Reviewerly/exHarmony huggingface dataset:
Compilation of Alumni Profiles Based on Tracker Study in the Doctoral Study Program (S3) of Postgraduate Education, State University of Gorontalo

The Doctor of Education Program at State University of Gorontalo will carry out the accreditation, where the alumni's track record and user satisfaction have a sizable portion in the accreditation assessment. This service aims to identify, record, formulate and analyze the profiles of graduate students based on tracer studies in the postgraduate doctoral study program, State University of Gorontalo. In particular, this study aims to: 1) Describe tracer study-based student and alumni profiles in the Postgraduate Education Doctoral Study Program, State University of Gorontalo; 2) Mapping the curriculum in the Postgraduate Education Doctoral Study Program, State University of Gorontalo. This service is designed using a mix method with data collection techniques using questionnaires, observation, interviews, and documentation. Data analysis techniques using quantitative analysis and qualitative analysis. This service is expected to: a) be input or reference material in improving the quality of students and alumni in the Doctoral Study Program of Postgraduate Education, State University of Gorontalo, b) become a reference in carrying out study and postgraduate program policies in developing the vision and mission of the organization, c) be a reference in the development of study programs and postgraduate programs in preparing accreditation forms in the future, and d) be useful for the formulation of work programs for the Doctor of Education Study Program that are more responsive to the needs of students and alumni. The end result of this service is expected to be; 1) Formulate a Compilation of Profiles of Doctoral Education Graduates for Users of Postgraduate Education Doctoral Graduates, State University of Gorontalo, and also 2) a Compilation of the Curriculum for the Doctoral Study Program in Postgraduate Education, State University of Gorontalo, with the Outcomes of the Collaborative Service Program being; 1) Consolidated Service Report Formulation of Doctoral Education Graduate Profiles and Doctoral Education Curriculum for Users of Doctoral Graduates (S3) in Postgraduate Education, State University of Gorontalo. 2) Service Journal. 3) Publication of Service Activities in Gorontalo Post or Electronic Print Media; Instagram and FB. 4) IPR Grand Design Service Research Report Based on Tracer Study at the Doctoral Study Program of Postgraduate Education, State University of Gorontalo.
An experimental review of the ensemble-based data stream classification algorithms in non-stationary environments

Abstract Data streams are sequences of fast-growing and high-speed data points that typically suffer from the infinite length, large volume, and specifically unstable data distribution. These potential issues of data streams bold the necessity of data stream mining tasks. Ensemble learning as a prevalent classification approach is widely used in data stream mining studies. Besides the impressive performance of ensemble learning algorithms in providing a collection of diverse and accurate classifiers, they are specifically efficient in handling non-stationary data streams. Due to the component-based nature and the chance of dynamic updates for the components of the ensemble, this category is appropriate for dynamically learning the changing concepts of the data. This paper aims to provide a thorough review of the most significant ensemble-based data stream classification approaches, along with a discussion about the potential issues of the non-stationary data streams. Furthermore, comprehensive experimental analysis is performed to compare the classification performance of the well-known state-of-the-art ensemble-based data stream classification approaches on 24 synthetic non-stationary data streams. The superiority of the approaches is proved by conducting various statistical tests.
Enhancing Cybersecurity Awareness among Banking Employees in Malaysia: Strategies, Implications, and Research Insights

Cybersecurity awareness is important for employees in Malaysia's banking sector.Protecting valuable data from theft and misuse requires fostering a culture of awareness and providing comprehensive security training.Unintentional disclosure of sensitive information poses a significant risk, leading to cyberattacks and loss of clients, eventually impacting profitability.This paper explores the dependent variable of cybersecurity awareness and delves into independent variable analysis, strategies, and the existing lack of cyber security measures.A sample of 384 employees from a Malaysian banking organization was selected using the Krejcie & Morgan sampling technique.Data analysis was conducted and demonstrating good internal consistency and mitigating concerns about measurement reliability.Furthermore, collinearity issues were absent, as indicated by VIF values below ten and tolerance values exceeding 0.1 for the examined independent variables.
Hierarchical Attentive Feature Aggregation for Person Re-identification

