Reviewerly/exHarmony huggingface dataset:
Audit of Information Technology Governance on School Operational Cost Flow in SMKN West Jakarta Using COBIT 2019

IT governance is a process that aims to carry out the process of aligning business objectives in an agency in accordance with the business strategy applied to that agency. One sector that is trying to improve IT in improving its governance is the education sector including all SMKNs in West Jakarta. The problem with data management within the West Jakarta Vocational High School is that there is no analysis and research on good and relevant information technology governance in every budget the boss funds issue. This study aims to determine the level of capability and gaps in information technology governance that is currently being implemented, namely the boss fund management information system (Rkas, Arkas, Ready Bop Bos and Headquarters). This study uses analysis based on the 2019 COBIT standard with a focus on the APO12 (Managed Risk) and APO13 (Managed Security) domains to produce a value of capability level that can be used as a reference for analyzing risk management and security management in boss fund management at all West Jakarta Vocational High Schools. The data used in this study came from interviews, questionnaires and direct observation to the research site. The audit results show. APO12 level 2 capability level with a value of 89.29% (Fully Achieved). The capability level of APO12 is level 3 with a value of 77.36% (Largely Achieved) where the capability level of Level 3 APO12 does not reach Fully Achieved so that the capability level of APO12 is at level 3 The capability level of APO13 level 2 with a value of 61.43% (largely Achieved) which does not reach Fully Achieved so The capability level of APO13 is at level 2. The results of this study provide recommendations for aligning the vision, mission, and objectives of boss fund management, so as to improve the function of the boss fund management information system in all SMKN West Jakarta.
Advancing Cloud Technology Security: Leveraging High-Level Coding Languages like Python and SQL for Strengthening Security Systems and Automating Top Control Processes

In today's dynamic business environment, staying ahead of competitors requires the integration of cutting-edge technologies into organizational processes. Cloud Computing, a transformative technological advancement, offers a promising avenue for achieving operational efficiency and innovation. This paper explores the integration of Cloud Computing with two powerful coding languages, Python and SQL, to enhance cloud security and automate control processes. Cloud Computing's adoption has revolutionized resource management through virtualization and diverse computing models. However, it also introduces security challenges like data breaches and unauthorized access. Python and SQL emerge as essential tools for addressing these challenges and automating various control processes. Python's versatility empowers organizations to establish sophisticated security protocols and automate tasks such as intrusion detection, anomaly detection, real-time monitoring, and computer vision. On the other hand, SQL's role involves automating control processes like resource provisioning, scaling, backup, recovery, access control, and database management.&#x0D; Integrating Python and SQL offers a holistic approach to cloud security enhancement. However, challenges such as skill set requirements, code quality, integration, maintenance, scalability, monitoring, and data privacy must be addressed. Fortunately, solutions like Snowpark, dbt, Hex, and Dataiku provide platforms that unify various programming languages, fostering collaboration and streamlining tasks. This convergence of Cloud Computing with Python and SQL presents numerous benefits. Automation enhances efficiency, reduces human error, and ensures consistent control process execution. This synergy allows organizations to achieve scalability, cost savings, improved security, and comprehensive monitoring and reporting.&#x0D; As institutions increasingly d on Cloud Computing to drive innovation and competitiveness, the importance of fortifying these systems against evolving threats cannot be overstated. Integrating Python and SQL represents a pivotal juncture in achieving this goal. By harnessing their combined power, organizations can create robust security mechanisms, streamline operations, and promote cross-functional collaboration. As the digital landscape evolves, embracing this approach is crucial for sustaining success in a rapidly changing environment.
Hybrid Library Service Policy Implementation at Bina Mandiri University Gorontalo

Bina Mandiri University Library (UBM) Gorontalo has made progress in terms of services both conventionally and digitally. However, not all academic communities are interested in using the system, in the sense that there are still library users who need a conventional or manual service system. Therefore, a Hybrid library service policy is needed that combines the two systems. This study aims to measure the level of community satisfaction as service users for the implementation of the Hybrid Library service policy at UBM Gorontalo. The method used is quantitative descriptive using the General Guideline approach for the Preparation of Community Satisfaction Survey by Permenpan RB 2017. The results showed that the implementation of the Hybrid library service policy at UBM Gorontalo as a whole, the assessment of service elements was on average assessed by respondents with good and very good categories. The dominant service element is the compatibility between the service product and the service aspect; The competence of service personnel is quite adequate and has experience in various libraries; and prompt and precise handling of complaints
Honoring 80 Years of Innovation: The Scientific Legacy of M. I. Korabelnikov

