Cosine method obtained best precision value in the test using 5 words in the query and the F-Measure value and the best recall on the 3 words test in query.
Clustering Vaksinasi Penyakit Mulut dan Kuku Menggunakan Algoritma Fuzzy C-Means

Foot and Mouth Disease is a disease that attacks cloven hooves, this disease spreads very quickly and the mortality rate of infected animals is up to 100%. FMD is caused by type A picornaviridae virus, namely Apthaee epizootecae, which has a development period of 1-14 days after the animal is infected. The delay in handling it can cause many livestock to die and have an impact on cattle farmers. One of the steps taken to prevent the spread of this disease is to eradicate all livestock. The Riau Provincial Government has taken steps to prevent vaccination of all livestock in Riau Province in the form of preventing this disease from becoming more widespread. From these problems, this research will form a data cluster for the PMK program in Riau Province so that the government can improve supervision of livestock to prevent re-outbreaks of foot and mouth disease in Riau Province. The method used is data mining with the Fuzzy C-means algorithm and the data used comes from the Department of Animal Husbandry and Animal Health in Riau Province. The best cluster results after testing is 2 clusters. The most numerous clusters are in cluster 1 with a total of 48704 cows and cluster 2 with a total of 21232. The validity test using the DBI gets a value of 0.416, so it is still far from good
Personalized Federated Learning Algorithm with Adaptive Clustering for Non-IID IoT Data Incorporating Multi-Task Learning and Neural Network Model Characteristics

The proliferation of IoT devices has led to an unprecedented integration of machine learning techniques, raising concerns about data privacy. To address these concerns, federated learning has been introduced. However, practical implementations face challenges, including communication costs, data and device heterogeneity, and privacy security. This paper proposes an innovative approach within the context of federated learning, introducing a personalized joint learning algorithm for Non-IID IoT data. This algorithm incorporates multi-task learning principles and leverages neural network model characteristics. To overcome data heterogeneity, we present a novel clustering algorithm designed specifically for federated learning. Unlike conventional methods that require a predetermined number of clusters, our approach utilizes automatic clustering, eliminating the need for fixed cluster specifications. Extensive experimentation demonstrates the exceptional performance of the proposed algorithm, particularly in scenarios with specific client distributions. By significantly improving the accuracy of trained models, our approach not only addresses data heterogeneity but also strengthens privacy preservation in federated learning. In conclusion, we offer a robust solution to the practical challenges of federated learning in IoT environments. By combining personalized joint learning, automatic clustering, and neural network model characteristics, we facilitate more effective and privacy-conscious machine learning in Non-IID IoT data settings.
BAHS: A Blockchain-Aided Hash-Based Signature Scheme

Hash-based one-time signatures are becoming increasingly important as they are post-quantum safe and have been used in multi-cast communication and other applications. However, managing the state of such signatures can present a significant challenge, as signers are typically responsible for ensuring that the state cannot be reused. Recently, blockchain, as a public platform, is used to design revocation management and status verification systems. While blockchain revocation is attractive, many well-known blockchains make use of ECDSA as their underlying signature scheme, and this is not post-quantum safe. Researchers have been working on replacing ECDSA with post-quantum signature schemes but they are much more costly. In this paper, we introduce a new one-time signature scheme, called Blockchain-Aided Hash-based Signature (BAHS), in which a hash-based commitment scheme acts as the building block, and signers’ commitments and opened commitments are publicly accessible via a distributed blockchain. A signature is formed from the commitment/opened commitment and blockchain. Unlike existing blockchain systems, the commitment in BAHS is simpler than that in most existing hash-based one-time signature schemes or other post-quantum signature schemes. We provide a formal security model for the BAHS scheme and give the security proof. Finally, we have implemented our BAHS scheme and the result shows its practicality.
Design and Implementation of a Traffic Sign Recognition System using Computer Vision and Clustering

The article centers around the challenge of designing and implementing a system capable of identifying six prescriptive traffic signs. Instead of discussing in detail the already existing approaches for recognizing traffic signs, the paper focuses on the methodology and algorithms used in the development of the proposed system. The effect of different external conditions on the accuracy of the system through a series of experiments is examined. The paper covers an overview of the relevant theoretical concepts, a description of the algorithm development process using the OpenCV library, and an evaluation of the system’s efficiency and performance.
Improving the international competitiveness of universities in the global digital space

The article examines the competitiveness concept of universities, presented by modern researchers. The most important factors of increasing the international competitiveness of universities in the global digital space are identified and analyzed. The internationalization importance of teaching personnel and student staff, as well as the introduction of international practices using digital technologies into the universities educational programs’ is established. The results of a sociological survey of students in Russian higher educational institutions are presented. The factors that have the greatest influence on the choice of university and educational program by students are determined. The attitude of students to gaining international learning experience in the digital educational space has been studied and practice-oriented recommendations for improving the international competitiveness of universities have been prepared. In addition, foreign policy factors influencing the intensity of international activity of Russian higher education institutions were taken into account when making recommendations.
Internal control and its role in enhancing the credibility of financial reporting and transparency: an exploratory study of the opinions of a sample of auditors at the Northern Technical University