Abstract Recent efforts on person re-identification have shown promising results by learning discriminative features via multi-branch network. To further boost feature discrimination, attention mechanism has also been extensively employed. However, the branches in existing models generally work independently, which may compromise the ability of mining diverse features. To mitigate this issue, a novel framework called Hierarchical Attentive Feature Aggregation (Hi-AFA) is proposed. In Hi-AFA, a hierarchical aggregation mechanism is applied to learn attentive features. The current feature map is not only fed into the next stage, but also aggerated into another branch, leading to hierarchical feature flows along depth and parallel branches. We also present a simple feature suppression operation and a lightweight dual attention module with only a few parameters to guide feature learning. By this manner, the branches could cooperate to mine richer and more diverse feature representations. We integrate the hierarchical aggregation and multi-granularity feature learning into a unified architecture that builds upon OSNet, resulting a resource-economical and effective person re-identification model. Extensive experiments on three public datasets, including Market-1501, DukeMTMC-reID, and CUHK03, are conducted to validate the effectiveness of the proposed method, and results show that state-of-the-art performance is achieved.
Implementasi Sistem Room Reservation Berbasis Web dengan PHPRad

Abstract - The Shanti Bhuana Institute is one of the colleges in the Bengkayang. The campus, which was founded in 2016, is now getting busier, both because of the increasing number of students from year to year, as well as the number of employees working there. This raises problems, one of which is related to the reservation (borrowing) of several rooms that can be used freely, both for lectures and meetings. Previously, recording room reservations was still done manually using paper. In addition, recording reservations is also not flexible because someone must to go to an authorized employee to record reservations. To adapt to the challenges of the digital era and the green economy, it is necessary to reduce the use of paper (paperless) by building a website-based room reservation system where users can make reservations that can be accessed in real-time anytime and anywhere. The research method used will apply the principle of object oriented programming. The system was built using the PHPRad framework with system implementation carried out at the Shanti Bhuana Institute. System implementation went well with a total of 5 rooms that could be reserved and 168 reservations had occurred since the system was implemented.
Eco-friendly mortars with granite powder and fly ash and their prediction with artificial neural networks

This paper focuses on mortar in which cement is partially replaced with siliceous fly ash and granite powder and subjected to abrasion.To optimise the process of testing abrasion resistance, artificial neural network (ANN) was proposed to predict results.Predicting abrasion resistance of mortar, especially with addition of wastes is not widely explored and requires greater academic attention.The possibility of applying machine learning is reducing amount of destructive tests methods in favour of prediction, therefore sustainable approach.This study confirms that it is possible to create eco-friendly mortar with siliceous fly ash and / or granite powder and that these materials do not deteriorate the surface of mortar samples.The most similar results of abrasion resistance to reference samples with only Portland cement were obtained for combination of fly ash and granite powder.The application of ANN to predict abrasion resistance of tested mortars showed very accurate results with the network's worst quality at 0.967 (with 1.0 as ideal result).The quality of the network relies on properly selected and detailed inputs.This study proved satisfactory results of predicting abrasion resistance based on mortar's components only.
A stepwise Bayesian updating approach by enhancing an active learning Gaussian process regression model

Bayesian updating framework is regarded as a promising approach for probabilistic calibration and uncertainty quantification, and the main obstacle for practical engineering problems is the high computational cost, especially for time-consuming models.The attractive point of traditional Bayesian updating with structural reliability methods (BUS) is to reformulate Bayesian updating into a structural reliability problem by constructing the limit state function with the likelihood and an auxiliary random variable.This paper proposes a step-wise Bayesian updating approach, by developing a varying observation domain-based strategy to reduce the dimensionality and nonlinearity of the constructed limit state function.In our work, the reliability problem is decomposed into a series of sub-problems by attributing random samples of the auxiliary random variable to each sub-problem.Thereafter, the exponential relation in each limit state function degenerates into squared linear additive type, so as to comprehensively reduce the nonlinearity.To overcome the inefficiency caused by rare event, this paper further develops an active learning procedure based on Gaussian process regression (GPR) to approximate a series of induced limit states as well as the acceptance rates.The main advantage of this procedure is of sharing the common performance function evaluations which can largely reduce the computational cost.
Investigating the Role of Speaker Counter in Handling Overlapping Speeches in Speaker Diarization Systems

In real-life conversations, meetings, or debates, there are often situations where many people speak at the same time, leading to overlapping speech segments. Such overlapping speech is an extremely challenging problem for the speaker diarization task. The widely used clustering-based diarization approaches perform quite poorly under such situations due to their limited capabilities in handling overlapping speeches. This paper investigates a speaker diarization framework in which a new building block, called speaker count, is integrated. Such speaker counter predicts the number of active speakers in each analyzing audio window, then its output is used in the conventional re-segmentation step of the diarization pipelines in order to better label the active speakers in each considered segment. We also investigate the effect of the analyzing audio window size on diarization performance by theoretical analysis. We claim that the speaker count block ensures a lower diarization error rate when the analyzing window size is small enough. Experiment results obtained from two state-of-the-art diarization systems with different settings on two benchmark datasets, AMI Headset mix and DIHARD III, confirmed the effectiveness of the proposed approach.
Analisa Quality of Services Pada Jaringan Sekolah Dengan Metode Queue Tree Studi Kasus: Lab Bahasa SMAN 47 Jakarta