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LMQSAS - post-quantum aggregate signature scheme with lazy verification

Aggregated signatures are a type of digital signatures that allow individual signatures obtained by different signers for different messages to be combined into a single signature that provides authenticity, integrity, and non-repudiation for all signed messages. In this case, the length of the resulting aggregated signature is much less than the sum of the lengths of the individual signatures. Sequential aggregated signatures are a subtype of aggregated signatures and allow individual signatures to be combined only during the signing procedure. Signatures of this type can be used in a variety of applications, including secure routing protocols, secure logging, sensor networks, public key infrastructure, blockchain. In this paper we present a new post-quantum aggregated signature scheme with lazy verification that eliminates the need for checking the current sequential aggregated signature during the signature process, which expands its possible application as compared to existing sequential post-quantum signature schemes. The proposed signature scheme is based on post-quantum HFEv- one-way trapdoor permutation, which is known to be resistant to quantum attacks; and Unified Framework of Gentry, O'Neill and Reyzin for obtaining lazy verification using an ideal cipher that can be implemented using the Feistel network and a block cipher. For the presented scheme we provide formal proofs of its security using Bellare-Rogaway game model: the security of the scheme was reduced to the security of a HFEv- one-way trapdoor permutation. We also present a set of parameters for the proposed scheme to meet 80 and 120-bit security levels.
The Representation of ICT Competencies in English Language Reading Skill Modules for Open University

The current global and national issues require students to upgrade their level of ICT competencies in this 21st century. To meet the new standard, there is a need to upgrade the development of the learning materials. Therefore, this study is aimed to portray the ICT competencies covered in the reading skill modules as a concern in addressing the issue of ICT competencies development in education. The qualitative content analysis is used as the method of this study. The main source of data is reading skills modules which are taken from four reading courses (Reading I, II, III, and IV) in Open University English Education Study Program. The results of this study show that 90 representations of ICT competencies are found and mostly represented in the form of Hypermedia, followed by Hyperlink, Multimedia (graphs, tables, and diagrams) and Communication Media (e-mail). The analysis of the table of ISTE NETS (2016) shows that Knowledge Constructor is the most dominant standard (74%), followed by Computational Thinker (16%), and Creative Communicator (10%).
Evaluation of deep learning-based algorithms on person re-identification

At present, CCTVs and multifunctional cameras have been well equipped everywhere in our daily lives. However, most of the public sectors especially the department of public safety are still using the traditional approach, analysing these data by humans. This research presents a summary of the current technologies and algorithms based on deep learning in person Re-identification (Re-id), which aims to recognize a particular individual in several camera views. This paper firstly described the current challenges in using deep learning for person Re-id. Then, a brief introduction of standard person Re-id including feature learning and metric learning is given. Furthermore, the open world Re-id problem is examined from three aspects: person re-id with occlusions, unsupervised learning-based method, and cross-modal person re-Id. The author then evaluated the regularly used datasets and analysed the performance of the algorithms from recent times. Finally, some potential future directions of research for Re-id are proposed.
A novel GBDT-BiLSTM hybrid model on improving day-ahead photovoltaic prediction

Despite being a clean and renewable energy source, photovoltaic (PV) power generation faces severe challenges in operation due to its strong intermittency and volatility compared to the traditional fossil fuel power generation. Accurate predictions are therefore crucial for PV's grid connections and the system security. The existing methods often rely heavily on weather forecasts, the accuracy of which is hard to be guaranteed. This paper proposes a novel GBDT-BiLSTM day-ahead PV forecasting model, which leverages the Teacher Forcing mechanism to combine the strong time-series processing capabilities of BiLSTM with an enhanced GBDT model. Given the uncertainty and volatility inherent in solar energy and weather conditions, the gradient boosting method is employed to update the weak learner, while a decision tree is incorporated to update the strong learner. Additionally, to explore the correlation between photovoltaic power output and historical time-series data, the adaptive gradient descent-based Adam algorithm is utilized to train the bidirectional LSTM model, enhancing the accuracy and stability of mid- to long-term time-series predictions. A prediction experiment, conducting with the real data from a PV power station in Sichuan Province, China, was compared with other methods to verify the model's effectiveness and robustness.
The Development of Digital Competencies in Pre-Service Teachers

