Reviewerly/exHarmony huggingface dataset:
Deep extreme learning machine with knowledge augmentation for EEG seizure signal recognition

Introduction Intelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance, contributing to removing the noise in raw EEG signals. But traditional deep networks suffer from time-consuming training and slow convergence. Methods Therefore, a novel deep learning based ELM (denoted as DELM) motivated by stacking generalization principle is proposed in this paper. Deep extreme learning machine (DELM) is a hierarchical network composed of several independent ELM modules. Augmented EEG knowledge is taken as complementary component, which will then be mapped into next module. This learning process is so simple and fast, meanwhile, it can excavate the implicit knowledge in raw data to a greater extent. Additionally, the proposed method is operated in a single-direction manner, so there is no need to perform parameters fine-tuning, which saves the expense of time. Results Extensive experiments are conducted on the public Bonn EEG dataset. The experimental results demonstrate that compared with the commonly-used seizure prediction methods, the proposed DELM wins the best average accuracies in 13 out of the 22 data and the best average F-measure scores in 10 out of the 22 data. And the running time of DELM is more than two times quickly than deep learning methods. Discussion Therefore, DELM is superior to traditional and some state-of-the-art machine learning methods. The proposed architecture demonstrates its feasibility and superiority in epileptic EEG signal recognition. The proposed less computationally intensive deep classifier enables faster seizure onset detection, which is showing great potential on the application of real-time EEG signal classification.
Digital Archival Security Design with AES Algorithm on Mail Archives

Manual filing activities are carried out every day and sometimes it is difficult to find back documents that have been archived so that it requires a Letter archive application.The application makes it easy to collect data, archive, and search for archived letter documents.This research focuses on developing a letter archive application by implementing the AES-256 algorithm in securing letter archive files.Advanced Encryption Standard (AES) is a cryptographic algorithm that can be used to secure data.The AES algorithm is a symmetric blockchipertext that can encrypt and decrypt information.The AES algorithm uses cryptographic keys of 128, 192, and 256 bits to encrypt and decrypt data in blocks of 128 bits.So that the AES-256 algorithm can be implemented on the current system, the help of native libraries from the go programming language is used, namely crypto/cipher and crypto/aes so that the encryption and decryption process can be applied to the letter archive file that will be archived into the application.By doing system development on the letter archive application that is currently running, it can provide a sense of trust and security to users and produce a safer archiving system than before.
DEVELOPMENT OF A NEW METACOGNITIVE SELF-REGULATED MODEL OF COMPETENCY

Since 2017, the University of Aix-Marseille (AMU) has engaged in a pedagogical transformation towards a Competency-Based Approach (CBA).Nevertheless, the evaluation of competencies is not yet operational.The transition to a CBA implies a pedagogical paradigm shift: what is evaluated here is not only students' performance (the result of their activity) but also their ability to achieve this result (the mental processes that lead to performance).In order to make CBA operational, it is essential to go beyond traditional evaluation systems since these systems focus almost exclusively on the productive character of competence (performance).Indeed, some conceptions of CBA are still influenced by an empiricist and behaviourist approach and tend to reduce competence to its observable manifestations, leaving aside its constructive character.The difficulty of creating new tools to assess competencies lies in the lack of socio-constructivist models that give their rights due to the learner's cognitive activity and social acceptability.The present model aims to anchor CBA in the Self-Regulated Learning (SRL) theories and, more specifically, to define competency through the metacognitive approach.Metacognitive analysis, judgments, and controls are necessary for students to plan and regulate their activity according to what is asked of them in a specific situation.To be competent, students must be performative and consciously explain the mental processes that lead them to acceptable performance.Also, for a student to be competent, a third party must validate their knowledge and abilities.Competence is certificative.Competency is a conscious mental ability that translates into socially acceptable behaviour.This model aims to present competence through a fuller scope, including its productive, constructive, and social characteristics.The practical use of this model would allow for the implementation of new and more robust competency assessment tools.
An elementary belief function logic

Non-additive uncertainty theories, typically possibility theory, belief functions and imprecise probabilities share a common feature with modal logic: the duality properties between possibility and necessity measures, belief and plausibility functions as well as between upper and lower probabilities extend the duality between possibility and necessity modalities to the graded environment. It has been shown that the all-or-nothing version of possibility theory can be exactly captured by a minimal epistemic logic (MEL) that uses a very small fragment of the KD modal logic, without resorting to relational semantics. Independently, a belief function logic has been obtained by extending the modal logic S5 to probabilistic graded modalities using Łukasiewicz logic, albeit using relational semantics. This paper shows that a simpler belief function logic can be devised by adding Łukasiewicz logic on top of MEL. It allows for a more natural semantics in terms of Shafer basic probability assignments.
2D GAME DEVELOPMENT USING UNITY ENGINE “MINOTSCAPE”