This study aims to identify the correlation and effect between internal control (an independent variable) and its dimensions represented by (control environment, information and communication systems, control procedures, risk assessment, control) and transparency as a (dependent variable) through its dimensions represented by (trust and empowerment, Issue, Integrity, Participation) through a survey of the opinions of a sample of accountants and auditors at the Northern Technical University.&#x0D; To achieve the goal of the study, a questionnaire was prepared and distributed in the researched organization as a random sample of ( ) individuals. The study relied on the descriptive analytical approach to answer the hypotheses of the study and reach the results.&#x0D; The study concluded that internal control has a positive and significant impact on transparency, while the influence process extended to reflect the impact of each dimension of internal control on transparency.&#x0D; The study recommended the need to work on continuous training for accountants and auditors to deepen their awareness of the importance of internal control in order to achieve transparency and conduct more studies to serve Iraqi organizations.
PERANCANGAN KAMPANYE “BATIK NONGKRONG” MENGGUNAKAN BATIK SURABAYA UNTUK ANAK MUDA

Batik is an intangible cultural heritage of Indonesia that has been recognized by UNESCO and needs to be preserved. But unfortunately there are limitations to wearing batik, many consider batik as formal clothing that is only suitable for formal events such as weddings, offices, and other events. The purpose of this design is to normalize the use of batik clothes in various places, therefore a design for this batik hangout campaign was created using qualitative research methods by interviewing several sources who understand design. With this design, it is hoped that batik can be accepted by young people for use in casual activities, starting from cafes, entertainment facilities, and other leisure activities. The design result is an Instagram social media account named @banong.id, Instagram social media was chosen because it is easily accessible by young people.
Parameters and Variables

Variables have been part of the Unix shell since its inception more than 30 years ago, but their features have grown over the years. The standard Unix shell now has parameter expansions that perform sophisticated manipulations on their contents. bash adds even more expansion capabilities as well as indexed and associative arrays.
Pelatihan Media Interaktif Mentimeter dalam Memberdayakan Kualitas Teknologi Guru di MTS Arifah Gowa

Mentimeter is an interactive application that is very important to use in learning, but receives little attention. The aim of this research is to provide mentimeter training in empowering the quality of teacher technology at MTS Arifah Gowa. The methods used in this research are; a) Observation; b) Discussion and Question and Answer; c) Independent/Structured Assignments; d) Assignment Review; and e) Direct and online consultation. Based on the results of the implementation of this training activity, it can be concluded that there has been an increase in teachers' abilities regarding mentimeter technology. These results can be seen from the evaluation results which show that 87% of MTS Arifah Gowa teachers understand mentimeter technology in the good category.
Research competence as the basis of students’ professional competence formation

The purpose of the research is to investigate research competence as the basis of students’ professional competence formation. The definition to the notion of research competence as a complex personal formation has been given. Analysis of a significant number of studies and publications with the focus on the study of theoretical foundations of students’ scientific research activity organization, scientific research organizations, formation of readiness for scientific research activity has been done. Researchers’ approaches to defining the essence of the concept "research competence" have been distinguished. The characteristics of research competence, which are generally recognized as common and mandatory, have been identified. Research competence is considered as an indicator of the quality of education and is characterized by future specialists’ adaptation abilities to the conditions of multi-role research activity, including the ability to design and implement it, personal and professional self-realization, establishing interpersonal, business, professional, social connections and the ability to continue self-education.
On Distribution Dependent Sub-Logarithmic Query Time of Learned Indexing.

A fundamental problem in data management is to find the elements in an array that match a query. Recently, learned indexes are being extensively used to solve this problem, where they learn a model to predict the location of the items in the array. They are empirically shown to outperform non-learned methods (e.g., B-trees or binary search that answer queries in O(logn) time) by orders of magnitude. However, success of learned indexes has not been theoretically justified. Only existing attempt shows the same query time of O(logn), but with a constant factor improvement in space complexity over non-learned methods, under some assumptions on data distribution. In this paper, we significantly strengthen this result, showing that under mild assumptions on data distribution, and the same space complexity as non-learned methods, learned indexes can answer queries in O(loglogn) expected query time. We also show that allowing for slightly larger but still near-linear space overhead, a learned index can achieve O(1) expected query time. Our results theoretically prove learned indexes are orders of magnitude faster than non-learned methods, theoretically grounding their empirical success.