Even before the Covid-19 pandemic created a historic pivotal moment for teacher training programmes on a global scale, the development of digital competencies has been regarded as a focal point for teachers. Tertiary institutions had just become accustomed to including technology in their curricula when Covid-19 forced a critical re-evaluation of the current practice. In a mere matter of days, the use of technology was not only a point for discussion anymore; it was lived. The ability of teacher educators to teach with technology had a direct impact on student success. Connectivity, access to suitable devices and sustainable training and support were now important considerations for teacher educators. The harsh reality soon became clear: Although some tertiary institutions could support pre-service teachers regarding access, connectivity and suitable devices, many students lacked the digital competencies to use the technology required for academic progress and success. It is evident that, in post-Covid-19 academia, the enhancement of digital competencies will remain a priority for teacher training programmes. The integration of digital competencies with content and pedagogical knowledge is essential for the successful training of future teachers. This article thus reflects on the efforts of a tertiary institution in South Africa to address the inclusion of technology in its curriculum through newly developed content that focuses on the enhancement of digital competency. Recommendations indicate that the effective inclusion of technology and digital competencies does not have to include major curricular changes but should rather become embedded within modules such as teaching practice.
Analisa Sistem Pakar Metode Bayes Dalam Mendiagnosa Penyakit Tubercolosis

Tubercolosis is an infection caused by acid-resistant bacilli (AFB). Tubercolosis is an infectious disease that can attack anyone through the air. Tuberculosis is a dangerous infectious disease. Tuberculosis is a chronic or chronic disease that can strike between the ages of 15-35 years. How to diagnose Tuberculosis is by expert experts interviewing and then testing sputum samples by using a laboratory to find out positive or negative Tuberculosis so it requires a long time. Therefore we need an Expert System with Bayes method to facilitate diagnosing tuberculosis disease. This expert system was developed using the Microsoft Visual Studio 2010 programming language and by using a Microsoft Access 2010 database.
Does Talker Variability Help Adults Learn Novel Words?

Exposure to multiple talkers has been shown to be beneficial for adults learning speech sounds in a second language and for children learning novel words in a lab-based word learning task. The current study examines the extent to which variability is beneficial for adults in a word learning task and how individual differences in reading and language skills affect performance. Seventy-six participants were randomly assigned to either the multiple-talker (four talkers) or single-talker condition and learned novel object-nonword pairings. To assess word learning, listeners completed three naming tasks throughout the study (after 4 exposures, after 8 exposures, and after a brief delay) in which they had to generate the name of the object. Overall, listeners with higher language skills showed better word learning than those with poorer language skills. No effects of reading ability or talker variability were found. These results suggest that variability may only be beneficial in some tasks.
Kajian Arsitektur Islami Pada Masjid-Masjid di Kecamatan Banda Sakti Kota Lhokseumawe

The emergence of mosque architecture in Indonesia is inseparable from the development of Islam entering Indonesia. The development of Islamic civilization at all times has produced many forms and types of mosques, so that in Indonesia the forms of mosques are very diverse, especially in Aceh, the city of Lhokseumawe. The existence of Islam in Aceh is proven by the many historical mosques with Islamic architecture that have survived to this day, but in fact the function of mosques is more than that, mosques also act as centers of education, spread of Islamic symbols, and become centers of community empowerment. This research process combines field research (field research) and library research, namely using a qualitative descriptive research form. The mosque building itself is synonymous with Islamic architecture, which has seven important elements including gardens, courtyards, domes, pointed arches, muqarnas, minarets and mihrab, while not all elements of Islamic architecture are found in every mosque building.
Evaluation of Fire Protection System on Educational Laboratory Building of Universitas Samudra

This research aims to evaluate fire protection system of educational laboratory building of Universitas Samudra in Langsa city. The building, containing chemical and biological materials that are susceptible to fire, should be protected carefully against potential fires. In addition, sophisticated and expensive equipment for practicum and research including the physical building material are also an additional factor to be preserved according to large investments budget provided. The building, served as practicum and research center will be reviewed from the aspect of active, passive, and management systems that refer to Indonesian standards and regulations. Field observations, data collection and interview were performed in observing the existing condition of the building using descriptive methods. To support the evaluation process, building model is carried out using Sketchup, AutoCAD and Dialux simulation in order to achieve more in-depth observations. The results of this study show that in general the building has met adequate fire protection system standards. The existing volume of the ground water tank is only sufficient for the daily operational needs of water in the laboratory building. However, this amount is not sufficient in a fire scenario. The addition of a new ground water tank and the addition of a Siamese connection to support the need for water imported from fire trucks is required. In addition, there is a need for a fire hazard management team in the building and the need for technical operational standards for the maintenance of all components.
Rewarding Honesty: An Incentive Mechanism to Promote Trust in Blockchain-Based E-commerce