2D game development involves the creation of games that exist within a two-dimensional space, where characters, objects, and environments are represented using two axes: X and Y. This medium offers a diverse range of gameplay possibilities, from side-scrolling adventures to top-down exploration and puzzle- solving experiences. As we grown by playing and enjoying 2D games from our childhood, we always wondered how these games work .This is why the reason we have planned to develop 2D game in order to learn the process. The realm of video games has witnessed a profound transformation over the decades, marked by the evolution of graphics, gameplay mechanics, and player experiences. This research delves into the intricate world of 2D games, exploring their enduring relevance and innovative contributions to the gaming industry. Through an extensive analysis of historical and contemporary examples, this study examines the artistic, technical, and design aspects that have shaped the trajectory of 2D gaming. The objectives of this research are threefold: to uncover the reasons behind the sustained popularity of 2D games in an era dominated by 3D graphics; to investigate the utilization of advanced technologies and techniques in enhancing the visual appeal and interactivity of 2D games; and to highlight the unique design challenges faced by developers in creating compelling gameplay within the constraints of a two-dimensional space.
The Comparison between Ordinal Logistic Regression and Random Forest Ordinal in Identifying the Factors Causing Diabetes Mellitus

Diabetes is one of the high-risk diseases. The most prominent symptom of this disease is high blood sugar levels. People with diabetes in Indonesia can reach 30 million people. Therefore, this problem needs further research regarding the factors that cause it. Further analysis can be done using ordinal logistic regression and random forest. Both methods were chosen to compare the modelling results in determining the factors causing diabetes conducted in the CDC dataset. The best model obtained in this study is ordinal logistic regression because it generates an accuracy value of 84.52%, which is higher than the ordinal random forest. The four most important variables causing diabetes are body mass index, hypertension, age, and cholesterol.
Unsupervised Feature Selection Based on Self-configuration Approaches using Multidimensional Scaling

Some researchers often collect features so the principal information does not lose. However, many features sometimes cause problems. The truth of analysis results will decrease because of the irrelevant or repetitive features. To overcome it, one of the solutions is feature selection. They are divided into two, namely supervised and unsupervised learning. In supervised, the feature selection can only be carried out on data containing labels. Meanwhile, in unsupervised, there are three approaches correlation, configuration, and variance. This study proposes an unsupervised feature selection by combining correlation and configuration using multidimensional scaling (MDS). The proposed algorithm is MDS-Clustering, which uses hierarchical and non-hierarchical clustering. The result of MDS-clustering is compared with the existing feature selection. There are three schemes in the comparison process, namely, 75\%, 50\%, and 25\% feature selected. The dataset used in this study is the UCI dataset. The validities used are the goodness-of-fit of the proximity matrix (GoFP) and the accuracy of the classification algorithm. The comparison results show that the feature selection proposed is certainly worth recommending as a new approach in the feature selection process. Besides, on certain data, the algorithm can outperform the existing feature selection.
Algoritma Adaboost pada Metode Decision Tree untuk Klasifikasi Kelulusan Mahasiswa

Colleges provide higher education as the benchmark of education quality and evaluate higher education syllabi. Graduation rates and enrollment capacity are essential for graduation assessment and decision-making. Unfortunately, some students majoring in statistics failed to finish their studies on time, impacting the accreditation of the study program. It is necessary to examine the characteristics of students who managed and failed to complete their studies on time using the data mining classification method, namely Algorithm C5.0. In this study, Adaboost algorithm and Algorithm C5.0 was employed to classify graduation rates accurately. Graduation data of the Statistics Study Program of Universitas Tanjungpura Batch 1 of 20217/2018 to Batch II of 2022/2023 School years were regarded in this study. First, the entropy, gain, and gain ratio values were measured. After that, each data was given equal weight, and iteration was performed 100 times. The analysis using Algorithm C5.0 showed School Accreditation as the variable with the highest gain ratio, indicating that School Accreditation has the most decisive influence on graduation rates with an accuracy percentage of 70%. This percentage then increased to 82.14% after the boosting using the Adaboost algorithm. Adaboost Algorithm is regarded as good in improving the accuracy of algorithm C5.0. The results of this study can provide insight for colleges in designing policies to increase on-time graduation based on the factors that influence student graduation.
P1351: REAFFIRMING HEMATOPOIETIC PROGENITORS LINEAGE COMMITMENT BY TRANSCRIPTIONAL ATLAS