Abstract - The use of the internet with a large number of users has problems with fair and equitable use of bandwidth. The use of the internet in the computer network of SMAN 47 Jakarta is a very large number of students and teachers. Computer networks in schools are provided not only to support the teaching and learning process but also to support the very high mobility of the learning community. Of the many teachers, staff and students at SMAN 47 Jakarta who are active internet users, some users download (using the Download manager), and some use it for browsing, running YouTube, Twitter, Instagram applications, and so on. . With limited bandwidth and no regulation of bandwidth usage, causing unfair and equitable distribution of bandwidth, bandwidth management is carried out using the simple queue method, as well as to make it easier for network administrators to configure. The success of bandwidth management is carried out through Quality of Services (QOS) with indicators: throughput, jitter, packet loss and delay. The value used as a barometer of QOS success is the THIPON index. From the test results of the proposed system made by the author, a case study in the Lab room, that the implementation of bandwidth management with the simple queue method resulted in a score of 4 THIPON indexes on all measured QoS indicators. Through this bandwidth management, network administrators can provide guarantees to users on the availability and fairness of bandwidth usage according to the user's priorities and needs. It is necessary to make a Standard Operational Procedure (SOP) regarding the bandwidth management mechanism so that bandwidth management can be carried out in accordance with the standards and carried out by whoever is the network administrator.
The Impact of Rosetta Stone on Students’ Pronunciation for English Subject

The Objective of the study was to ascertain how Rosetta Stone in English class affected students' ability to pronounce English.This methodology employed a quasi-experimental pretest-posttest design with 30 samples split into two groups.The technique used was non-probability sampling.Pronunciation test technique was employed for data collecting.To compare Pronunciation abilities before and after using Rosetta Stone, data analysis used a t-test.The findings demonstrated that the t-Stat statements derived from the material discussed had value.A key element of Pronunciation is the students' capacity to pronounce the words and sentences that are practiced by imitating the application.The findings of this study have significance for students' knowledge of how to pronounce the words and sentences correctly and influence their level of pronunciation.The findings demonstrated that the t-test-table value was (1,699 <9,950 > 1,331), indicating that Rosetta Stone gave a significant effect on treatment class on students' pronunciation.This result finding recommend other researcher to use the Rosetta Stone as an alternative media used for future.
Segmentasi Jalan Berlubang Citra Jalan Raya Menggunakan Metode Thresholding Dan K-Means

Abstract - One of the important meanings for human life is the road. The road is used as a means of transportation which has very useful planning in the development of human life. In 2018, based on statistical data, the number of motor vehicle users in Indonesia is increasing every year, reaching 146,858,759 units. Seeing the number of the enhancement of motor vehicle users, the impact occured. Many roads are damaged and the conditions are very unsettling and concerned for the road users. One of the most common damages is that there are many holes in the road, which can increase the accidents’ risk. Based on science and technology development, one of the causes for the lengthy time to repair road damage is due to calculating the area of each damage manually and it takes a relatively long time. For this reason, this study aims to perform image processing in the form of segmentation on the image of a potholed road on the highway using the Thresholding algorithm and K-Means, followed by the calculation of the area and perimeter of the hole. The results of this study were obtained with 106 images of potholed roads, then segmented and calculated the area and circumference can be read properly using a program that has been designed. Keywords: Segmentation, Thresholding, K-Means
A Study on the Usage of the ‘V+C₁C₂+NP&lt;SUB&gt;处&lt;/SUB&gt;’ Structure in Modern Chinese

In Modern Chinese grammar, it is stipulated that the locative object following a verb can only be placed in between the compound directional complement. However, there is a significant phenomenon of the locative object appearing after the compound directional complement in our surroundings. How is this ungrammatical word order phenomenon distributed and what differences exist between it and the normal grammatical expression? Based on previous research, this article presents a comparative analysis of a large corpus of data, enumerating the distribution and syntactic differences between two types of expressions. For instance, the verb category represented by “进” and “回” tends to use the intermediate-object structure, while the verb category represented by “出” and “过” tends to use the post-object structure in the compound directional complement. From an information structure perspective, the intermediate-object structure is often employed to emphasize the locative object at the end of a sentence, while the post-object structure is frequently used in serial verb constructions to highlight the following constituent. Through an analysis of the corpus and a comparison with actual usage through questionnaire surveys, this article discovers that the phenomenon of placing the locative object after the compound directional complement deserves our attention. It is not simply ungrammatical sentences but rather a special word order with usage patterns and tendencies. Moreover, to some extent, it can fulfill the needs of specific syntax and semantics.
Application of Machine Learning in Malaysia Sign Language Translation