Building trust is a difficult task among strangers over a network. This is because fraud happens when the temptation to cheat becomes greater than the rewards of staying honest. The enormous growth of e-commerce has resulted in cheating and fraud becoming increasingly important issues. Advocates for blockchains argue that this new technology can effectively eliminate misconduct and promote trust among participants. However, recent field experimental studies show that fraud still exists in the blockchain-based marketplace. This article suggests a new design for the arbitration process. A trusted third party is given the right to resolve disputes and reward blockchain cryptographic tokens to honest users. We show that the optimal strategies of individual users involve delivering quality items as described and leaving honest reviews about purchased items.
Accuracy Improvement of Knowledge Transfer from Private Training Data with PATE

Abstract Some machine learning applications involve sensitive training data, such as the medical histories of patients in a clinical trial. A model may inadvertently and implicitly store some training data and careful analysis of the model may reveal the sensitive information. To address this problem, Papernot et al. proposed Private Aggregation of Teacher Ensembles (PATE) framework. PATE transfers the knowledge from the teacher model to the student model with teachers training on disjoint data and privacy guaranteed by noisy aggregation of teachers? predictions. However, PATE has a limit that it requires disjoint training data for a large number of teachers. If the number of teachers is small, then the student model has a poor accuracy. To address the issue of the PATE, we propose an efficient algorithm to improve the student accuracy when the number of teachers is small. In order to validate the performance of our algorithm, we conduct a series of experiments on MNIST and BreastCancer. The evaluation shows our algorithm improves on the original PATE with both high accuracy and strong privacy. For example, when the number of teachers n = 50 and the privacy budget = 3.76, the student of our algorithm achieves 92.1% accuracy while the original PATE only has 60.0%.
Saul Robot: A Cutting-Edge Weapon against Ebola- A Comprehensive Review of its Impact on Infection Eradication in Quarantine Environments

The emergence of highly infectious diseases like Ebola has emphasized the critical need for advanced technologies in infection control and eradication. The Saul Robot, a state-of-the-art automated system, has gained prominence as a potent tool in combating Ebola outbreaks within quarantine environments. This review paper comprehensively assesses the efficacy and impact of the Saul Robot in eradicating infections in high-risk settings. Through an exhaustive analysis of research studies, case reports, and field applications, this paper examines the robot's capabilities, limitations, and contributions to infection containment. The review encompasses the robot's integration of cutting-edge technologies, including artificial intelligence, remote operation, and advanced sensing mechanisms, enabling real-time monitoring, disinfection, and decontamination. Key aspects, such as operational efficiency, cost-effectiveness, and human-robot collaboration, are scrutinized to evaluate the overall effectiveness of the Saul Robot. The paper also discusses challenges faced during implementation and highlights areas for potential improvement. By synthesizing diverse findings, this review provides valuable insights into the transformative role of the Saul Robot in revolutionizing infection control strategies, thereby enhancing global preparedness against deadly diseases like Ebola.
MarQO: A query optimizer in multilingual environment for information retrieval in Marathi language

Information retrieval is a crucial component of modern information systems. A significant portion of the vast amount of information stored worldwide is in local languages. While most information retrieval systems are designed primarily for English, there is a growing need for these systems to work with data in languages other than English. Cross Language Information Retrieval (CLIR) systems play a pivotal role in enabling information retrieval across multiple languages. However, these systems often face challenges due to ambiguities in query translation, impacting retrieval accuracy. This paper introduces "MarQO," a query optimizer designed to address these challenges in the context of Marathi language. MarQO employs a multi-stage approach, including lexical processing, extraction of multi-word terms, synonym addition, phrasal translations, utilization of word co-occurrence statistics, and more. By disambiguating query keyword translations, MarQO significantly improves the accuracy of translations, thereby leading to more relevant document retrieval results.
Emotional Text Detection dengan Long Short Term Memory (LSTM)

Emotional Text Detection is a technique in natural language processing that aims to identify the emotions contained in conversations or text messages. The LSTM (Long Short-Term Memory) method is one of the techniques used in natural language processing to model and predict sequential data. In this study, we propose the use of the LSTM method for emotion detection in conversation. The dataset used is a conversational dataset that contains positive, negative, and neutral emotions. We process datasets using data pre-processing techniques such as tokenization, data cleansing and one-hot encoding. Then, we train the LSTM model on the processed dataset and obtain evaluation results using accuracy metrics. The experimental results show that the LSTM model can be used to detect emotions in conversation with a good degree of accuracy. In addition, we also conducted an analysis on the prediction results of the model and showed that the LSTM model can correctly identify emotions. In conclusion, the LSTM method can be used to detect emotions in conversation with a good degree of accuracy. This method can be used to improve user experience in chat applications and increase the effectiveness of human and machine interactions.