Topic: 23. Hematopoiesis, stem cells and microenvironment Background: Deciphering the hematopoiesis mechanism that underlies progenitor cells definition and fate decision are critical to understanding fundamental questions of hematopoietic lineage c ommitment. Classic and traditional surface markers are often speciosity, inefficient and inaccurate for defining hematopoietic progenitor cells. Aims: The transcriptional atlas and the lineage commitment of hematopoietic progenitors (HPCs) in adult peripheral blood remains to be elucidated. Methods: Herein, we reaffirmed the adult hematopoietic progenitors, and recovered the key transcript factors in lineages fate decision, and restructured the lineage commitment model of HPCs by single-cell sequencing. Results: We demonstrated the transcription atlas data of original status HPCs in adult peripheral blood with large number of HPCs. An elaborate and comprehensive transcriptional atlas of all types of hematopoietic progenitors was then constructed. The reaffirmed lineage commitment also revealed hematopoiesis was a continuum procedure controlled by the activation and suppression of transcription factors, with relative identified boundary. Several key factors, including NFE2, LYL1, MYB, and RUNX1 of myeloid and RUNX2, HOXA9, and BCL11A of lymphoid progenitors, were identified as major switches that controlled hematopoietic lineage formation. Our study further reaffirmed and established a comprehensive model of hematopoiesis that can reconcile previous models and explain the conflict among functional studies. Summary/Conclusion: Taken together, our transcriptional atlas of HPCs established a comprehensive continuum and key regulators controlled model of the human adult peripheral blood hematopoiesis. Toward this transcriptional atlas, it provides accurate definition, hierarchy, and biological insights of hematopoiesis and lineage commitment of HPCs. Importantly, this can enable new research development in cell engineering, regenerative medicine, and disease treatment. We are the first to clearly depict the transcriptional atlas and defined each subgroups of adult peripheral blood progenitor. Our results will enable the whole map of the transcriptional signal and pathway among lineage commitment. Moreover, we are also the first to identify the cell fate decision transcriptional factors in each branches among hematopoietic hierarchy formation. The precise control on the hematopoietic progenitor cells priming and differentiation can then be interfere by the key transcriptional factors and receptors signals, which we called Artificial Operation of HPCs.Keywords: Stem cell marker, Differentiation, Peripheral blood progenitor cell, Hematopoietic stem and progenitor cells
DEVELOPMENT OF A REAL-TIME SPEECH RECOGNITION SYSTEM FOR THE AZERBAIJANI LANGUAGE

This paper investigates the development of a real-time automatic speech recognition system dedicated to the Azerbaijani language, focusing on addressing the prevalent gap in speech recognition system for underrepresented languages. Our research integrates a hybrid acoustic modeling approach that combines Hidden Markov Model and Deep Neural Network to interpret the complexities of Azerbaijani acoustic patterns effectively. Recognizing the agglutinative nature of Azerbaijani, the ASR system employs a syllable-based n-gram model for language modeling, ensuring the system accurately captures the syntax and semantics of Azerbaijani speech. To enable real-time capabilities, we incorporate WebSocket technology, which facilitates efficient bidirectional communication between the client and server, necessary for processing streaming speech data instantly. The Kaldi and SRILM toolkits are used for the training of acoustic and language models, respectively, contributing to the system's robust performance and adaptability. We have conducted comprehensive experiments to test the effectiveness of our system, the results of which strongly corroborate the utility of the syllable-based subword modeling approach for Azerbaijani language recognition. Our proposed ASR system shows superior performance in terms of recognition accuracy and rapid response times, outperforming other systems tested on the same language data. The system's success not only proves beneficial for Azerbaijani language recognition but also provides a valuable framework for potential future applications in other agglutinative languages, thereby contributing to the promotion of linguistic diversity in automatic speech recognition technology.
The effect of revaluation and implementation of accounting management information system of state-owned assets on the quality of financial report

The benefit that the state gets from the revaluation of SOA is to get the updated value of assets in the financial report in accordance with their fair value. In addition, quality spending is the creation of cost savings from maintenance expenditure efficiency because the inventory results produce updated SOA condition data. Then, decision-making in the management of SOA could be better and supports the asset function as a revenue center. The SOA revaluation is also useful to support state budget financing resources in the form of providing SOA as an underlying asset of issuance. This study to determine the effect of the revaluation and implementation of the Management Accounting Information System of State-Owned Assets (SOA) on the quality of the financial report. This research was conducted at The Office of State Asset and Auction Service (KPKNL) Bandar Lampung using primary data collected from the distribution of questionnaires and interviews. The population in this study was all KPKNL Bandar Lampung employees, there are 34 people, which the population was taken as research samples. The data analysis was made using Partial Least Square with revaluation and implementation of SIMAK as the independent variables and quality of the financial report as the dependent variable. The data analysis instrument was using Smart-PLS 3.3.9 version. The results of this study show that the revaluation of state-owned asses had no significant effect on the quality of the financial report and the implementation of accounting management information system for state-owned asset (SIMAK-SOA) had a significant effect on the quality of financial report. From this research, it can be obtained that there are evaluations and improvements that need to be made by SOA management and can be a source of knowledge about SOA for the community in order to help supervise SOA management. Furthermore, researchers are expected to be able to add to the types of variables that have been presented in this study and expand the research subject.
The Perception of Totalitarianism and Authoritarianism in Various Universitas Andalas Students