Sign language serves as a vital form of communication for individuals with hearing impairments, enabling seamless interaction among those who cannot hear. It is a widely used means of communication worldwide, facilitating communication within the deaf community. In Malaysia, Malaysian Sign Language (MySL) prevails as the primary sign language employed by the deaf community. However, sign languages possess unique grammatical rules and structures, making them unfamiliar to hearing individuals, leading to potential misunderstandings and communication barriers. This project aimed at developing a sign language translator capable of translating 24 alphabets based on hand gestures. The system employs a dataset of sign language alphabet images, gathered and trained using the Teachable Machine. To evaluate the translator's performance, response time is thoroughly analyzed. The results indicate that 18 out of the 24 alphabets can be recognized within 5 seconds, displaying promising accuracy. By bridging the communication gap between deaf and hearing individuals, the findings of this study hold substantial potential to enhance interactions and foster better understanding between these two communities. The sign language translator represents a significant step towards inclusive communication and improved accessibility for individuals with hearing impairments
Exploration of the ideal sanskrit grammar for natural language processing: An analysis of laukik and Vedic Sanskrit

Prior to the writing of this paper, there has been a lot of research into the applicability of Sanskrit as a language for programming.This further expanded into research about how applicable it is for Natural Language Processing (NLP).Reasons cited include its syntax and almost mathematical grammar, along with its lack of exceptions.This made Sanskrit a major candidate in the field of natural language processing.However, since then, research in the field has not progressed further, and we are yet to see Sanskrit being used in natural language processing.Furthermore, Sanskrit is a language with a rich history, dating back several thousand years, and it has evolved a lot since its conception.This gave way to even more possibilities of the usage of Sanskrit, since there were ancient and modern forms of the language.Therefore, researching the ideal version of Sanskrit for usage in Natural Language processing was chosen as a topic, and Vedic(ancient) and Laukik(modern) versions of the language were compared.After conducting research, it was found that Laukik Sanskrit seems to be a more refined version of the language.It has eliminated several ambiguities, and streamlined the grammar.This led to the belief that Laukik Sanskrit is more suitable for Natural Language Processing.
Neural network encoded variational quantum algorithms

We introduce a general framework called neural network (NN) encoded variational quantum algorithms (VQAs), or NN-VQA for short, to address the challenges of implementing VQAs on noisy intermediate-scale quantum (NISQ) computers. Specifically, NN-VQA feeds input (such as parameters of a Hamiltonian) from a given problem to a neural network and uses its outputs to parameterize an ansatz circuit for the standard VQA. Combining the strengths of NN and parameterized quantum circuits, NN-VQA can dramatically accelerate the training process of VQAs and handle a broad family of related problems with varying input parameters with the pre-trained NN. To concretely illustrate the merits of NN-VQA, we present results on NN-variational quantum eigensolver (VQE) for solving the ground state of parameterized XXZ spin models. Our results demonstrate that NN-VQE is able to estimate the ground-state energies of parameterized Hamiltonians with high precision without fine-tuning, and significantly reduce the overall training cost to estimate ground-state properties across the phases of XXZ Hamiltonian. We also employ an active-learning strategy to further increase the training efficiency while maintaining prediction accuracy. These encouraging results demonstrate that NN-VQAs offer a new hybrid quantum-classical paradigm to utilize NISQ resources for solving more realistic and challenging computational problems.
Stimulasi Kemampuan Anak Usia Dini dalam Mengenal Huruf melalui Media Kartu (Suatu Kajian Literatur)

The purpose of writing this article is to describe the stimulation of children's ability to recognize letters through card media. In this study the method used is descriptive qualitative method which is a literature study. Researchers use various written sources such as books, articles, journals, theses and documents that are relevant to the research in this study. In the study of this research literature, there was an increase in Children's Ability to Recognize Letters through the media of theme cards in early childhood learning. Learning in this study focuses on card media in improving children's ability to recognize letters. The results of several card media studies can improve children's ability to recognize letters and read in children, through themes that are taught in every school. Playing or games is the right way to develop early childhood language skills, especially in letter recognition. The learning atmosphere can be realized in a letter guessing game using card media, which makes children feel happy and not boring.