This research studies the readers' responses to George Orwell's Nineteen Eighty-Four regarding their perception of totalitarianism and authoritarianism in various Universitas Andalas students as based on the said novel. This research focuses on how the novel's readers understand totalitarianism and authoritarianism after reading Nineteen Eighty-Four. Furthermore, this research also investigates the readers' answer on why they relate those mentioned concepts to their lives based on the novel Nineteen Eighty-Four. In analyzing the readers' responses, we apply a reader-response theory that focuses solely on the object's extrinsic elements, that is, in this case, the readers. As for the study method, we utilize the quantitative mixed with the qualitative method and library research.
Unsupervised Domain Adaptation with CycleGAN: Adapting Image Style and Content for Improved Cross-Domain Performance

Abstract: In order to improve model performance while transferring information from a source domain to an unlabelled target domain, unsupervised domain adaptation is a crucial problem in deep learning. The CycleGAN design, a well-known generative adversarial network architecture, is used in this study to explore the domain adaptation challenge. Improved cross-domain performance is the goal, and both picture style and content adaptation are being focused on. In this study, we explore the use of CycleGAN to modify images from a source domain to more closely reflect the look and feel of a target domain. With no need for paired data, the architecture's built-in capacity to learn domain mappings makes it easier to transfer knowledge between dissimilar domains. By placing a strong emphasis on content and style adaptation, we hope to get beyond the problems of domain shift and distribution mismatch, which will improve generalization and classification accuracy in the target domain. We extensively experiment on benchmark datasets to show the efficacy of the suggested approach. Comparing cross-domain performance to baseline models and other adaption strategies, quantitative evaluation using known measures demonstrates the significant improvement attained. Additionally, qualitative assessments demonstrate the successful alteration of images, demonstrating CycleGAN's competence in adjusting both visual appeal and semantic information. By highlighting the need of simultaneously addressing visual style and content, this study makes a contribution to the field of unsupervised domain adaptation. The outcomes highlight CycleGAN's potential as a formidable domain adaptation tool, opening the door for improved knowledge transfer and performance in real-world settings across several domains
A Learning-based Data Placement Framework for Low Latency in Data Center Networks

Abstract: The challenge of achieving low-latency data services has become increasingly crucial for data center applications. In modern distributed storage systems, efficient data placement plays a vital role in minimizing data movement delays, thereby significantly reducing service latency. While previous solutions for data placement have often relied on pre-assumed data request distributions or traced analysis, the dynamic nature of network conditions and evolving user request patterns poses a complex online decision-making challenge. Traditional static model-based approaches are limited in their ability to cope with such dynamic system dynamics. To address these challenges and consider both data movement and analytical latency, we introduce a novel framework named DataBot+. This framework leverages reinforcement learning techniques to autonomously learn optimal data placement policies. DataBot+ employs neural networks that are trained using a modified version of Q-learning. The input to these networks comprises real-time data flow measurements, while the output is a value function that estimates the imminent future latency. To ensure timely decision-making, DataBot+ is designed with a two-fold asynchronous architecture, consisting of production and training components. This separation guarantees that the training process does not introduce additional overhead to the handling of data flows. Our approach is validated through extensive evaluations using real-world traces, which convincingly demonstrate the efficacy of our proposed design.
Video-rate multi-scale band-limited illumination profilometry with cubic-meter-level measurement volume

We have developed multi-scale band-limited illumination profilometry (MS-BLIP), for robust video-rate three-dimensional (3D) imaging over measurement volumes at the cubic meter (m$^{3}$) level. MS-BLIP applies a newly developed iterative method for distortion compensation, which empowers a dynamic 3D visualization with a measurement volume of up to 1.5 m$^{3}$. It can robustly resolve spatially isolated objects with varied reflectance. It also allows flexible adjustment of the orientation of the field of view with a dove prism. MSBLIP showcases the video-rate 3D surface imaging of full human-body movements.